JP2022164661A - lithography method - Google Patents

Abstract

A method for determining optimized values of one or more operating parameters of a sensor system configured to measure properties of a substrate is disclosed. The method determines measurement parameters of multiple substrates obtained using a sensor system for multiple values of operating parameters, and determines substrate-to-substrate variations in quality parameters and mapping of measurement parameters from substrate to substrate. and determining an optimized value of the one or more operating parameters based on the comparison. [Selection diagram] Figure 6b

Description

The present invention relates to lithographic methods for manufacturing devices. More particularly, the invention relates to a measurement method for alignment of substrates in lithographic methods.

Lithographic methods are used to apply a desired pattern onto a substrate, usually onto a target portion of the substrate. Lithography can be used, for example, in the manufacture of integrated circuits (ICs). In such cases, a patterning device, also called a mask or reticle, can be used to generate the circuit patterns formed on the individual layers of the IC. This pattern can be transferred onto a target portion (eg comprising part of, one, or several dies) on a substrate (eg a silicon wafer). Transfer of the pattern is typically via imaging onto a layer of radiation-sensitive material (resist) provided on the substrate. In general, a single substrate will contain a network of adjacent target portions that are successively patterned. Conventional lithographic apparatus employ a so-called stepper, in which each target portion is irradiated by exposing the entire pattern onto the target portion at once, and each step by scanning the pattern in a particular direction (the "scan" direction) through the radiation beam. a so-called scanner that synchronously scans the substrate parallel or anti-parallel to this direction while the target portion is illuminated. It is also possible to transfer the pattern from the patterning device to the substrate by imprinting the pattern onto the substrate.

A manufactured integrated circuit typically includes multiple layers containing different patterns, each layer being produced using an exposure process such as that described above. Successively exposed layers must be properly aligned with each other to ensure proper operation of the fabricated integrated circuit. To achieve this, the substrate is typically provided with a plurality of so-called alignment marks (also called alignment targets) whereby the positions of the alignment marks are used to locate previously exposed patterns. determine or estimate Therefore, before exposing subsequent layers, the positions of the alignment marks are determined and used to determine the positions of previously exposed patterns. Typically, for example alignment sensors are applied to determine the position of such alignment marks. The alignment sensor is configured to project a beam of radiation onto an alignment mark or target and determine the position of the alignment mark based on the reflected beam of radiation. In the scanner, the alignment markers are read by a scanner alignment system and serve to properly place each field on the substrate when subjected to the patterning steps provided by the scanner. Ideally, the measured positions of the alignment marks correspond to the actual positions of the marks.

However, due to various causes, the measured position of the alignment mark may deviate from the actual position. In particular, deformation of the alignment marks can cause the aforementioned deviations. Such deformations are caused, for example, by substrate processing, such as etching, chemical-mechanical polishing (CMP), or deposition of layers that lead to the determination of sub-optimal marker positions. As a result, the layer may be projected or exposed at locations that are not aligned or aligned with the previously exposed pattern, resulting in so-called overlay errors.

According to one aspect of the invention, the invention provides a method for determining optimized values of one or more operating parameters of a sensor system configured to measure a property of a substrate, comprising: determining a quality parameter for a plurality of substrates; determining a measured parameter for a plurality of substrates obtained using a sensor system for a plurality of values of the operating parameter; The substrate-to-substrate variation is compared, and optimized values of one or more of the operating parameters are determined based on the comparison.

The mappings are weighted summation, non-linear mappings, or trained mappings based on machine learning methods.

The method includes determining an optimal set of weighting factors for weighting the measured parameters associated with the first value of the operating parameter and the measured data associated with the second value of the operating parameter based on the comparison. .

A quality parameter is an overlay or focus parameter.

A measured parameter is the location of a property provided on a plurality of substrates or the out-of-plane deviation of a given location on a substrate.

Operating parameters are parameters associated with the light source of the sensor system. The operating parameter is wavelength, polarization state, spatial coherence state, or temporal coherence state of the light source.

Quality parameters are determined using a metrology system. Quality parameters are determined using simulation models that predict quality parameters based on either contextual information, measured data, reconstructed data, or hybrid measured data.

The optimized values of the operating parameters include a first set of values associated with the first coordinates of the measurement parameters and a second set of values associated with the second coordinates of the measurement parameters.

The method includes determining a third coordinate parallel to the first preferred direction of the mark, determining a fourth coordinate parallel to the second preferred direction of the mark, and determining a third coordinate of the operating parameter associated with the third coordinate. The method can further include determining a set of three optimization values. A fourth set of optimized values for operating parameters associated with the fourth coordinates determines a transformation from the third and fourth coordinates to the first and second coordinates, and uses the determined transformation to transform the determined optimized values of the operating parameters at the third and fourth coordinates to optimized values of the operating parameters at the first and second coordinates.

The first value of the operating parameter may be optimized independently of the second value of the operating parameter.

In some embodiments, determining optimized values for one or more of the operating parameters based on the comparison may be performed for different zones of the substrate. The different zones may include zones proximate the edge of the substrate and zones proximate the center of the substrate. Each zone may include one or more alignment marks applied to the substrate. Each zone can correspond to an individual alignment mark of a plurality of alignment marks applied to the substrate.

In some embodiments, the measurement parameter is the measured position of the mark, the quality parameter is the shift from mark to device, and the optimized value of the operating parameter is a quality parameter such that substrate-to-substrate variation is minimized. determined to optimize the parameters. The operating parameter is a parameter associated with the radiation source, radiation from the source is directed at the substrate, and the optimized value of the operating parameter is a weighting for adjusting measurements obtained utilizing the operating parameter. is determined by applying Radiation from the radiation source directed at the substrate may be collected by the sensor system after targeting the substrate. Weighting may include lens heating effects of lenses used to direct radiation to the substrate and/or to collect radiation by the sensor system. The method uses measurements obtained from a substrate having sub-segmented marks with an intentional mark-to-device shift applied to determine operating parameters for measuring sub-segmented marks. Determining weights to determine sensitivity to mark-to-device shifts in operating parameters can further include.

In some embodiments, the method may be used to optimize operating parameters of a metrology system utilized to control substrate processing. The sensor system includes a first sensor system associated with a first measurement system configured to measure a first property of the substrate prior to processing and a second property of the substrate after processing. and a second sensor system associated with the configured second measurement system. The method can include determining a first set of measured parameters of a plurality of substrates obtained using a first sensor system for a plurality of values of operating parameters. determining a second set of measured parameters of the plurality of substrates obtained using the second sensor system for the plurality of values of the operating parameter; and for each of the first and second sets of measured parameters. , comparing the substrate-to-substrate variability of the quality parameter and the substrate-to-substrate variability of the mapping of the measurement parameter. Determining optimized values for one or more of the operating parameters includes simultaneously determining a first set of operating parameters associated with the first measurement system and a second set of operating parameters associated with the second measurement system. Optimizing may be included, where the optimization mitigates variations in the second board-to-board property. The quality parameter may be overlay determined from the measured second property of the substrate after processing.

According to a second aspect, the invention includes a method for determining conditions of a semiconductor manufacturing process. The method determines an optimized value for an operating parameter according to the first aspect of the invention, compares the determined operating parameter to a reference operating parameter, and determines a condition based on the comparison.

According to a third aspect, the invention includes a method of optimizing measurement data from a sensor system configured to measure properties of a substrate. The method includes acquiring overlay data for multiple substrates. The overlay represents the deviation between the measured and expected positions of the alignment markers on the substrate and includes a plurality of measurements of the alignment marker positions made by the sensor system, each of the plurality of measurements of the sensor system. Utilize different operating parameters. The method further operates based on the obtained overlay data such that for each of the different operating parameters weighted adjustments to measurements by the sensor system for all of the different operating parameters are combined to minimize overlay. Determining weights for adjusting the obtained measurements using the parameters.

An operating parameter may be a parameter associated with a source of radiation from the sensor system. The operating parameter can be the wavelength, polarization state, spatial coherence state or temporal coherence state of the light source.

According to another aspect, the invention includes a method of aligning layers within an integrated circuit wafer. The method includes using a sensor system to obtain a plurality of position measurements of alignment markers on the wafer, each of the plurality of measurements utilizing different operating parameters. For each of the plurality of alignment mark position measurements, a position deviation is determined as the difference between the expected alignment mark position and the measured alignment mark position, and the measured alignment mark position is based on the respective alignment mark position measurement. determined by A set of functions is defined as possible sources of position deviation, the set of functions including a substrate deformation function representing deformation of the substrate and at least one mark deformation function representing deformation of one or more alignment marks. A matrix equation PD=M*F is generated whereby the vector PD containing the position deviations is set equal to the weighted combination and represented by the weighting factor matrix M of the vector F containing the substrate deformation function, whereby at least 1 The weighting factors associated with one mark deformation function vary depending on the applied alignment measure. The values of the weighting factors of the matrix M are determined based on overlays obtained for multiple substrates, where the overlays represent deviations between the measured and expected positions of the alignment markers and the sensor system using different operating parameters. Weight adjusting measurements are obtained using different operating parameters such that the weight adjustments to the measurements are combined to minimize overlay. . An inverse or pseudo-inverse of the matrix M is determined, thereby obtaining the value of the substrate deformation function as a weighted combination of the positional deviations. The values of the substrate deformation function are applied to perform alignment of the target portion with the patterned beam of radiation.

Exemplary embodiments are described herein with reference to the accompanying drawings.

