WO2023174178A1 - Heuristic search-based periodic nanostructure morphology parameter measurement method and apparatus - Google Patents

Abstract

A heuristic search-based periodic nanostructure morphology parameter measurement method and apparatus. The measurement method comprises: acquiring a measurement signal of a periodic nanostructure to undergo measurement (S101); constructing a simulation signal database and a Jacobian matrix database on the basis of a three-dimensional morphology parameter design value of said periodic nanostructure and an optical scattering characteristic modeling method (S102); establishing a heuristic search model, and, on the basis of the heuristic search model, the simulation signal database and the Jacobian matrix database, determining a target simulation signal corresponding to the measurement signal (S103); and determining, on the basis of the simulation signal database, a target three-dimensional morphology parameter discrete value corresponding to the target simulation signal, and using the target three-dimensional morphology parameter discrete value as a three-dimensional morphology parameter of said periodic nanostructure (S104). By means of heuristic search, the measurement efficiency for the morphology parameters of a periodic nanostructure can be greatly improved while the measurement accuracy of the morphology parameters of a periodic nanostructure is ensured.

Description

Method and device for measuring morphological parameters of periodic nanostructures based on heuristic search Technical field

The invention relates to the field of optical precision measurement technology, and in particular to a method and device for measuring morphological parameters of periodic nanostructures based on heuristic search.

Background technique

Optical scatterometer is a model-based three-dimensional morphology measurement technology of periodic nanostructures. Compared with nanometer measurement methods such as scanning electron microscope, transmission electron microscope, atomic force microscope and X-ray stack diffraction imaging, optical scatterometer has high speed, It has the advantages of non-destructiveness, so it is very suitable for use in the field of online measurement of the three-dimensional morphology of periodic nanostructures.

In order to ensure the online measurement speed in the three-dimensional morphology measurement of periodic nanostructures based on optical scattering instruments, the optical scattering instruments rely on a database-based online search method, namely library matching. The basic principle is to discretely select multiple parameter values within the range above and below the nominal value (i.e., design value) of its three-dimensional morphology for a certain periodic nanostructure to be measured, and calculate the corresponding simulation signals, thereby gradually generating a simulation signal database. . After the database is established, the measurement signal obtained by the online measurement of the optical scattering instrument will be compared with each simulation signal in the database in real time until the three-dimensional morphology value of the nanostructure corresponding to the most approximate simulation signal is found, which is the output value.

However, in traditional optical scatterometers based on library matching, the scale of library matching and the accuracy of meshing create a conflict between speed and measurement accuracy: the larger the library size and the finer the meshing, the smaller the three-dimensional shape. The more accurate the appearance measurement results are, but the online search time increases exponentially.

Contents of the invention

In view of this, it is necessary to provide a method and device for measuring the morphology parameters of periodic nanostructures based on heuristic search to solve the problem in the existing technology that the morphology parameters of periodic nanostructures cannot be measured simultaneously with measurement accuracy and speed. technical issues.

In order to solve the above technical problems, the present invention provides a method for measuring the morphology parameters of periodic nanostructures based on heuristic search, including:

Obtain the measurement signal of the periodic nanostructure to be measured;

Based on the three-dimensional morphology parameter design values of the periodic nanostructure to be measured and the optical scattering characteristic modeling method, a simulation signal database and a Jacobian matrix database are constructed respectively;

Establish a heuristic search model, and determine the target simulation signal corresponding to the measurement signal based on the heuristic search model, the simulation signal database, and the Jacobian matrix database;

The target three-dimensional morphology parameter discrete value corresponding to the target simulation signal is determined based on the simulation signal database, and the target three-dimensional morphology parameter discrete value is used as the three-dimensional morphology parameter of the periodic nanostructure to be measured.

In some possible implementations, the simulation signal database and the Jacobian matrix database are respectively constructed based on the three-dimensional morphology parameter design values of the periodic nanostructure to be measured and the optical scattering characteristic modeling method, including:

Determine the upper limit and lower limit of the three-dimensional morphology parameter design value, and discretize the three-dimensional morphology parameter design value based on the upper limit and the lower limit to obtain a plurality of three-dimensional morphology parameter discrete values;

Determine multiple simulation signals and multiple Jacobian matrices that correspond one-to-one to the discrete values of the multiple three-dimensional morphology parameters based on the optical scattering feature modeling method;

Construct the simulation signal database based on the plurality of simulation signals and the plurality of three-dimensional topography parameter discrete values corresponding to the plurality of simulation signals;

The Jacobian matrix database is constructed based on the plurality of Jacobian elements and the plurality of three-dimensional morphology parameter discrete values corresponding to the plurality of Jacobian matrices.