1 depicts a lithographic apparatus according to an embodiment of the invention; FIG. Figure 3 shows some possible alignment measurement results when applying different measurement parameters. It shows how different operating parameters of the sensor are affected when making measurements on the substrate. Fig. 4 is a graph showing how different operating parameters are affected by mark deformation; Figures 4A and 4B show markers with different types of mark deformations; FIG. 4 is a flow diagram that schematically illustrates a wafer alignment, exposure and overlay measurement process; FIG. 4 is a flow diagram that schematically illustrates another wafer alignment, exposure and overlay measurement process; Figures 7a-c are graphs showing how the product and mark shifts vary for different colors of radiation. Fig. 10 is a graph showing how the sensitivity to mark-to-device shift can be calibrated; FIG. 4 is a plot showing asymmetry of alignment marks across a wafer; FIG. FIG. 10a is a plot showing an on-product overlay of a wafer map where the active color is near infrared (NIR). FIG. 10b shows an on-product overlay wafer map of the same wafer using two-color weighting. Figure 10c shows the difference between the plots of Figures 10a and 10b. Figures 11a and 11b are two graphs, one for the wafer edge marks and the other for the center marks, showing how the overlay error differs according to different dichroic weightings in two orthogonal directions. or 5 schematically illustrates the process of determining OCWs for alignment corrections using multiple different colors, models and layouts and determining overlay corrections using multiple frequencies, models and layouts; Fig. 4 schematically illustrates the process of determining the optimal combination of both alignment correction and overlay correction; An alignment mark is shown that includes two sets of gratings. 1 is a block diagram illustrating a computer system that can be used in utilizing the embodiments described herein; FIG.

To assist in understanding the principles applied in embodiments of the present invention, a lithographic apparatus and how it is used will first be described with reference to FIG.

Figure 1 schematically depicts a lithographic apparatus according to one embodiment of the invention. The apparatus is configured to support an illumination system (illuminator) IL configured to condition a beam of radiation B (eg UV radiation or other suitable radiation) and a patterning device (eg mask) MA; It comprises a mask support structure (eg mask table) MT connected to a first positioning device PM configured to accurately position a patterning device according to certain parameters. The apparatus is a substrate table configured to hold a substrate (e.g. a resist-coated wafer) W and connected to a second positioning device PW configured to accurately position the substrate according to certain parameters. Also includes (eg wafer table) WT or "substrate support". The apparatus further comprises a projection system (e.g. a refractive projection lens system) configured to project the pattern imparted to the radiation beam B by the patterning device MA onto a target portion C (e.g. comprising one or more dies) of the substrate W. ) PS.

Illumination systems use various types of optical components, such as refractive, reflective, magnetic, electromagnetic, electrostatic or other types of optical components, or any combination thereof, to direct, shape, or control radiation. can contain.

The mask support structure supports, ie bears the weight of, the patterning device. It holds the patterning device in a manner that depends on the orientation of the patterning device, the design of the lithographic apparatus, and other conditions such as whether the patterning device is held in a vacuum environment. The mask support structure can use mechanical, vacuum, electrostatic or other clamping techniques to hold the patterning device. The mask support structure can be, for example, a frame or table, which can be fixed or movable as required. The mask support structure may ensure that the patterning device is at a desired position, for example with respect to the projection system. Any use of the terms "reticle" or "mask" herein may be considered synonymous with the more general term "patterning device."

As used herein, the term "patterning device" should be interpreted broadly to refer to any device that can be used to impart a cross-sectional pattern to a beam of radiation so as to create a pattern on a target portion of a substrate. be. It should be noted that the pattern imparted to the radiation beam may not exactly correspond to the desired pattern of the target portion of the substrate, for example if the pattern contains phase-shifting features or so-called assist features. Generally, the pattern imparted to the beam of radiation corresponds to a particular functional layer of the device to be fabricated in the target portion, such as an integrated circuit.

A patterning device may be transmissive or reflective. Examples of patterning devices include masks, programmable mirror arrays, and programmable LCD panels. Masks are well known in lithography, and include mask types such as binary, alternating phase-shift, and attenuated phase-shift, as well as various hybrid mask types. An example of a programmable mirror array uses a matrix arrangement of small mirrors, each of which can be individually tilted so as to reflect an incoming radiation beam in different directions. The tilted mirrors impart a pattern to the radiation beam reflected by the mirror matrix.

Patterning devices can be transmissive or reflective. Examples of patterning devices include masks, programmable mirror arrays, and programmable LCD panels. Masks are well known in lithography, and include mask types such as binary, alternating phase-shift, and attenuated phase-shift, as well as various hybrid mask types. An example of a programmable mirror array uses a matrix arrangement of small mirrors, each of which can be individually tilted so as to reflect an incoming radiation beam in different directions. The tilted mirrors impart a pattern to the radiation beam reflected by the mirror matrix.

The term "projection system" as used herein refers to any refractive, reflective, catadioptric, magnetic, It should be construed broadly to include any type of projection system including electromagnetic and electrostatic optical systems, or any combination thereof. Any use of the term "projection lens" herein may be considered as synonymous with the more general term "projection system".

As shown here, the device is of the transmissive type (eg using a transmissive mask). Alternatively, the device may be of a reflective type (eg, using a programmable mirror array of the type mentioned above, or using a reflective mask).

The lithographic apparatus may be of a type having two (dual stage) or more substrate tables or "substrate supports" (and/or two or more mask tables or "mask supports"). In such "multi-stage" machines, additional tables or supports are used in parallel, or one or more other tables or supports are used for exposure while preparatory steps are performed on one or more tables or supports. can.

The lithographic apparatus may be of a type in which at least part of the substrate is covered with a liquid having a relatively high refractive index, for example water, filling a space between the projection system and the substrate. An immersion liquid can also be applied to other spaces in the lithographic apparatus, for example between the mask and the projection system. Immersion techniques can be used to increase the numerical aperture of projection systems. As used herein, the term "immersion" does not imply that a structure such as a substrate must be submerged in liquid, but that liquid is between the projection system and the substrate during exposure. Only.

Referring to Figure 1, the illuminator IL receives a radiation beam from a radiation source SO. The source and lithographic apparatus may be separate entities, for example when the source is an excimer laser. In such a case the source is not considered to form part of the lithographic apparatus and the beam of radiation is directed to the source by e.g. It is passed from SO to illuminator IL. In other cases the source may be an integral part of the lithographic apparatus, for example when the source is a mercury lamp. The source SO and illuminator IL, together with the beam delivery system BD if necessary, are sometimes referred to as the radiation system.

The illuminator IL may include an adjuster AD configured to adjust the angular intensity distribution of the radiation beam. Generally, at least the outer and/or inner radial extent (commonly referred to as σ-outer and σ-inner, respectively) of the intensity distribution in the pupil plane of the illuminator can be adjusted. Additionally, the illuminator IL may include various other components such as an integrator IN and a capacitor CO. Illuminators can be used to condition the radiation beam to have the desired uniformity and intensity distribution across its cross-section.

The radiation beam B is incident on the patterning device (eg, mask MA), which is held on the mask support structure (eg, mask table MT), and is patterned by the patterning device. After passing through the mask MA, the beam of radiation B passes through a projection system PS which focuses the beam onto a target portion C of the substrate W. FIG. The substrate table WT is precisely positioned so as to position different target portions C on the path of the radiation beam B with the aid of a second positioning device PW and position sensors IF (e.g. interferometric devices, linear encoders or capacitive sensors). be moved to Similarly, using the first positioning device PM and another position sensor (not explicitly shown in FIG. 1), the mask MA is emitted, e.g. after mechanical retrieval from a mask library or during scanning. It can be positioned accurately with respect to the path of the beam. In general, movement of the mask table MT can be realized using a long-stroke module (coarse positioning) and a short-stroke module (fine positioning), which form part of the first positioning device PM. Similarly, movement of the substrate table WT or 'substrate support' may be realized using a long-stroke module and a short-stroke module, which form part of the second positioner PW. In the case of a stepper (as opposed to a scanner) the mask table MT may be connected only to short stroke actuators or may be fixed. Mask MA and substrate W may be aligned using mask alignment marks M1, M2 and substrate alignment marks P1, P2. Although the substrate alignment marks as illustrated occupy dedicated target portions, they may be located in spaces between target portions (these are known as scribe-lane alignment marks). Similarly, in situations in which more than one die is provided on the mask MA, the mask alignment marks may be located between the dies.

The illustrated device can be used in at least one of the following modes:

In step mode, the mask table MT or "mask support" and the substrate table WT or "substrate support" remain essentially stationary and the entire pattern imparted to the radiation beam is projected onto the target portion C at once (i.e. , a single static exposure). The substrate table WT or 'substrate support' is then shifted in the X and/or Y direction so that a different target portion C can be exposed. In step mode, the maximum size of the exposure field limits the size of the target portion C imaged in a single static exposure.

In scan mode, the mask table MT or "mask support" and the substrate table WT or "substrate support" are scanned synchronously and a pattern imparted to the radiation beam is projected onto a target portion C (i.e. a single dynamic exposure ). The velocity and direction of the substrate table WT or 'substrate support' relative to the mask table MT or 'mask support' may be determined by the (demagnification) magnification and image reversal properties of the projection system PS. In scan mode, the maximum size of the exposure field limits the width of the target portion (non-scan direction) in a single dynamic exposure. On the other hand, the length of the scanning motion determines the height of the target portion (in the scanning direction).

In another mode, the mask table MT or 'mask support' is essentially stationary holding the programmable patterning device and the substrate table WT is projected onto a target portion C with a pattern imparted to the radiation beam. Or the "substrate support" is moved or scanned. In this mode, a pulsed radiation source is typically used and the programmable patterning device is updated as required after each movement of the substrate table WT or "substrate support" or between successive radiation pulses during scanning. . This mode of operation is readily applicable to maskless lithography utilizing programmable patterning devices such as programmable mirror arrays of the type mentioned above.

Combinations and/or variations of the above modes of use, or completely different modes of use may also be used.

Embodiments of the present invention are typically used in a lithographic apparatus as described above further comprising an alignment system AS configured to determine the position of one or more alignment marks present on a substrate. The alignment system is configured to perform multiple different alignment measurements, thereby obtaining multiple measured alignment mark positions for the considered alignment mark. In this regard, performing different alignment measurements for a particular alignment mark means performing alignment measurements using different measurement parameters or characteristics. Such different measurement parameters or properties include, for example, performing alignment measurements using different optical properties. By way of example, an alignment system applied in a lithographic apparatus according to the invention includes an alignment projection system configured to project a plurality of alignment beams having different properties or parameters onto alignment mark locations on a substrate, An alignment position is determined based on the beam.

As described above, after the wafer is aligned and patterned during the exposure step, the wafer is measured to check patterning accuracy. The deviation between the actual (measured) position of the pattern and the desired position of the pattern relative to the position of the pattern in the previous layer on the wafer is commonly referred to as overlay error or simply overlay. Process-related overlay errors are a good indicator of process quality. Overlay can therefore be viewed as a quality parameter of the process. Overlay error is not the only relevant parameter that indicates process quality. A focus error that occurs when exposing a substrate (wafer) is also important. Overlay error is usually associated with positional error in the plane of the substrate and is therefore closely related to alignment system performance. Focus error is related to the position error perpendicular to the plane of the substrate and is closely related to the performance of another measurement system of the lithographic apparatus; the leveling system. Focus error can also be viewed as a quality parameter of the lithography process.

Generally, quality parameters are measured by a metrology system (eg, a scatterometer used to determine overlay error). However, predictions can also be used to derive quality parameters in addition to or instead of using metrology systems. contextual data (e.g., knowledge about which processing equipment was used to process the substrate of interest); measurement data not directly related to quality data (e.g., wafer shape data measured to predict overlay error); Virtual measurement data are reconstructed as representative of directly measured quality parameter data. This concept is often referred to as "hybrid metrology", combining various data sources and optionally combining simulation models to obtain metrology data associated with quality parameters of interest (overlay and/or focus error). is a method of reconstructing Alternatively, simulation models can be used to derive quality parameters based on contextual and/or measured data. For example, a simulation model can be used to mimic the lithographic process based on pre-exposure measurements (leveling data, alignment data) and context data (reticle layout, process information). The simulation model itself may generate a map of quality parameter data (in this case a prediction overlay).

Within the meaning of this disclosure, the alignment system operates with different operating parameters including at least different polarizations or different wavelength (frequency) content of the alignment beam. Accordingly, the alignment system can use different operating parameters (eg, using alignment beams having different colors, ie frequencies/wavelengths) to determine the positions of the alignment marks. Generally, the purpose of such alignment mark measurements performed by an alignment system is to determine or estimate the position of a target portion (such as target portion C shown in FIG. 1) for subsequent exposure processes. Colloquially, the term "color" is used to refer to a beam with a particular measured parameter or set of measured parameters. The different "color" beams are not necessarily beams having different colors within the visible spectrum, but may have different frequencies (wavelengths) or other characteristics such as polarization. To determine the positions of these target portions, the positions of alignment marks, which may be provided, for example, in scribe lanes surrounding the target portions are measured. If the measured alignment mark position deviates from its nominal or expected position, it can be assumed that there is also a misalignment in the portion of the target that is to be subsequently exposed. The measured positions of the alignment marks can be used to determine or estimate the actual position of the target portion to ensure that the next exposure is at the proper position and aligned with the target portion. be.

If the measured alignment mark position deviates from the expected or nominal position, this tends to be due to deformation of the substrate. Such deformations of the substrate are caused, for example, by various processes to which the substrate is exposed.

Once a plurality of measured alignment mark positions are available and the positional deviations, i.e. deviations of the expected alignment mark positions, are determined, these deviations are fitted to a function that, for example, describes the deformation of the substrate. . This is, for example, a two-dimensional function representing deviation (Δx, Δy) as a function of (x, y) position. Such functions can be used to determine or estimate the actual position of the target portion where the pattern needs to be projected.

Alignment position measurements performed by the alignment system can be disturbed by deformations or asymmetries of the alignment marks themselves. In other words, the deformation of the alignment mark results in a misaligned alignment mark position measurement compared to when the alignment mark is not deformed. If no precautions are taken, such misaligned alignment mark position measurements can lead to erroneous alignment mark position determinations. It has further been observed that this type of deviation, ie the deviation position measurement caused by deformation of the alignment marks, depends on the operating parameters utilized. As an example, if the alignment mark positions are measured using alignment beams with different frequencies, this may lead to different results, ie different measured positions of the alignment marks.

Therefore, when the positions of the alignment marks are measured using different operating parameters, e.g. using alignment beams of different frequencies, different results may be obtained, e.g. position can be obtained.

As is clear from the above, the result of the alignment measurement procedure should be an estimate of the actual substrate deformation, i.e. the actual position of the alignment marks, which is the actual position of the target portion of subsequent exposures. can be used to determine

Considering the effects described, especially the effects of alignment mark deformation, the measured alignment mark positions (e.g., commonly referred to as "measurement parameters"), i.e., alignment mark position parameters obtained from different measurements, are , is affected by both the actual (unknown) substrate deformation and the induced (unknown) mark deformation.

Both effects can result in misalignment between the expected alignment mark position and the measured alignment mark position. Therefore, if a position deviation is observed, it can be caused by actual substrate deformation, or by alignment mark deformation, or a combination thereof.

FIG. 2 schematically shows some possible scenarios. Assume that three measurements M1, M2, M3 are performed to determine the position of the alignment mark X. FIG. 2(a) schematically shows the nominal or expected position E of the alignment marks and the measured positions M1, M2, M3. FIG. 2(a) further shows the actual position A of the alignment mark. As can be seen, none of the measurements performed provide an accurate representation of the actual positional deviation (EA).

Therefore, in the scenario shown in FIG. 2(a), the combination of the actual displacement of the alignment mark (the position A of the actual alignment mark differs from the expected position E) and the deformation of the mark results in a misaligned measurement. occur.

Fig. 2(b) illustrates another scenario where the measurements (M1, M2, M3), different differences in the measured parameters (in this case the measured positions) from the expected values of the measured parameters (e.g. position E) are observed. show. The actual position A is assumed to match the expected position E. In this scenario, the measurements show that there is misalignment of the alignment marks, but there really is not, i.e. the positions of the alignment marks are not affected by the deformation of the substrate.

FIG. 2(c) schematically shows a third scenario in which all three measurements M1, M2, M3 are in consistent agreement with the actual position A. FIG. Such a scenario can occur if there is no deformation of the alignment marks to affect the measurements.

As is evident from the various scenarios depicted, it is necessary to be able to distinguish between mark deformation effects and substrate deformation effects in order to arrive at a good estimate of the actual alignment mark position.

The present invention provides a method to achieve such separation of both effects. In one example, the lithographic apparatus may include a processing unit PU (see Figure 1) for performing the operations necessary to separate both effects. Such a processing unit PU may thus comprise a processor, microprocessor, computer or the like.

FIG. 3 illustrates the basic physical principle behind the present invention (often referred to as the concept of "optimal color weighting (OCW)" when the operating parameter of interest is the color of the alignment beam). . Although the above diagram shows that in an ideal situation all colors used in a multi-color measurement would produce the same alignment position indication 30 for markers 32 on a geometrically perfect substrate 34. In practice, for the reasons given above and as shown in the figure below, different colors will result in different position indications 36 relative to the actual (ie, imperfect) substrate 38 .

FIG. 4 shows how the different colors are affected by the deformation of the mark, and the position error of each color shown in graph 40 can be assumed to vary linearly with the degree of deformation (the angle of top tilt of the mark). In that case, it might be possible to determine a single color as providing the best indication of true mark position. However, when there may be multiple different types of mark deformations, as shown in FIG. 5, no single color can provide the best fit for all deformation types. In practice, it turns out that the error caused by mark deformation is on different scales not only for layer thickness variations and the type of mark being measured, but also for different colors (wavelength, polarization, etc.). OCW-based methods aim to determine the optimal combination of all the different colors used to minimize the effect of marker deformation on the determined marker position.

Process variations (PV), including deformation of the marks, cause variations in alignment position and variations in color i within and between wafers (PV). The OCW solution moves away from a single optimal color, but allows registration positions to be defined for all colors (x-bar). A "weight" wi is added to each color (xi) to arrive at a linear combination of xi to define the robust position y (y bar) of the process.

Accordingly, embodiments of the present invention address the problem of alignment mark distortion due to wafer-to-wafer variations in process variation (PV) that result in product-to-product overlay errors. OCW solutions include:
• Define the OCW position as a weighted linear combination of the linear positions x.
• Minimize the process sensitivity of y to process variations by taking the best linear combination that minimizes wafer-to-wafer overlay error.
• Determine the optimal weight for each color/polarization using training with overlay data.
• Preferably, the overlay data is obtained from measurements made on wafers that have undergone similar processing, where both measurement and processing are performed using the same or similar equipment.

・所与のＮ個の測定マーク

・重みを最適化して、補正されていないオーバーレイを最小にする

ここで、補正されていないオーバーレイ＝オーバーレイ－適用されたウェハアライメントであり
・色の重みｗ（ｗバー）は、以下で得られる。

The mathematical principle used to determine the color weights w (wbar) based on the overlay data is as follows.
o OCW position y is the weighted sum of the measurements M of the measured color position x (x bar)

a given N measurement marks

Optimize weights to minimize uncorrected overlay

where Uncorrected Overlay = Overlay - Applied Wafer Alignment Color weights w (wbar) are given by

As discussed above, optimal color weighting (OCW) determines optimal color weighting factors in an alignment recipe that may be to achieve minimal overlay variation of patterns on the wafer. The OCW may be determined at multiple locations on the mark. A position on a mark can be described using a two-dimensional representation, which is a set of coordinates such as 2D coordinates u, v. The set of u, v coordinates may be linear coordinates, ie they are expressed with respect to two axes, the u axis and the v axis, having different directions that are not parallel to each other. The u-axis and v-axis directions are sometimes referred to as the u- and v-coordinate directions, respectively. The u, v coordinates are Cartesian or orthonormal. The u and v axes can be aligned independently of the marks. OCW can be trained on previously acquired registration and overlay data. Color weighting factors can be trained and applied separately in the u and v directions. The color weighting factors can also be trained in combination with u and v, but training them independently improves overlay performance.