In some possible implementations, the heuristic search model is:

P _i+1 =P _i +ΔP _i

ΔP _i =-[J(P _i ) ^T wJ(P _i )] ^-1 *

J(P _i ) ^T w[R _e -R _i (P _i )]

In the formula, P _i+1 is the discrete value of the three-dimensional morphology parameter searched for the i+1th time; P _i is the discrete value of the three-dimensional morphology parameter searched for the i-th time; ΔP _i is the gradient operator; J(P _i ) is the Jacobian matrix of the discrete values of the three-dimensional morphology parameters searched for the i-th time; w is the coefficient matrix; R _i (P _i ) is the simulation signal corresponding to the discrete values of the three-dimensional morphology parameters searched for the i-th time; R _e is the measurement signal; []-1 is the inverse matrix of matrix []; [] _T is the transpose matrix of matrix []. In some possible implementations, determining the target simulation signal corresponding to the measurement signal based on the heuristic search model, the simulation signal database, and the Jacobian matrix database includes:

Step 1. Determine the current simulation signal to be compared in the simulation signal database;

Step 2: Determine the evaluation function, and determine the evaluation function value between the measurement signal and the current simulation signal to be compared based on the evaluation function;

Step 3. Determine whether the evaluation function value is greater than the preset threshold;

Step 4. If the evaluation function value is less than or equal to the preset threshold, use the current simulation signal to be compared as the target simulation signal;

Step 5: If the evaluation function value is greater than the preset threshold, determine the next simulation signal to be compared based on the heuristic search model, and use the next simulation signal to be compared as the current simulation signal to be compared, Repeat steps 2-5.

In some possible implementations, the current simulation signal to be compared is the first simulation signal in the simulation signal database.

In some possible implementations, after determining the target three-dimensional topography parameter discrete value corresponding to the target simulation signal based on the simulation signal database, the method further includes:

Construct robust statistical correction models;

The discrete values of the target three-dimensional morphology parameters are corrected based on the robust statistical correction model to obtain accurate discrete values of the three-dimensional morphology parameters.

In some possible implementations, the robust statistical correction model is:

P ^* ＝ _Psearch +ΔP ^*

In the formula, P ^* is the discrete value of the precise three-dimensional morphology parameter; P _search is the discrete value of the target three-dimensional morphology parameter; ΔP ^* is the correction value of the three-dimensional morphology parameter; argmin{} is the least squares function; ρ is Robust evaluation function; is the k-th wavelength component value in the measurement signal _Re ; is the k-th wavelength component value of the target simulation signal; is the k-th row in the Jacobian matrix corresponding to the discrete value of the target three-dimensional morphology parameter; P is the design value of the three-dimensional morphology parameter of the periodic nanostructure to be measured.

On the other hand, the present invention also provides a device for measuring morphological parameters of periodic nanostructures based on heuristic search, including: a measurement signal acquisition unit for acquiring measurement signals of the periodic nanostructure to be measured;

A database construction unit, configured to respectively construct a simulation signal database and a Jacobian matrix database based on the three-dimensional morphology parameter design values of the periodic nanostructure to be measured and the optical scattering characteristic modeling method;

A heuristic search unit configured to establish a heuristic search model and determine the target simulation signal corresponding to the measurement signal based on the heuristic search model, the simulation signal database, and the Jacobian matrix database;

A target value determination unit configured to determine a target three-dimensional morphology parameter discrete value corresponding to the target simulation signal based on the simulation signal database, and use the target three-dimensional morphology parameter discrete value as the periodic nanostructure to be tested. Three-dimensional morphology parameters.

On the other hand, the present invention also provides an electronic device, including: one or more processors;

memory; and

One or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the heuristic-based method described in any of the above possible implementations Search methods for measuring morphology parameters of periodic nanostructures.

On the other hand, the present invention also provides a computer storage medium on which a computer program is stored, and the computer program is loaded by the processor to perform the heuristic search described in any of the above possible implementations. Steps in the method for measuring morphology parameters of periodic nanostructures.