上記の方程式では、オーバーレイを最適化するために重み係数ｗ_ｕｃｏｌ及びｗ_ｖｃｏｌが決定され、ＯＣＷ決定位置ｕ_ｏｃｗ及びｖ_ｏｃｗが決定される。例えば、公称マーク位置、ウェハ負荷、及びウェハ変形が重み係数の影響を受けないようにするために、色の重みに１つ以上の追加の制限を課してもよい。これは、すべての色の重みの合計が１に等しくなければならないという要件を追加することによって達成できる。つまり、重みは、独立した方向ｕとｖの両方で１００％になり、独立した方向ｕとｖについて：

Mathematically, one implementation for determining color weights in two independent directions is:

In the above equations, the weighting factors w _ucol and w _vcol are determined to optimize the overlay, and the OCW determination positions u _ocw and v _ocw are determined. For example, one or more additional constraints may be imposed on the color weights so that the nominal mark position, wafer load, and wafer deformation are not affected by the weighting factors. This can be achieved by adding a requirement that the sum of all color weights must equal one. That is, the weight will be 100% in both independent directions u and v, and for independent directions u and v:

上記のマトリックス表記では、各色ｕ_ｃｏｌ、ｖ_ｃｏｌは独自の重みマトリックスＷ_ｃｏｌを持ち、ｕ方向とｖ方向の両方の座標の色の重みが含まれる。上記の計算で説明したＯＣＷの実装では、各重み行列Ｗｃｏｌが対角行列であり、つまり、主対角に配置されていない要素はゼロに等しくなる。上記の行列方程式からわかるように、これは、ｕ_ｏｃｗの計算は、Ｖ_ｃｏｌに依存する項を含まず、同様にｖ_ｏｃｗの計算は、ｕ_ｃｏｉに依存する項が含まれていないことを示す。したがって、色の重みの計算は、ＯＣＷの実装において、ｕに依存しない。 In the implementation above, the color weights in the u and v directions are computed independently, but we can combine the notation of the u and v computational set above into a single notation in matrix form:

In the matrix notation above, each color u _col , v _col has its own weight matrix W _col containing the color weights for coordinates in both the u and v directions. In the OCW implementation described in the calculations above, each weight matrix Wcol is diagonal, ie the elements not located on the main diagonal are equal to zero. As can be seen from the matrix equation above, this indicates that the calculation of u _ocw does not include terms that depend on V _col , and likewise the calculation of v _ocw does not include terms that depend on u _coi . . Therefore, the computation of color weights is independent of u in the OCW implementation.

OCW by segment
Alignment marks may include structures having one or more preferred directions. For example, the mark is a sieve BF mark as shown in FIG. 14, whose orientation includes two grids that are not aligned with the coordinates used for OCW. If the sub-segmentation of the BF marks of the sieve, i.e. their pitch and direction are not aligned with the coordinates u,v, OCW may have different effects at different angles, and the OCW results are different between different wafers (overlay). It leads to less stable performance.

If the alignment mark has a preferred direction, e.g. a preferred direction of the mark structure that is not aligned with the u,v coordinates, it may be desirable to perform OCW using a new alternate set of coordinates to determine the color weights. Yes, and the new coordinate directions match one or more of the mark's preferred directions. For example, for sieve BF marks, the grid directions as shown in FIG. 14 can be used as preferred directions for determining the new coordinates u', v'. Therefore, in some implementations, performing OCW includes determining a new set of coordinates u′, v′, where the u′, v′ directions are the preferred directions of the marks, e.g. Aligned in the pitch direction. The new coordinates u', v' can be chosen independently of the old coordinates u, v. In this implementation, called OCW per segment, the new coordinates are used to perform OCW as described in the normal OCW method above. If one or more representations of the determined OCW positions and color weights are required in the old set of coordinates u,v, then the coordinate transformation coordinates from the new set u',v' to the old set u',v' are the color is performed after the weights of are determined.

ＯＣＷは、ｕ’、ｖ’の色の重みが互いに独立して計算される新しい座標セットを使用して、上記の方法を使用して実行される。

The mathematical principles used to determine color weights based on overlay data using segmented OCW are as follows:
Let φ1 and φ2 be the angles normal to the new directions u′, v′ with respect to positive u. The angles φ1 and φ2 need not be the same and do not form an angle of 180° with each other. That is, the directions u' and v' do not have to be parallel. The angles φ1 and φ2 may be orthogonal or may form different angles with each other.
The relationship between new coordinates and old coordinates can be expressed as:

OCW is performed using the above method using a new set of coordinates in which the u', v' color weights are calculated independently of each other.

これから以下の方程式が導かれる：

このＷｃｏｌをｕ、ｖ座標で表すと、

To represent _u'OCW , _v'OCW in relation to a set of coordinates u,v, a transformation from the new coordinate system to the old coordinate system is performed as follows:

This leads to the following equation:

Representing this Wcol in terms of u, v coordinates,

Using OCW by segments, color weights are determined independently for the two directions of the new coordinates u', v'. The fact that the _OCW positions u′OCW, _v′OCW , represented by the new coordinates u′,v′ are independent of each other means that _u′OCW does not depend on the weight _w′vcol or the position _v′col , v ' _ocw means _independent of weight w' u col or position u' _col . Given that the determined OCW positions are expressed in terms of the old coordinates u, v of u _ocw and v _ocw , _both the optimized positions u _col and v _ocw , as a function of w′ u col and w′ v col, are _expressed as u and v in both directions as u _col and v _col and as weights w′ _ucol and w′ _vcol . If the sum of the weight constraints is satisfied for coordinates u', v', then the constraints are also satisfied for the corresponding color weights denoted by coordinates u', v':

変換された座標角φι＝－４５°及びφ２＝４５°に基づいて新しい座標に対して決定されたこの色の重み行列から、ｕ及びｖで表されるＯＣＷ位置は次のように表される：

An example of this OCW by segment is shown below for a sieve BF mark with preferred directions of angles φ1=−45° and φ2=45°. The old coordinates are represented as u with orientation of 0° and v with orientation of 90°. For this particular example, according to OCW with the above segmentation algorithm, the adjusted color weight matrix denoted by u and v is expressed as follows:

From this color weight matrix determined for the new coordinates based on the transformed coordinate angles φι=−45° and φ2=45°, the OCW positions denoted by u and v are given by :

Extended OCW
In the normal OCW example based on u,v coordinates, the color weights w _ucol and w _vcol for the u,v directions are determined independently of each other. In per-segment OCW, the color weights w′ _ucol and w′ _vcol are determined independently of each other using u′, v′ coordinates, whereas if the OCW position is expressed in old coordinates, u, v , u _ocw and v _ocw are not independent of the weights w′ _ucol , W′ _VCOL and colors u _col and v _col associated in the other directions. Both methods provide two degrees of freedom in determining the optimal color weights by determining the color weights in the two directions separately.

In some implementations of OCW, the number of degrees of freedom used to determine OCW positions is further increased to more than two per color. This can be achieved by adding an additional factor to the color weights for determining OCW position. Specifically, increased degrees of freedom can be determined by adding individual color weight elements at one or more locations of the color weight matrix that are not on the main diagonal. The resulting color weighting matrix is composed of three or more individual color weightings that are independent of each other. Color weightings are independent because the value of one color weighting does not depend on one or more values of other individual color weightings.

Although this approach differs from segment-wise OCW, which may contain non-zero color weighting matrix elements at positions off the main diagonal, each color weighting matrix element has two independent color weights _w'ucol and w ' is interconnected as a function of _vcol only.

拡張ＯＣＷでは、上記の色重み付けマトリックスを使用してｕ_ｏｃｗ及びｖ_ｏｃｗを決定する。４つの個別の色の重み付けｗ_{ｕｕｃｏｌ}，ｗ_{ｕｖｃｏｌ}，ｗ_{ｖｕｃｏｌ}，ｗ_{ｖｖｃｏｌ}は、すべて互いに独立して決定できる。上記のマトリックスは、拡張ＯＣＷでＯＣＷ位置ｕ_ｏｃｗ、ｖ_ｏｃｗを計算するために使用される：

拡張ＯＣＷでは、重み付けの合計の制約も適用できる。つまり、ここでは行列形式で記述されている次の一連の方程式が、色の重み付けによって満たされる必要がある：

非行列形式では、拡張ＯＣＷ方程式は以下のように記述される：

An implementation of OCW with more than two degrees of freedom is Extended OCW, where two independent color weights are added to each color's weight matrix to determine:

Extended OCW uses the above color weighting matrix to determine u _ocw and v _ocw . The four individual color weightings w _uucol , w _uvcol , w _vucol , w _vvcol can all be determined independently of each other. The above matrix is used to calculate the OCW positions u _ocw , v _ocw in Extended OCW:

In extended OCW, weighted sum constraints can also be applied. This means that the following set of equations, written here in matrix form, must be satisfied by the color weights:

In non-matrix form, the extended OCW equation is written as:

If a mark includes one, more, or all features across multiple directions formed as part of the same process layer, the deformations that occur in that process layer may affect some or all of these multiple directions. may affect the characteristics of For example, a mark may have features in the u and v directions or u' and v' directions that are affected by corresponding and/or correlated deformations. In such cases, making the position of the optimized color weight dependent on the color position in both directions may yield more accurate results. Therefore, extended OCW improves optimization and improves overlay.

The described method of linear weighting applied to measured parameters (alignment data) can be generalized to the mapping of measured parameters. As mentioned above, the mapping is typically a linear weighted sum of measured parameters. However, the invention is not limited to linear weighted sums and trained mappings such as those used in machine learning algorithms can also be used.

The described optimal color weighting method is not limited to using color as the target operating parameter, but rather utilizes different polarization modes to generate different measurement parameters, such as those measured by an alignment sensor system. can also be derived (measuring the mark position). Also, the degree of coherence can be viewed as an operating parameter (if the degree of coherence is adjustable, e.g. by adjusting the laser properties, the temporal and/or spatial coherence can be adjusted). . Also, for example, if the operating parameter is color and the sensor system is a level sensor, the measured parameter could be the focus value associated with the substrate being level sensor measured. A relevant quality parameter for level sensor measurements is the focus error that occurred during the exposure of the substrate.

FIG. 6a is a flow diagram that schematically illustrates the wafer alignment, exposure and overlay measurement process. As shown, in step 601 a wafer alignment scan is performed using several different colors (sensor system operating parameters). At step 602, the color recipe is used to determine how different color measurements should be applied to determine wafer marker positions for wafer alignment. At step 604, the wafer (or layer) is aligned by the apparatus using the marker positions determined from the previous step. At step 604, adjustments to the positioning of the wafer are made based on data provided from measurements made on the wafer in previous stages (ie, after the underlying layers of the wafer have been processed). At step 605, the wafer is exposed to a processing stage (as described above with reference to FIG. 1). At step 606 overlay measurements are taken and the overlay data is provided to a training data processor (APC). At step 607, the APC evaluates the overlay data to determine deviations from expected positions and uses this to correct the alignment of subsequent wafers/layers.

FIG. 6b is a flow diagram that schematically illustrates another wafer alignment, exposure and overlay measurement process. The same steps described above with respect to Figure 6a have the same reference numerals in Figure 6b. One difference is that in step 602′, which occurs at the same location as step 602 in FIG. is to determine Another difference is that in step 607' more data is used as training data instead of simply determining the alignment correction determined from the overlay. This data includes registration measurement data 608 for each of the colors obtained in step 601 and overlay data from previous wafer measurements (step 606). Other relevant data such as stack data 611 can also be used for training data. The training data is then used to not only provide wafer positioning alignment corrections in step 604, but also to update the optimal color weightings 609 used in step 602' and the substrate grid model used in step 603. Update 610.

As can be seen from Fig. 6b, the system learns during use, continuously updating the weightings of the OCW measurements and alignment procedures. The main advantage of the above method is therefore that local device-specific variations in the operating parameters of the sensor system used are taken into account and corrected. The more sensor systems and devices used, the better the alignment.

The optimal color weighting (OCW) technique described here combines the alignment information from all wavelengths measured simultaneously so that the measured alignment position is the least sensitive to mark deformation. Computes the optimal preference weights used in a linear combination of colors. However, the nature of the stack in which the markers are etched or over which the marks are covered can change over time. If the changes affect the optical properties of the stack (eg, refractive index), the mark's response to various operating parameters (color, polarization state) may change accordingly. The effect of such changes in stack properties is that the particular optimal set of weights used in the linear combination of operating parameters may no longer be optimal.

Moreover, the deformation of the marks can change over time, for example due to changes in the characteristics of the processing equipment (such as CMP tools and deposition equipment). The mark deformation can vary, for example, from a floor slope, such as the deformation when etched into a substrate, to an uphill deformation and/or a sidewall angle change of the mark. As a result of changes in the mark deformation properties, the previously determined optimal set of weights associated with the linear combination of colors may no longer be optimal (e.g., suboptimal alignment of substrates, overlay quality may be degraded).

This disclosure proposes periodically determining an optimal set of weights that gives the least amount of overlay variation between substrates. one or more within the semiconductor manufacturing process if the substrate-to-substrate variability of the quality parameter calculated based on the determined set of weights deviates significantly from the previously observed wafer-to-wafer variability of the quality parameter; process may have changed. In other words, if a new set of weights determined based on newly observed substrate-to-substrate variations in quality parameters deviates significantly from the previously determined set of weights, one One or more processes may have changed.

In one embodiment, the semiconductor manufacturing process conditions include: a) determining optimized values of operating parameters (e.g., a new set of weights associated with alignment colors); by comparing to a reference operating parameter (eg, a previously determined set of weights associated with the colors of the alignment); and c) determining a condition based on the comparison.

For a set of previously determined weights associated with alignment sensor colors, the reference operating parameter can be represented as a vector. For example, if the optimal weight is +1 for the color red and -1 for the color green, then the reference operating parameters can be represented as the vector <1, -1>. This vector has no component parallel to the orthogonal complement <1,1>. For example, the component vector <1,-1> is associated with the up-tilt deformation of the (etched) alignment mark and the component vector <1,1> is associated with the sidewall angular deformation of the (etched) mark. If the process is changed, the new optimal set of weights will be 1.2 for red and 0.6 for green. The new optimized values of the operating parameters can be represented by the vector 1.2*<1,-1>+0.6*<1,1>. Clearly, the vector <1,1> became more relevant, indicating that the etched alignment mark deformed according to the sidewall angular profile. By monitoring vector representations of optimal operating parameters, semiconductor manufacturing processes can be monitored.

In one embodiment, the optimal set of weights is first determined based on their sensitivity to variations in quality parameters (board-to-board) and variations in operational parameters. Subsequent measured substrates are further characterized by an orthogonal (or orthonormal) set of vectors representing the ratio of the operating parameter present in the substrate to the substrate variation in the measured data. For example, if the alignment data associated with red exhibits wafer dependent variation f(w_i) (a function of wafer “w_i”) and the alignment data associated with green −f(w_i), then the vector representation <1, − 1> is present in the measured data. Variations in the alignment data may change when process changes occur. For example, red indicates wafer-dependent variation 3*g(w_i), green indicates wafer-dependent variation g(w_i), and the vector representation is <3,1>. The vector <3,1> is the projection 1*<1,-1> onto <1,-1> and the projection 2*<1,1> onto <1,1> (<1,1> is < 1, −1> orthogonal complement). Thus, the process change introduced a <1,1> component to the measured data variability that was not previously present. The optimal set of weights can now be optimized to suppress the strongest components (vectors with the highest amplitudes) observed in the measurement data set. It is proposed to periodically project newly measured operating parameters onto an orthogonal basis corresponding to the instant of original calibration of the optimal weight set. If the distribution of amplitudes across vectors changes, the process may have changed.

In one embodiment, semiconductor manufacturing process conditions are monitored by:
a) obtaining optimized values of operating parameters determined by an embodiment of the present invention, wherein the optimized values of operating parameters are represented as a first vector with individual operating parameters as a reference;
b) obtaining variation across operating parameters for substrate-to-substrate variations in measured data;
c) determining new values for the operating parameters associated with the expected substrate-to-substrate variability of the measured data, the new values for the operating parameters being represented as a second vector having the individual operating parameters as a reference;
d) determining conditions for the semiconductor manufacturing process based on the comparison of the first vector and the second vector;

In one embodiment, the following steps follow:
a) obtaining measured data for a plurality of substrates and a plurality of operating parameters;
b) determining a set of vectors representing linear combinations of the operating parameters present in the measured data;
c) Optionally, if a previously determined set of optimal weights is available for the operating parameters, the projection of the set of vectors onto the space defined by the previously determined optimal set of weights. is subtracted from the set of vectors,
d) a singular value decomposition (SVD) is applied to the set of vectors;
e) the singular values obtained in the previous step are analyzed and the vector associated with the (almost) zero singular value is particularly important as it represents a combination of operating parameters that contains no information about the mark deformation,
f) A so-called "zero kernel" is calculated based on the vectors associated with the (nearly) zero singular values, the zero kernel being essentially an operating parameter independent of initial mark deformation and initial stack (optical) properties is a linear vector space representing combinations of

In one embodiment, singular values are ranked and all singular values above a threshold are removed. A zero kernel is determined based on the vector associated with the singular values that are not filtered out.

Changes in process conditions can be detected by projecting newly determined operating parameter data (associated with one or more substrates) onto the determined zero kernel. A method of monitoring and/or determining changes in zero kernel processing conditions is used because the projection of new operating parameter data onto the zero kernel changes if the nature of the mark deformation and/or stack characteristics changes.

In one embodiment, an initial set of vectors representing variations in measured and/or performance data are determined for a plurality of operating parameters. The vector represents a linear combination of operating parameters related to substrate-to-substrate variations in measurement and/or quality parameters. The procedure of determining the set of vectors is repeated for multiple different mark deformations and/or stack properties. The total set of vectors thus represents an optimally selected operating parameter (combination) of a standard set of mark deformations and/or stack properties. New measurement data is acquired periodically for new substrates and multiple operating parameters. Newly acquired measurement data is used to acquire new vector representations associated with new optimal operating parameters. The newly obtained vector representation is projected onto the initial set of vectors and the relative weights associated with the projection onto each vector from the set of vectors are calculated. The relative weights are then ranked, and relative weights below a threshold are considered zero (eg, components below a certain measure of relevance are excluded). In one embodiment, optimal operating parameters are monitored and their vector representation is decomposed into vectors belonging to an initial set of vectors. Subsequently, component ranking and threshold application are performed. Since the relative strength of the non-zero components can be inferred from these components (vectors) how the etched mark is affected (e.g. top slope, sidewall angle change, etc.) It can be regarded as a KPI of the semiconductor manufacturing process. This indicates which process steps have changed. For example, a large change in the relevance of vector <1,-1> indicates that the top slope property of the alignment mark has changed, which is usually related to CMP process step drift.

One application embodying the above principles is to correct for the so-called mark-to-device offset (MTD). This is the effect if the alignment mark has a different shift between the surrounding product features and the nominal value. This effect is caused by the presence of product features (ie feature widths or spacings between features) that have a much smaller pitch than the alignment marks and the exposure light passing through different parts of the projection lens. In the case of lens aberrations caused, for example, by heating of the lens, this results in a pitch dependent shift. These effects are not wafer-to-wafer or lot-to-lot stable and cannot be fully compensated for by APC systems, as they depend on the particular scanner's illumination settings and product feature history.

Proposed solutions to this problem include mark design and computational MTD (c-MTD). Mark design is limited by design rules, detectability, and aberration sensitivity, but cMTD does not consider processing impacts.

Another method uses sub-segmented marks. Here, additional marks with a finer pitch (similar to the product feature pitch) are included on the substrate. These so-called sub-segment marks consist of coarse pitch marks (used for alignment) and fine pitch marks (according to product design rules). The exposure light for illuminating the fine pitch marks passes through the same portion of the projection lens as the product feature exposure light. The pitch-dependent shift, or MTD, caused by lens aberrations leads to the asymmetry of litho-induced marks. This mark asymmetry leads to different alignment positions for different colors of the alignment sensor.