The beneficial effects of using the above embodiments are: the method for measuring the morphology parameters of periodic nanostructures based on heuristic search provided by the present invention, by establishing a heuristic search model, and based on the heuristic search model and the constructed simulation signal database and Jacobian matrix The database determines the target simulation signal. Compared with the prior art, which requires traversing the simulation signal database to determine the target simulation signal, the present invention can greatly improve the accuracy of the measurement of periodic nanostructure morphology parameters through heuristic search. Efficiency in measuring morphological parameters of periodic nanostructures.

Description of the drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those skilled in the art, other drawings can also be obtained based on these drawings without exerting creative efforts.

Figure 1 is a schematic flow chart of an embodiment of a method for measuring morphological parameters of periodic nanostructures based on heuristic search provided by the present invention;

Figure 2 is a schematic structural diagram of an embodiment of the periodic nanostructure to be tested provided by the present invention;

Figure 3 is a schematic flow diagram of an embodiment of S102 in Figure 1 of the present invention;

Figure 4 is a schematic diagram of an embodiment of the construction process of the simulation signal database and Jacobian matrix database provided by the present invention;

Figure 5 is a schematic flow diagram of an embodiment of S103 in Figure 1 of the present invention;

Figure 6 is a schematic structural diagram of an embodiment of the target simulation signal search process in the prior art;

Figure 7 is a schematic structural diagram of an embodiment of the target simulation signal search process in the present invention;

Figure 8 is a schematic diagram of the principle of robust statistical correction provided by the present invention;

Figure 9 is a schematic flow chart of an embodiment of robust correction provided by the present invention;

Figure 10 is a schematic structural diagram of an embodiment of a periodic nanostructure morphology parameter measuring device provided by the present invention;

Figure 11 is a schematic structural diagram of an embodiment of the electronic device provided by the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of protection of the present invention.

Reference herein to "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of this phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those skilled in the art understand, both explicitly and implicitly, that the embodiments described herein may be combined with other embodiments.

The present invention provides a method and device for measuring morphological parameters of periodic nanostructures based on heuristic search, which will be described separately below.

Figure 1 is a schematic flow chart of an embodiment of a method for measuring morphology parameters of periodic nanostructures based on heuristic search provided by the present invention. As shown in Figure 1, the method for measuring morphology parameters of periodic nanostructures based on heuristic search includes:

S101. Obtain the measurement signal of the periodic nanostructure to be measured;

S102. Construct a simulation signal database and a Jacobian matrix database respectively based on the three-dimensional morphology parameter design values of the periodic nanostructure to be measured and the optical scattering characteristic modeling method;

S103. Establish a heuristic search model, and determine the target simulation signal corresponding to the measurement signal based on the heuristic search model, simulation signal database, and Jacobian matrix database;

S104. Determine the target three-dimensional morphology parameter discrete value corresponding to the target simulation signal based on the simulation signal database, and use the target three-dimensional morphology parameter discrete value as the three-dimensional morphology parameter of the periodic nanostructure to be measured.

Compared with the existing technology, the method for measuring the morphology parameters of periodic nanostructures based on heuristic search provided by the embodiment of the present invention establishes a heuristic search model, and based on the heuristic search model and the constructed simulation signal database and Jacobian matrix The database determines the target simulation signal, which can greatly improve the efficiency of measuring the morphology parameters of periodic nanostructures while ensuring the accuracy of the measurement of periodic nanostructure morphology parameters.

In some embodiments of the present invention, as shown in Figure 2, a typical periodic nanostructure to be measured can be a diffraction grating with a trapezoidal cross-section. The cross-section of the grating can be fully characterized by four set parameters, namely periodic, trapezoidal Top width W, height H and bottom width D. For the field of industrial measurement, the period is often known. Therefore, the three-dimensional morphological parameters of the periodic nanostructure to be measured mainly include the top width W, height H and bottom width D of the trapezoid. These three parameters can be unified into one to be measured. Measuring three-dimensional topography Parameter P is expressed as: P=[W, H, D].