The principle of OCW can be applied to sub-segmented marks to determine the respective weights of different colors (motion parameters) of the sub-segmented marks, but in this case the effect of lens aberrations on each different color can also be taken into account. The training data used to determine the color weights is obtained from product overlay data.

Note that OCW is generally applied to minimize the effects of process-induced mark asymmetry, and is particularly suitable for layers where processing problems are expected (mainly back-end optical lithography - BEOL). Should. However, MTD is primarily a front-end optical lithography (FEOL) problem where extreme illumination settings are used.

FIG. 7 shows the MTD shift effect in three scenarios. In FIG. 7(a), the effect of lens aberration Z on the detected overlay error (OVL) for small device (product) characteristic pitch is shown as ΔD, where ΔD is essentially the lens aberration Z Linearly proportional, ΔD=ml+SdZ, where ml is the constant offset and Sd is the aberration sensitivity of the device. In Fig. 7(b), the larger pitch alignment marker exhibits a shift ΔM in the detected marker position (APD), which is also essentially linear and proportional to Z, with a litho-induced color asymmetry of It does not depend on the lighting emission (color). In this case ΔΜ=m2+SmZ, where m2 is the constant offset and Sm is the aberration sensitivity of the main marker. ΔΜ does not have the same relationship (ie the slope of the graph) as AD because the illumination radiation is passing through another part of the projection lens.

In FIG. 7(c), the effect of sub-segment marks is shown. Here, there is color (wavelength) dependence and litho-induced asymmetry (different measurements for different colors). where ΔΜ=m3+SmZ+K(λ)[Sm−Ss]Z, where Ss is the sensitivity of the segmented mark and K(λ) is the sensitivity of the stack. However, when using the OCW principle, different colors are given different weights, as mentioned above, so that the color-weighted measurements can be determined.

To calibrate the color weights to be MTD independent, the lens heating effect can be included in the calibration set. Calibration data may also be obtained from measurements made using designer segment marks (DSM), which use intentional MTD shifts to calculate the sensitivity of the alignment position to the MTD of each color. An example of calibration is shown in FIG. Another possibility is to use a computational method to calculate different color sensitivities.

The same principle can be applied to metrology marks used to measure overlays. This is because these marks can also be sub-segmented and are subject to similar mark-to-device offsets.

Another issue that can be addressed by the OCW principles described herein relates to variations that can occur across a substrate or wafer. Previously, wafer alignment settings such as mark layout, color and mark type were used across the wafer. However, the asymmetry of the marks is usually different in different regions of the wafer. Using the same color setting for wafer alignment across wafers does not account for differences in mark asymmetry, which can lead to even greater wafer-to-wafer variability. For example, in situations where the wafer edge marks are highly asymmetric, the current practice is to ignore the wafer edge marks if they produce unacceptably large errors.

Thus, embodiments can provide optimization through the use of OCW for wafer alignment applied to the entire wafer surface area by applying different color weightings to different regions or zones of the wafer. Therefore, different color weighting can reduce overlay errors in areas where the mark asymmetry is greater or different than the rest of the wafer. Furthermore, if the correct color weighting is applied for each region/zone (ie edge vs. center), there is more flexibility in optimizing the wafer alignment layout.

FIG. 9 shows an asymmetric plot of alignment marks across a wafer. The plot shows the variation between the four colors of the array of alignment marks on the wafer. The larger the arrow associated with the mark, the more asymmetric the mark. The asymmetry of the marks is clearly greater at the edge of the wafer. A similar effect can be seen in FIG. 10, plot (a) showing an on-product overlay wafer map where the active color is near-infrared (NIR). Plot 10(b) shows an on-product overlay wafer map of the same wafer using two-color weighting. Plot 10(c) shows the difference between plots 10(a) and 10(b) and it is clear that there is a large difference between NIR and TCW. The difference is most significant in the area distributed around the edge of the wafer. This shows that the effect of mark asymmetry is different across the wafer. To investigate this behavior, TCW analysis was performed on the edge of the wafer and the center of the wafer to determine the optimal color weights for both zones on the wafer.

The improvement in wafer alignment performance is shown by applying a two-color weighting (TCW), referencing only two colors. FIG. 11 contains two graphs, one for the wafer edge marks and one for the center marks. Each graph shows how the overlay error varies in two orthogonal directions parallel to the wafer surface (X-overlay and Y-overlay) as a function of different two-color weight combinations. The two colors in this case are green (ie, visible light at about 50 nanometers) and near-infrared (NIR). The two color weights are -1 to -2 for green and 2 to -1 for NIR. The sum of the weights is always one.

FIG. 11 shows that the optimal color weighting (for minimum overlay error) is different for the edge and center of the wafer. For the edge of the wafer, a combination of weighting green with -1 and NIR with 2 gives the best performance, while green at the center of the wafer uses a weighting of -0.4 and green with 1.4 for optimum performance. good performance is obtained. The weighting difference is 20%.

It will be appreciated that using more colors/color weightings can provide greater improvement.

Applying color weighting to different zones of the wafer (and ultimately, per mark) reduces the effect of mark asymmetry not only at the edge of the wafer, but also at the center. There are different color settings (colors, weightings) for each zone of the wafer where this method can be applied. In this way, the user can optimize the wafer alignment strategy for different zones of the wafer and fine-tune the wafer alignment to reduce wafer-to-wafer variability during processing.

In the wafer processing method described above, two sets of overlay corrections are applied to account for variation between overlay wafers. One correction is through registration. Before the wafer is exposed, the alignment marks on the wafer are measured by the scanner's alignment sensor, and a predefined alignment model is used to calculate a correction set with the alignment measurements. Corrections are applied to the wafer during exposure. Other corrections are per wafer overlay process corrections. After exposing the wafer, send it to an overlay metrology tool to measure the overlay marks. The measured overlay is used to calculate the correction set used to set the next exposure. This correction can be done on a wafer-by-wafer basis.

Each of the two correction methods has advantages and disadvantages. Alignment is always wafer-by-wafer and is a real-time correction, but due to measurement time limitations, the number of alignment marks is limited and can be adversely affected by alignment mark asymmetries. Per-wafer overlay correction has more correction capabilities - many overlay marks can be measured per wafer, but the correction is usually not "real-time", eg temporal filters are used in run-to-run control.

Alignment and wafer-to-wafer overlay correction have the same purpose, which is to reduce wafer-to-wafer variation in overlay. The settings for the two methods are done separately: for registration correction, settings are based on registration model, sampling, color optimization; while for overlay correction, settings are based on overlay model, sampling, measurement frequency, etc. Based on optimization. However, the independent setup does not consider the interaction between alignment and overlay. Therefore, the settings may not be optimal.

This point is shown in FIG. The diagram above illustrates the process of determining OCWs for alignment corrections using multiple different colors, models, and layouts. Overlay measurements are used to evaluate the optimal combination of color, model, and layout, and optimal color weightings are determined for the alignment correction process, as described above. The figure below shows the corresponding process of overlay correction using multiple frequencies, models and layouts. Overlay measurements are used to evaluate the optimal combination of frequency, model, and layout, and optimal color weightings are determined for the registration correction process. It should be noted that the two correction procedures have different optimal color weightings.

Embodiments of the present invention use overlay evaluation, as shown in FIG. 13, to provide a single evaluation to determine the optimal combination of both alignment and overlay corrections. Therefore, by simultaneously evaluating the settings based on the same overlay measurements, a single combination of alignment and overlay setting parameters is determined, which are optimal for combining alignment and overlay correction, but not for registration. It may differ from either setting determined for only one or the other of the overlay corrections.

The described method of optimal color weighting (OCW) is a very effective method for minimizing the influence of processing equipment (eg affecting marks) on the control of the lithographic equipment. However, it is not necessary to use the OCW method in all cases. a) process-induced variations in wafer-to-wafer quality parameters (e.g., overlay) are small or uncorrectable, and the process-induced variations are absent in the final result; Robust enough to process and read the marks (or stacks in the case of level sensor readouts) of selected operating parameters may yield similar results. An evaluation of the benefits of OCW may need to be made for each layer on the substrate that is subject to the semiconductor manufacturing process. In embodiments of a set of layers of interest, both i) the wafer-to-wafer variability of the correctables associated with quality parameters and ii) the wafer-to-wafer variability of the measured data variability across operating parameters are determined. Layers for which the correctable wafer-to-wafer variation and/or wafer-to-wafer variation of measured data variation is less than a certain threshold may be excluded from the OCW framework.

In one embodiment, the layer associated with the substrate has a) a first substrate-to-substrate variation in the layer associated quality parameter and b) a second variation between the layer associated measured parameter across a selection of operating parameters. is selected based on the substrate-to-substrate variation of .

In one embodiment, a layer is selected for application of the OCW algorithm if the variation between the first substrate and the variation between the second substrate exceeds a threshold.

In one embodiment, the first substrate-to-substrate variation and the second substrate-to-substrate variation are configured as KPFs for a semiconductor process. These KPFs are monitored over time, for example by plotting them on one plot (the x-axis is the value of the first KPI associated with the variability between the first substrate, the y-axis is the is the value of the second KPI associated with the variability).

If both the first and second KPIs exceed the threshold, it is decided to determine a new OCW recipe by recalculating the optimal operating parameters configured to yield the least substrate-to-substrate variation in quality parameters. can be Due to the combination of quality parameter variation and measured data variability across operating parameters, a) measurements are clearly affected by process changes, and b) performance (as represented by the quality parameter) is poor as a result. there is Therefore, recalculation of the optimal operating parameters will likely improve performance (eg, reduce initial board-to-board variability) and therefore make sense.

Alternatively, both the first and second KPIs can be combined into one KPI. In this case, it may be decided to determine a new OCW recipe when a single KPI exceeds a threshold.

If only the second KPI exceeds the threshold, the mark may be subject to processing changes, but this does not lead to significant performance degradation. It can be concluded that the current OCW settings (recipe with optimal operating parameter settings) are adequate to control the modified process.

If only the first KPI exceeds the threshold, then process-induced mark deformation or stack property changes may not be the cause of the observed change in quality parameter variation. Therefore, it does not make much sense to recalculate the optimal operating parameters.

Further embodiments of the present invention are disclosed in the following numbered list of clauses 1. A method of determining one or more optimized values of operating parameters of a sensor system configured to measure a property of a substrate, comprising:
determine quality parameters for multiple substrates;
determining measured parameters of a plurality of substrates obtained using the sensor system for a plurality of values of the operating parameter;
comparing the board-to-board variability of the quality parameter with the board-to-board variability of the mapping of the measurement parameter;
Based on the comparison, one or more optimized values for the operating parameters are determined.
2. The method of clause 1, wherein the mapping is a weighted summation, a non-linear mapping, or a trained mapping based on machine learning methods.
3. Based on the comparison, determining an optimal set of weighting factors for weighting the measured parameter associated with the first value of the operating parameter and the measured parameter associated with the second value of the operating parameter. , clause 1.
4. A method according to any preceding clause, wherein the quality parameter is an overlay or focus parameter.
5. A method according to any preceding clause, wherein the measured parameter is the position of a property provided on a plurality of substrates or the out-of-plane deviation of the position on said substrates.
6. A method according to any preceding clause, wherein the operating parameter is a parameter related to the light source from the sensor system.
7. 6. The method of clause 5, wherein the operating parameter is the wavelength, polarization state, spatial coherence state or temporal coherence state of the light source.
8. A method according to any preceding clause, wherein the quality parameter is determined using an instrumentation system.
9. 7. The method of any of clauses 1-6, wherein the quality parameter is determined using a simulation model that predicts the quality parameter based on any of contextual information, measured data, reconstructed data, hybrid metrology data.
10. A method for determining a state of a semiconductor manufacturing process, comprising:
determining the optimized values of the operating parameters in accordance with the preceding clause;
A method of comparing the determined operating parameter to a reference operating parameter and determining a condition based on the comparison.
11. A method of optimizing measurement data from a sensor system configured to measure a property of a substrate, comprising:
Obtaining overlay data for multiple substrates, where overlay represents the deviation between measured and expected positions of alignment markers on the substrate, and combining multiple measurements of alignment marker positions made by the sensor system wherein each of the plurality of measurements utilizes a different operating parameter of the sensor system;
Based on the obtained overlay data, the operation is performed such that for each of the various operating parameters weighted adjustments to the measurements made by the sensor system for all of the various operating parameters are combined to minimize overlay. How to determine weights for adjusting measurements taken using parameters.
12. 12. The method of clause 11, wherein the operating parameter is a source-related parameter from the sensor system.
13. 13. The method of clause 12, wherein the operating parameter is the wavelength, polarization state, spatial coherence state or temporal coherence state of the light source.
14. 10. The method of any of clauses 1-9, wherein determining optimized values of one or more of the operating parameters based on the comparison is performed for different zones of the substrate.
15. 15. The method of clause 14, wherein the different zones include zones proximate the edge of the substrate and zones proximate the center of the substrate.
16. 16. The method of Clause 14 or Clause 15, wherein each zone comprises one or more alignment marks applied to the substrate.
17. 16. The method of Clause 14 or Clause 15, wherein each zone corresponds to an individual alignment mark of a plurality of alignment marks applied to the substrate.
18. The measurement parameter is the measured position of the mark, the quality parameter is the shift from mark to device, and the optimized value of the operating parameter optimizes the quality parameter to minimize substrate-to-substrate variability. The method of any of Clauses 1 through 9, as determined by:
19. The operating parameter is a parameter associated with the radiation source, the radiation from the source being directed at the substrate, and the optimized value of the operating parameter adjusting the measurements obtained utilizing the operating parameter. 19. The method of clause 18, wherein the method is determined by applying a weighting for .
20. 20. The method of clause 19, wherein radiation from the radiation source directed at the substrate is collected by the sensor system after targeting the substrate.
21. 20. The method of clause 19, wherein the weighting comprises lens heating effects of lenses used to direct radiation to the substrate and/or collect radiation by the sensor system.
22. Using measurements obtained from a substrate having sub-segmented marks with intentional mark-to-device shifts applied to determine the sensitivity of operating parameters to mark-to-device shifts. 22. The method of any of clauses 18-21, further comprising determining weights of operating parameters for measuring the segmented marks.
23. 10. The method of any of Clauses 1-9 for optimizing operating parameters of a metrology system utilized to control substrate processing, comprising:
The sensor system includes a first sensor system associated with a first measurement system configured to measure a first property of the substrate prior to processing;
The method includes a second sensor system associated with a second measurement system configured to measure a second property of the substrate after processing;
The method determines a first set of measured parameters of a plurality of substrates obtained using a first sensor system for a plurality of values of operating parameters;
determining a second set of measured parameters of the plurality of substrates obtained using the second sensor system for the plurality of values of the operating parameter;
comparing the substrate-to-substrate variability of the quality parameter with the substrate-to-substrate variability of the mapping of the measured parameters for each of the first and second sets of measured parameters;
Determining optimized values for one or more of the operating parameters includes simultaneously determining a first set of operating parameters associated with the first measurement system and a second set of operating parameters associated with the second measurement system. A method, comprising optimizing, wherein the optimization mitigates a second characteristic of substrate-to-substrate variation.
24. 24. The method of clause 23, wherein the quality parameter is overlay determined from the measured second property of the substrate after processing.
25. 2. The method of clause 1, wherein the quality parameter and the measurement parameter relate to specific layers associated with a plurality of substrates.
26. A particular layer is used to evaluate i) first substrate-to-substrate variability in a quality parameter associated with the particular layer and ii) variability associated with a particular phase between second substrates in the variation between measured parameters. 26. The method of clause 25, wherein the method is selected based on
27. 27. The method of clause 26, wherein the particular layer is selected if the variation between the first substrate and the variation between the second substrate exceeds a threshold.
28. A method of monitoring a condition of a semiconductor manufacturing process, comprising:
a) using the method of any of paragraphs 1 through 27 to obtain optimized values of the operating parameters;
b) obtaining additional measured parameters of the substrate obtained using the sensor system for a plurality of values of the operating parameter;
c) determining new values for the operating parameters associated with the minimum expected substrate-to-substrate variation in the measured data;
d) determining the conditions of the semiconductor manufacturing process based on the comparison of the optimized values and the new values of the operating parameters;
29. Clause 1, wherein the optimized values of the operating parameters comprise a first set of values associated with the first coordinates of the measurement parameters and a second set of values associated with the second coordinates of the measurement parameters The method described in .
30. A method according to clause 29, further:
determining a third coordinate parallel to the first preferred direction of the mark;
determining a fourth coordinate parallel to the second preferred direction of the mark;
determining a third set of optimized values for the operating parameters associated with the third coordinates and a fourth set of optimized values for the operating parameters associated with the fourth coordinates;
determining a transformation from the third and fourth coordinates to the first and second coordinates;
transforming the determined optimized values of the operating parameters at the third and fourth coordinates to optimized values of the operating parameters at the first and second coordinates using the determined transformations. .
31. 30. The method of clause 29, wherein the first value of the operating parameter is optimized independently of the second value of the operating parameter.

FIG. 15 is a block diagram illustrating a computer system 100 that facilitates implementing the methods and flows disclosed herein. Computer system 100 includes a bus 102 or other communication mechanism for communicating information, and a processor 104 (or multiple processors 104 and 105) coupled with bus 102 for processing information. Computer system 100 also includes main memory 106 , such as random access memory (RAM) or other dynamic storage device, coupled to bus 102 for storing information and instructions to be executed by processor 104 . Main memory 106 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 104 . Computer system 100 further includes a read only memory (ROM) 108 or other static storage device coupled to bus 104 for storing static information and instructions for processor 104 . A storage device 110, such as a magnetic or optical disk, is provided and coupled to bus 102 for storing information and instructions.

Computer system 100 may be coupled via bus 102 to a display 112, such as a cathode ray tube (CRT) or flat panel or touch panel display, for displaying information to a computer user. An input device 114 , including alphanumeric and other keys, is coupled to bus 102 for communicating information and command selections to processor 104 . Another type of user input device is cursor control 116 , such as a mouse, trackball, or cursor direction keys for communicating directional control information and command selections to processor 104 and for controlling cursor movement on display 112 . This input device typically has two degrees of freedom in two axes, one axis (such as x) and a second axis (such as y), and can be positioned in a plane. A touch panel (screen) display can also be used as an input device.

According to one embodiment, portions of the processes may be performed by computer system 100 in response to processor 104 executing one or more sequences of one or more instructions contained in main memory 106 . Such instructions may be read into main memory 106 from another computer-readable medium, such as storage device 110 . Execution of the sequences of instructions contained in main memory 106 causes processor 104 to perform the process steps described herein. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in main memory 106 . In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions. Thus, the description herein is not limited to any specific combination of hardware circuitry and software.

The term "computer-readable medium" as used herein refers to any medium that participates in providing instructions to processor 104 for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device 110 . Volatile media includes dynamic memory, such as main memory 106 . Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 102 . Transmission media can also take the form of acoustic or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer readable media include, for example, floppy disks, floppy disks, hard disks, magnetic tapes, other magnetic media, CD-ROMs, DVDs, other optical media, punch cards, paper tapes, etc. Other physical media with a pattern of holes, RAM, PROM, EPROM, FLASH-EPROM, other memory chips or cartridges, carrier waves as described below, or any other computer readable medium. be.

Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to processor 104 for execution. For example, the instructions may initially reside on a magnetic disk on a remote computer. A remote computer can load the instructions into dynamic memory and use a modem to send the instructions over a telephone line. A modem local to computer system 100 can receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector coupled to bus 102 can receive the data carried in the infrared signal and place the data on bus 102 . Bus 102 carries the data to main memory 106, from which processor 104 retrieves and executes the instructions. The instructions received by main memory 106 may optionally be stored on storage device 110 either before or after execution by processor 104 .