In some embodiments of the present invention, as shown in Figure 3, step S102 includes:

S301. Determine the upper limit and lower limit of the three-dimensional morphology parameter design value, and discretize the three-dimensional morphology parameter design value based on the upper limit and lower limit to obtain multiple three-dimensional morphology parameter discrete values;

S302. Determine multiple simulation signals and multiple Jacobian matrices that correspond one-to-one to multiple discrete values of three-dimensional morphology parameters based on the optical scattering feature modeling method;

S303. Construct a simulation signal database based on multiple simulation signals and multiple three-dimensional morphology parameter discrete values corresponding to the multiple simulation signals;

S304. Construct a Jacobian matrix database based on multiple Jacobian elements and multiple three-dimensional morphology parameter discrete values corresponding to multiple Jacobian matrices.

In some embodiments of the present invention, as shown in Figure 4, steps S301-S304 are specifically:

For the three-dimensional morphology parameter to be measured P = [D, H, W], a random discrete operation is performed in advance within the value range defined by its lower limit and upper limit, and then m sets of three-dimensional morphology parameter discrete values P _j = [D _j , H _j , W _j ] (where j=1,2,...m). For each set of three-dimensional morphology parameter discrete values P _j , the corresponding simulation signal is calculated using the optical scattering characteristic modeling algorithm. For example, the calculated simulation signal corresponding to P _j can be expressed as R _j = [R _j1 , R _j2 , …,R _jn ] (where j=1,2,…m; where n represents that there are n wavelength points corresponding to the measured signal and the simulated signal). In this way, each three-dimensional topography parameter discrete value P _j and its corresponding simulation signal R _j together form a basic unit in the simulation signal database.

Similarly, for each set of three-dimensional topography parameter discrete values P _j , the Jacobian matrix J _j is calculated correspondingly, where the Jacobian matrix is the discrete value of the three-dimensional topography parameter of the signal at a certain wavelength in the simulated signal R _j partial derivative of a component in P _j ).

Specifically, the simulation modeling algorithm for the optical scattering properties of periodic nanostructures can be any one of the strict coupled wave method, the finite difference time domain method, the finite element method, and the method of moments.

It should be noted that the simulated signal database and Jacobian matrix database are both generated by offline calculation. The simulated signal database and Jacobian matrix database are generated through offline calculation. It only needs to be calculated once to generate the simulated signal database and Jacobian matrix database on multiple occasions. It is used in an online measurement scenario to avoid repeated calculation of the simulation signal database and Jacobian matrix database, further improving the measurement efficiency of three-dimensional topography parameters.

In some embodiments of the invention, the heuristic search model is:

P _i+1 =P _i +ΔP _i

ΔP _i =-[J(P _i ) ^T wJ(P _i )] ^-1 *

J(P _i ) ^T w[R _e -R _i (P _i )]

In the formula, P _i+1 is the discrete value of the three-dimensional morphology parameter searched for the i+1th time; P _i is the discrete value of the three-dimensional morphology parameter searched for the i-th time; ΔP _i is the gradient operator; J(P _i ) is the Jacobian matrix of the discrete values of the three-dimensional morphology parameters searched for the ith time; w is the coefficient matrix; R _i (P _i ) is the discrete value of the three-dimensional morphology parameters searched for the ith time The corresponding simulation signal; Re _is the measurement signal; [] ^-1 is the inverse matrix of matrix []; [] ^T is the transpose matrix of matrix [].

It can be seen from the above formula that by introducing the gradient operator to search the target simulation signal in the simulation signal database, the ergodic search strategy in the existing technology is avoided, the search efficiency is greatly improved, and the accuracy of three-dimensional morphology parameter measurement is improved. efficiency. In some embodiments of the present invention, as shown in Figure 5, step S103 includes:

S501. Determine the current simulation signal to be compared in the simulation signal database;

S502. Determine the evaluation function, and determine the evaluation function value between the measured signal and the current simulation signal to be compared based on the evaluation function;

S503. Determine whether the evaluation function value is greater than the preset threshold;

S504. If the evaluation function value is less than or equal to the preset threshold, use the current simulation signal to be compared as the target simulation signal;

S505. If the evaluation function value is greater than the preset threshold, determine the next simulation signal to be compared based on the heuristic search model, and use the next simulation signal to be compared as the current simulation signal to be compared, and repeat S502-S505.

In a specific embodiment of the present invention, the evaluation function is a least squares evaluation function.

In order to avoid the technical problem that the determination rate of the target simulation signal is low or cannot be determined due to the inability to determine the current simulation signal to be compared, or the complexity of the determination process of the current simulation signal to be compared, in a preferred embodiment of the present invention, the current simulation signal to be compared is The compared simulation signal is the first simulation signal in the simulation signal database.