Computer system 100 also preferably includes a communication interface 118 coupled to bus 102 . Communication interface 118 provides bi-directional data communication for coupling to network link 120 which is connected to local network 122 . For example, communication interface 118 may be an Integrated Services Digital Network (ISDN) card or modem for providing a data communication connection to a corresponding type of telephone line. As another example, communication interface 118 may be a local area network (LAN) card that provides a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, communication interface 118 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

Network link 120 typically provides data communication through one or more networks to other data devices. For example, network link 120 may provide connection through local network 122 to data equipment operated by host computer 124 or an Internet Service Provider (ISP) 126 . ISP 126 provides data communication services through a worldwide packet data communication network now commonly referred to as the “Internet” 128 . Local network 122 and Internet 128 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link 120 and the signals on communication interface 118, which carry the digital data to and from computer system 100, are exemplary forms of carrier waves transporting the information.

Computer system 100 can send messages and receive data, including program code, through the network(s), network link 120 and communication interface 118 . In the Internet example, server 130 may transmit the requested code of the application program via Internet 128 , ISP 126 , local network 122 and communication interface 118 . One such downloaded application may, for example, provide lighting optimization for an embodiment. The received code may be executed by processor 104 as it is received, and/or stored in storage device 110, or other non-volatile storage for later execution. In this manner, computer system 100 can obtain application code in the form of a carrier wave.

Embodiments of the disclosure may be implemented in hardware, firmware, software, or any combination thereof. Embodiments of the present disclosure may also be implemented as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. A machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (eg, a computing device). For example, machine-readable media include read-only memory (ROM); random-access memory (RAM); magnetic disk storage media; optical storage media; flash memory devices; For example, carrier waves, infrared signals, digital signals, etc.). Additionally, firmware, software, routines, instructions may be described herein as performing particular actions. However, such description is for convenience only and it should be understood that such actions actually result from a computing device, processor, controller, or other device executing firmware, software, routines, instructions, etc. should understand.

Although the block diagrams show the illustrated components as separate functional blocks, embodiments are not limited to systems in which the functionality described herein is organized as illustrated. The functionality provided by each component may be provided by different configurations of software or hardware modules than those currently depicted; for example, such software or hardware may be mixed, combined, or duplicated. may be separated, divided, distributed (eg, within a data center or geographically), or otherwise organized. The functionality described herein may be provided by one or more processors of one or more computers executing code stored in tangible, non-transitory, machine-readable media. In some cases, a third party content distribution network may host some or all of the information communicated over the network. In that case, to the extent that information (such as content) is provided or said to be provided, the information is provided by sending instructions to obtain that information from the content distribution network.

Unless otherwise specified, it will be apparent from the description that throughout this specification, descriptions utilizing terms such as "processing", "calculating", "calculating", "determining", etc., refer to, for example, a dedicated computer or similar dedicated computer. It is understood to refer to actions or processes of a particular device, such as an electronic processing/computing device.

The reader should understand that this application describes several inventions. Rather than separating the inventions into multiple separate patent applications, Applicants have grouped these inventions into a single document because related subject matter facilitates the economy of the filing process. However, such distinct advantages should not be confused with aspects of the invention. In some cases, while embodiments address all deficiencies described herein, the inventions are useful independently and some embodiments address only a subset of such problems or provide other unmentioned advantages that will be apparent to those skilled in the art upon reviewing this disclosure. Due to cost constraints, some of the inventions disclosed herein may not be currently claimed and may be claimed in later applications, such as continuation applications, or by amending the present claims. Similarly, due to space constraints, neither the abstract section nor the abstract section of this document should be considered to contain a comprehensive listing of all such inventions or all aspects of such inventions.

The description and drawings are not intended to limit the invention to the particular forms disclosed, but rather all modifications, equivalents, and equivalents included within the spirit and scope of the invention as defined by the appended claims. should be understood to be intended to cover alternatives, and alternatives.

Modifications and alternative embodiments of various aspects of the invention will be apparent to those skilled in the art in view of this description. Accordingly, the description and drawings are to be construed as illustrative only and are for the purpose of teaching those skilled in the art the general manner of carrying out the invention. It is to be understood that the forms of the invention shown and described herein are to be construed as exemplary embodiments. Elements and materials may be substituted for those shown and described herein, parts and processes may be reversed, reordered or omitted, certain functions may be utilized independently, Embodiments or functions of embodiments can be combined. After having the benefit of this description of the invention, those skilled in the art will be able to modify the elements described herein without departing from the spirit and scope of the invention as set forth in the following claims. can be added. The headings used herein are for organizational purposes and are not intended to be used to limit the scope of the discussion.

The term "may" as used throughout this application has a permissive (i.e., potential) rather than a mandatory (i.e., must) meaning used in Words such as "include," "including," and "include" mean including but not limited to. As used throughout this application, the singular forms "a," "an," and "the" include plural referents unless the content clearly dictates otherwise. Thus, for example, reference to an "an" element or an "a" element refers to a combination of two or more elements, notwithstanding the use of other terms and phrases for one or more elements, such as "one or more." including. The term "or" is non-exclusive, i.e. includes both "and" and "or" unless otherwise indicated. Terms describing conditional relations, such as "depending on X, Y", "by X, Y", "if X, Y", "if X, Y", etc. The relation contains is a necessary causal condition, an antecedent is a sufficient causal condition, or an antecedent is a contributing causal condition of the consequent; "occurs at" and "occurs at X, Y and Z". Such conditional relations are not limited to results immediately following the taking of the preceding condition, as some results may be delayed, and in a conditional statement the preceding condition is connected to the result, e.g. Conditions are connected to the probabilities of what happens as a result. A statement in which multiple attributes or functions are mapped to multiple objects (e.g., one or more processors performing steps A, B, C, and D) will map to all such objects unless otherwise specified. including all such attributes or functions that are mapped and attributes or functions that are mapped to subsets of attributes or functions (e.g., all processors perform steps AD respectively, processor 1 performs step A, processor 2 performs step B and part of step C, processor 3 performs part of step C and step D). Further, unless otherwise specified, a statement that one value or action is “based on” another condition or value includes cases where the condition or value is the only factor and cases where the condition or value is one of multiple factors. Including both when it is a factor. Unless otherwise stated, a statement that "each" instance of some set has some property only applies if some other identical or similar member of a larger set does not have the property, i.e. Each should not be read unless it necessarily means all.

Where certain U.S. patents, U.S. patent applications, or other materials (such as articles) are incorporated by reference, the text of such U.S. patents, U.S. patent applications, and other materials are the same as those materials and this book. Incorporated by reference only to the extent consistent with the statements and drawings of the specification. In the event of such conflict, such conflicting text in such US patents, US patent applications, and other materials incorporated by reference is specifically not incorporated herein by reference. While specific embodiments of the disclosure have been described above, it will be appreciated that the embodiments may be practiced otherwise than as described.

Claims (14)

A method of optimizing measurement data from a sensor system configured to measure a property of a substrate, comprising:
obtaining overlay data for a plurality of substrates, the overlays representing deviations between measured and expected positions of alignment markers on the substrates, comprising a plurality of measurements of alignment marker positions by the sensor system; each of the plurality of measurements utilizes a different operating parameter of the sensor system, the method further comprising:
for each of the different operating parameters such that weighted adjustments to the measurements by the sensor system for all of the different operating parameters are combined to minimize the overlay based on the obtained overlay data. A method comprising determining weights for adjusting said measurements obtained utilizing operating parameters. 2. The method of claim 1, wherein the operating parameter is a source-related parameter from the sensor system. 3. The method of claim 2, wherein the operating parameter is the wavelength, polarization state, spatial coherence state or temporal coherence state of the radiation source. A method for determining conditions for a semiconductor process, comprising:
Determining the set of weights according to the method of any of claims 1-3;
comparing the determined set of weights to a set of reference weights;
and determining said condition based on said comparison. 5. The method of claim 4, wherein determining the set of weights is performed for different zones of the substrate. 6. The method of claim 5, wherein the different zones include zones proximate the edge of the substrate and zones proximate the center of the substrate. A method of monitoring conditions in a semiconductor manufacturing process, comprising:
a) determining the set of weights according to the method of any of claims 1-3;
b) obtaining a plurality of measurements of alignment marker positions for a further substrate using said sensor system for a plurality of values of said operating parameter;
c) determining a new set of weights associated with the smallest expected substrate-to-substrate variation of said measurements;
d) determining said condition based on a comparison of said set of weights and said new set of weights. A computer program for causing a computer to execute a method of optimizing measurement data from a sensor system configured to measure a property of a substrate, said method comprising:
obtaining overlay data for a plurality of substrates, the overlays representing deviations between measured and expected positions of alignment markers on the substrates, comprising a plurality of measurements of alignment marker positions by the sensor system; each of the plurality of measurements utilizes a different operating parameter of the sensor system, the method further comprising:
for each of the different operating parameters such that weighted adjustments to the measurements by the sensor system for all of the different operating parameters are combined to minimize the overlay based on the obtained overlay data. A computer program comprising determining weights for adjusting said measurements obtained using operating parameters. 9. The computer program product of claim 8, wherein the operating parameters are parameters related to radiation sources from the sensor system. 10. Computer program according to claim 9, wherein the operating parameter is the wavelength, polarization state, spatial coherence state or temporal coherence state of the radiation source. A computer program for causing a computer to execute a method for determining semiconductor process conditions, the method comprising:
Determining the set of weights according to the method of any of claims 1-3;
comparing the determined set of weights to a set of reference weights;
determining said condition based on said comparison. 12. The computer program product of claim 11, wherein determining the set of weights is performed for different zones of a substrate. 13. The computer program product of claim 12, wherein the different zones include zones proximate the edge of the substrate and zones proximate the center of the substrate. A computer program for causing a computer to execute a method of monitoring conditions in a semiconductor manufacturing process, the method comprising:
a) determining the set of weights according to the method of any of claims 1-3;
b) obtaining a plurality of measurements of alignment marker positions for a further substrate using said sensor system for a plurality of values of said operating parameter;
c) determining a new set of weights associated with the smallest expected substrate-to-substrate variation of said measurements;
d) determining said condition based on a comparison of said set of weights and said new set of weights.

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