By setting the current simulation signal to be compared as the first simulation signal in the simulation signal database, the current simulation signal to be compared can be quickly determined, further improving the efficiency of three-dimensional topography parameter measurement.

In order to more intuitively compare the differences between the embodiments of the present invention and the prior art, as shown in Figures 6 and 7, the traditional method of determining the target simulation signal from the simulation signal database is to compare the measured signal with all data in the simulation signal database To compare and find the optimal value, the basic principle is shown in Figure 6: each basic unit in the simulation signal database is represented by a gray circle, and the measured signal is represented by a gray square. First, compare the measured signal with the first simulated signal in the database through an evaluation function. Once it is determined that the evaluation function value between the measured signal and the first simulated signal is less than a given threshold, the simulated signal is directly The corresponding three-dimensional morphology parameters are output as the optimal values, and the remaining search process is terminated. If the evaluation function value is greater than the given threshold, then the second simulation signal and the measured signal are compared in a consistent manner, and so on until the evaluation function value is less than the given threshold. It can be seen that the traditional library matching method needs to traverse the data in the entire simulation signal database, making it difficult to achieve high-speed online measurement.

The embodiment of the present invention provides a method for determining the target simulation signal based on heuristic search, as shown in Figure 7: First, still compare the measured signal with the first simulation signal in the simulation signal database. If the two If the difference is greater than a given threshold, heuristic search begins. As can be seen from Figure 7, the embodiment of the present invention uses a gradient search method instead of a traversal search. Therefore, the search efficiency is greatly improved, thereby greatly improving the efficiency of measuring three-dimensional morphological parameters.

Since in the process of constructing the simulation signal database, the corresponding discrete values of the three-dimensional topography parameters between each two basic units are discontinuous, the discrete values of the target three-dimensional topography parameters are limited by the grid accuracy of the simulation signal database. , which is difficult to reflect the true three-dimensional morphology value of the periodic nanostructure to be measured; as shown in the grid surface in Figure 8, it represents the space where the target three-dimensional morphology parameter discrete value Psearch is located. The real three-dimensional morphology value Ptrue of the periodic nanostructure to be measured is outside the grid surface. Therefore, the target three-dimensional morphology parameter discrete value Psearch obtained by using heuristic search of the simulation signal database is different from the real period to be measured. There is a deviation between the three-dimensional morphology values Ptrue of the nanostructure, and this deviation is partly due to the meshing accuracy of the simulation signal database, and partly due to the inevitable measurement error in the measurement signal. Therefore, in some embodiments of the present invention, as shown in Figure 9, after determining the target three-dimensional topography parameter discrete value corresponding to the target simulation signal based on the simulation signal database in step S104, it also includes:

S901. Construct a robust statistical correction model;

S902. Correct the discrete values of the target three-dimensional morphology parameters based on the robust statistical correction model to obtain accurate three-dimensional morphology parameters. number of discrete values.

Furthermore, as shown in Figure 8, it can be seen that the accurate three-dimensional morphology parameter discrete value P* is closer to the real three-dimensional morphology value Ptrue of the periodic nanostructure to be measured than the target three-dimensional morphology parameter discrete value Psearch. Therefore, correcting the discrete values of the target's three-dimensional topography parameters through a robust statistical correction model can suppress the non-normal errors in the current measurement signal, and reduce the deviation caused by the grid division of the simulation signal to a certain extent, so that the obtained The discrete values of precise three-dimensional morphology parameters are more accurate, thereby improving the accuracy of measuring the three-dimensional morphology parameters of the periodic nanostructure to be tested.

In some embodiments of the invention, the robust statistical correction model is:

P ^* ＝ _Psearch +ΔP ^*

In the formula, P ^* is the discrete value of the precise three-dimensional morphology parameter; P _search is the discrete value of the target three-dimensional morphology parameter; ΔP ^* is the correction value of the three-dimensional morphology parameter; argmin{} is the least squares function; ρ is the robust evaluation function ; is the k-th wavelength component value in the measurement signal _Re ; is the kth wavelength component value of the target simulation signal; is the k-th row in the Jacobian matrix corresponding to the discrete value of the target three-dimensional morphology parameter; P is the design value of the three-dimensional morphology parameter of the periodic nanostructure to be tested.

Specifically, the robust evaluation function is:

ω(x)=ρ′(x)/x

In the formula, ρ′(x) is the first derivative of the robust evaluation function; x is any variable; ω(x) is Andrews Strong oscillation operator; c _A is a preset constant.

In a specific embodiment of the present invention, c _A is 1.339, and the measurement error in the correction process has a 95% probability of statistically satisfying the normal distribution assumption. This can eliminate the measurement bias caused by non-normal distribution as much as possible.

In order to better implement the heuristic search-based periodic nanostructure morphology parameter measurement method in the embodiment of the present invention, based on the heuristic search-based periodic nanostructure morphology parameter measurement method, correspondingly, as shown in Figure 10 , embodiments of the present invention also provide a heuristic search-based periodic nanostructure morphology parameter measurement device 1000, including:

The measurement signal acquisition unit 1001 is used to obtain the measurement signal of the periodic nanostructure to be measured;

The database construction unit 1002 is used to construct a simulation signal database and a Jacobian matrix database respectively based on the three-dimensional morphology parameter design values of the periodic nanostructure to be measured and the optical scattering characteristic modeling method;

The heuristic search unit 1003 is used to establish a heuristic search model and determine the target simulation signal corresponding to the measurement signal based on the heuristic search model, simulation signal database, and Jacobian matrix database;

The target value determination unit 1004 is configured to determine the target three-dimensional morphology parameter discrete value corresponding to the target simulation signal based on the simulation signal database, and use the target three-dimensional morphology parameter discrete value as the three-dimensional morphology parameter of the periodic nanostructure to be measured.

The device 1000 for measuring the morphology parameters of periodic nanostructures based on heuristic search provided in the above embodiment can implement the technical solution described in the embodiment of the method for measuring morphology parameters of periodic nanostructures based on heuristic search. Each of the above modules or units is specifically implemented. The principle of can be found in the corresponding content in the above-mentioned embodiment of the method for measuring morphology parameters of periodic nanostructures based on heuristic search, and will not be described again here.

As shown in Figure 10, the present invention also provides an electronic device 1100. The electronic device 1100 includes a processor 1101, a memory 1102 and a display 1103. FIG. 10 shows only some components of the electronic device 1100, but it should be understood that implementation of all illustrated components is not required, and more or fewer components may be implemented instead.

The memory 1102 may be an internal storage unit of the electronic device 1100 in some embodiments, such as a hard disk or memory of the electronic device 1100 . In other embodiments, the memory 1102 may also be an external storage device of the electronic device 1000, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), or a secure digital (SD) equipped on the electronic device 1100. Card, Flash Card, etc.

Further, the memory 1102 may also include both an internal storage unit of the electronic device 1100 and an external storage device. The memory 1102 is used to store application software and various types of data installed on the electronic device 1100 .

In some embodiments, the processor 1101 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 1102, such as in the present invention. Method for measuring morphology parameters of periodic nanostructures.

In some embodiments, the display 1103 may be an LED display, a liquid crystal display, a touch-controlled liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. The display 1103 is used to display information on the electronic device 1000 and to display a visual user interface. Components 1101-1103 of electronic device 1100 communicate with each other over a system bus.

In an embodiment, when the processor 1101 executes the heuristic search-based periodic nanostructure morphology parameter measurement program in the memory 1102, the following steps can be implemented:

Obtain the measurement signal of the periodic nanostructure to be measured;

Based on the three-dimensional morphology parameter design values of the periodic nanostructure to be tested and the optical scattering characteristic modeling method, the simulation signal database and the Jacobian matrix database were constructed respectively;

Establish a heuristic search model, and determine the target simulation signal corresponding to the measured signal based on the heuristic search model, simulation signal database, and Jacobian matrix database;

The target three-dimensional morphology parameter discrete value corresponding to the target simulation signal is determined based on the simulation signal database, and the target three-dimensional morphology parameter discrete value is used as the three-dimensional morphology parameter of the periodic nanostructure to be measured.

It should be understood that when the processor 1101 executes the heuristic search-based periodic nanostructure morphology parameter measurement program in the memory 1102, in addition to the above functions, it can also implement other functions. For details, please refer to the previous corresponding method embodiments. description of.

Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 1100 mentioned. The electronic device 1100 can be a mobile phone, a tablet computer, a personal digital assistant (personal digital assistant, PDA), a wearable device, or a laptop computer. (laptop) and other portable electronic devices. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices equipped with IOS, Android, Microsoft, or other operating systems. The above-mentioned portable electronic device may also be other portable electronic devices, such as a laptop computer (laptop) with a touch-sensitive surface (such as a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 1100 may not be a portable electronic device, but a desktop computer having a touch-sensitive surface (eg, a touch panel).

Correspondingly, embodiments of the present application also provide a computer-readable storage medium. The computer-readable storage medium is used to store computer-readable programs or instructions. When the programs or instructions are executed by the processor, the above method embodiments can be implemented. Method steps or functions provided.

Those skilled in the art can understand that all or part of the process of implementing the method of the above embodiments can be completed by instructing relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. Among them, the computer-readable storage media is a magnetic disk, an optical disk, a read-only memory or a random access memory, etc.

The above is a detailed introduction to the method and device for measuring the morphology parameters of periodic nanostructures based on heuristic search provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only It is used to help understand the method and its core idea of the present invention; at the same time, for those skilled in the art, there will be changes in the specific implementation and application scope according to the idea of the present invention. In summary, this specification The contents should not be construed as limitations of the invention.

Claims (8)

A method for measuring the morphology parameters of periodic nanostructures based on heuristic search, which is characterized by including: Obtain the measurement signal of the periodic nanostructure to be measured; Based on the three-dimensional morphology parameter design values of the periodic nanostructure to be measured and the optical scattering characteristic modeling method, a simulation signal database and a Jacobian matrix database are constructed respectively; Establish a heuristic search model, and determine the target simulation signal corresponding to the measurement signal based on the heuristic search model, the simulation signal database, and the Jacobian matrix database; Determine the target three-dimensional morphology parameter discrete value corresponding to the target simulation signal based on the simulation signal database, and use the target three-dimensional morphology parameter discrete value as the three-dimensional morphology parameter of the periodic nanostructure to be measured; The heuristic search model is:
P _i+1 =P _i +ΔP _i
ΔP _i =-[J(P _i ) ^T wJ(P _i )] ^-1 *
J(P _i ) ^T w[R _e -R _i (P _i )] In the formula, P _i+1 is the discrete value of the three-dimensional morphology parameter searched for the i+1th time; P _i is the discrete value of the three-dimensional morphology parameter searched for the i-th time; ΔP _i is the gradient operator; J(P _i ) is the Jacobian matrix of the discrete values of the three-dimensional morphology parameters searched for the i-th time; w is the coefficient matrix; R _i (P _i ) is the simulation signal corresponding to the discrete values of the three-dimensional morphology parameters searched for the i-th time; R _e is the measurement signal; [] ^-1 is the inverse matrix of matrix []; [] ^T is the transposed matrix of matrix []; Determining the target simulation signal corresponding to the measurement signal based on the heuristic search model, the simulation signal database, and the Jacobian matrix database includes: Step 1. Determine the current simulation signal to be compared in the simulation signal database; Step 2: Determine the evaluation function, and determine the evaluation function value between the measurement signal and the current simulation signal to be compared based on the evaluation function; Step 3. Determine whether the evaluation function value is greater than the preset threshold; Step 4. If the evaluation function value is less than or equal to the preset threshold, use the current simulation signal to be compared as the target simulation signal; Step 5: If the evaluation function value is greater than the preset threshold, determine the next simulation signal to be compared based on the heuristic search model, and use the next simulation signal to be compared as the current simulation signal to be compared, Repeat steps 2-5. The method for measuring morphology parameters of periodic nanostructures based on heuristic search according to claim 1, characterized in that the three-dimensional morphology parameter design values and optical scattering characteristic modeling method based on the periodic nanostructures to be measured are respectively Construct simulation signal database and Jacobian matrix database, including: Determine the upper limit and lower limit of the three-dimensional morphology parameter design value, and discretize the three-dimensional morphology parameter design value based on the upper limit and the lower limit to obtain a plurality of three-dimensional morphology parameter discrete values; Determine multiple simulation signals and multiple Jacobian matrices that correspond one-to-one to the discrete values of the multiple three-dimensional morphology parameters based on the optical scattering feature modeling method; Construct the simulation signal database based on the plurality of simulation signals and the plurality of three-dimensional topography parameter discrete values corresponding to the plurality of simulation signals; The Jacobian matrix database is constructed based on the plurality of Jacobian elements and the plurality of three-dimensional morphology parameter discrete values corresponding to the plurality of Jacobian matrices. The method for measuring morphological parameters of periodic nanostructures based on heuristic search according to claim 1, wherein the current simulation signal to be compared is the first simulation signal in the simulation signal database. The method for measuring morphology parameters of periodic nanostructures based on heuristic search according to claim 1, characterized in that the target three-dimensional morphology parameter discrete value corresponding to the target simulation signal is determined based on the simulation signal database. After that, it also includes: Construct robust statistical correction models; The discrete values of the target three-dimensional morphology parameters are corrected based on the robust statistical correction model to obtain accurate discrete values of the three-dimensional morphology parameters. The method for measuring morphological parameters of periodic nanostructures based on heuristic search according to claim 4, characterized in that the robust statistical correction model is:
P ^* ＝ _Psearch +ΔP ^*
In the formula, P ^* is the discrete value of the precise three-dimensional morphology parameter; P _search is the discrete value of the target three-dimensional morphology parameter; ΔP ^* is the correction value of the three-dimensional morphology parameter; argmin{} is the least squares function; ρ is Robust evaluation function; is the k-th wavelength component value in the measurement signal _Re ; is the k-th wavelength component value of the target simulation signal; for The k-th row in the Jacobian matrix corresponding to the discrete value of the target three-dimensional morphology parameter; P is the design value of the three-dimensional morphology parameter of the periodic nanostructure to be tested. A device for measuring morphological parameters of periodic nanostructures based on heuristic search, which is characterized by including: A measurement signal acquisition unit, used to obtain the measurement signal of the periodic nanostructure to be measured; A database construction unit, configured to respectively construct a simulation signal database and a Jacobian matrix database based on the three-dimensional morphology parameter design values of the periodic nanostructure to be measured and the optical scattering characteristic modeling method; A heuristic search unit configured to establish a heuristic search model and determine the target simulation signal corresponding to the measurement signal based on the heuristic search model, the simulation signal database, and the Jacobian matrix database; A target value determination unit configured to determine a target three-dimensional morphology parameter discrete value corresponding to the target simulation signal based on the simulation signal database, and use the target three-dimensional morphology parameter discrete value as the periodic nanostructure to be tested. Three-dimensional morphology parameters; The heuristic search model is:
P _i+1 =P _i +ΔP _i
ΔP _i =-[J(P _i ) ^T wJ(P _i )] ^-1 *
J(P _i ) ^T w[R _e -R _i (P _i )] In the formula, P _i+1 is the discrete value of the three-dimensional morphology parameter searched for the i+1th time; P _i is the discrete value of the three-dimensional morphology parameter searched for the i-th time; ΔP _i is the gradient operator; J(P _i ) is the Jacobian matrix of the discrete values of the three-dimensional morphology parameters searched for the i-th time; w is the coefficient matrix; R _i (P _i ) is the simulation signal corresponding to the discrete value of the three-dimensional morphology parameter searched for the i-th time; R _e is the measurement signal; [] ^-1 is the inverse matrix of matrix []; [] ^T is the transpose matrix of matrix []; Determining the target simulation signal corresponding to the measurement signal based on the heuristic search model, the simulation signal database, and the Jacobian matrix database includes: Step 1. Determine the current simulation signal to be compared in the simulation signal database; Step 2: Determine the evaluation function, and determine the evaluation function value between the measurement signal and the current simulation signal to be compared based on the evaluation function; Step 3. Determine whether the evaluation function value is greater than the preset threshold; Step 4. If the evaluation function value is less than or equal to the preset threshold, use the current simulation signal to be compared as the target simulation signal; Step 5: If the evaluation function value is greater than the preset threshold, determine the next simulation signal to be compared based on the heuristic search model, and use the next simulation signal to be compared as the current simulation signal to be compared, Repeat steps 2-5. An electronic device, characterized by including: one or more processors; memory; and One or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the heuristic-based search of any one of claims 1-5 Method for measuring morphology parameters of periodic nanostructures. A computer-readable storage medium with a computer program stored thereon, characterized in that the computer program is loaded by a processor to execute the heuristic search-based periodic nanostructure described in any one of claims 1-5. Steps in the method for measuring morphological parameters.