CN111624850B - Critical pattern screening method for full-chip light source mask optimization - Google Patents
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Abstract
一种用于全芯片光源掩模优化的关键图形筛选方法，以主要频率的轮廓和距离阈值为准则设计了主要频率之间的覆盖规则，以关键图形数量最小为原则设计了关键图形筛选方法，实现了全芯片光源掩模优化关键图形筛选。本发明筛选出的关键图形数量更少，且利用本发明的筛选结果进行全芯片光源掩模优化获得的工艺窗口更大。
A key pattern screening method for full-chip light source mask optimization, the coverage rule between main frequencies is designed based on the outline of the main frequency and the distance threshold, and the key pattern screening method is designed based on the principle of the minimum number of key patterns, The key pattern screening for full-chip light source mask optimization is realized. The number of key patterns screened out by the present invention is smaller, and the process window obtained by using the screening results of the present invention to optimize the mask of the full-chip light source is larger.
Description
Technical Field
The invention relates to a photoetching resolution enhancement technology, in particular to a key graph screening method for full-chip light source mask optimization.
Background
Photolithography is one of the key techniques in integrated circuit fabrication. The lithographic resolution determines the feature size of the integrated circuit. Light Source Mask Optimization (SMO) is one of the key lithographic resolution enhancement techniques that is commonly used in 28nm and smaller technology node integrated circuit fabrication. The SMO improves the photoetching resolution and increases the process window by jointly optimizing the light source and the mask pattern, and compared with a light source optimization technology for independently optimizing the light source or an optical proximity effect correction technology for independently optimizing the mask pattern, the SMO has higher optimization freedom and stronger optimization capability on the resolution and the process window. However, the SMO calculation speed is slow due to high optimization freedom, and the full-chip light source mask optimization can not be directly carried out generally. At present, the method for optimizing a full-chip light source mask is to screen out a small amount of key mask patterns by using a key pattern screening technology. And performing SMO on the key mask pattern to obtain an optimized light source. And then carrying out optical proximity effect correction on the whole mask pattern by taking the optimized light source as an illumination condition, and adding a sub-resolution auxiliary pattern. And completing the optimization of the full-chip light source mask in two steps. And SMO is carried out by using the key mask pattern, and the optimization speed is improved by reducing the number of mask patterns participating in the SMO, so that the full-chip light source mask optimization is realized.
The key pattern screening technique based on mask pattern diffraction spectrum analysis proposed by ASML of the Netherlands (see prior art 1, Hua-Yu Liu, Luoqi Chen, Hong Chen, Zhi-pan Li, Selection of options patterns in a design layout based on diffusion characterization analysis, PatentNo. US8543947B2) is one of the most advanced techniques at present. The effect of the technology is influenced by various key technologies such as a diffraction spectrum feature description method, a coverage rule among diffraction spectrums, a key pattern screening method and the like, and the effect of optimizing the full-chip light source mask by using the key mask patterns screened by the technology is not optimal.
Disclosure of Invention
The invention aims to provide a key graph screening method for full-chip light source mask optimization, which realizes the screening of the key graph for the full-chip light source mask optimization and increases the process window of the full-chip light source mask optimization.
The technical solution of the invention is as follows:
a key graph screening method for full-chip light source mask optimization. The invention comprises the following steps:
calculating each mask pattern PiThe formula is as follows:
Fi＝FFT{Pi},
wherein, FiIndicates the ith mask pattern PiI 1, …, N.
step 2.1, preprocessing the diffraction spectrum of the mask pattern:
a. calculating the intensity of the diffraction spectrum, wherein the intensity of the diffraction spectrum is FiIs expressed as | FiAnd build a list of all intensity values Ilist。
b. And removing diffraction orders and zero orders which cannot enter the projection objective of the photoetching machine in all diffraction orders of the diffraction spectrum.
c. Setting a threshold τ indicating removal below intensity IτThe similarity between the diffraction spectrum of the component (a) and the original diffraction spectrum is shown, and the value range of tau is 80-99%.
d. In the intensity value list I according to the value of τlistIn-traversal search strength IτRemoving intensity lower than I in diffraction spectrumτThe diffraction order of (a).
The diffraction spectrum of the mask pattern after pretreatment was recorded as ζi. All mask patterns are preprocessed.
Step 2.2 step 2.3 is entered when the mask pattern has periodicity. Otherwise, go to step 2.4.
Step 2.3 cycle mask pattern main frequency extraction:
the periodic mask pattern refers to a mask pattern having periodicity. Zeta of diffraction spectrum of periodic mask patterniIs composed of discrete diffraction peaks, each diffraction peak corresponding to a diffraction order.
When the periodic mask pattern contains only a single period, the first diffraction order is extracted as the dominant frequency. Finding the frequency coordinate corresponding to the intensity peak of the main frequency, expressed as
Called the peak frequency, the dominant frequency is recorded and expressed as
Where j denotes the number of the current primary frequency, j ═ 1, …, M.
When the periodic mask pattern includes a plurality of periods, the main frequency extraction step is as follows:
a. the diffraction order with the greatest intensity is found.
b. Extracting the diffraction order with the maximum intensity as the main frequency, and finding the peak frequency of the main frequency
Record the dominant frequency, expressed as
c. At the diffraction spectrum ζiWherein the major frequencies extracted in step b and their corresponding harmonic diffraction orders are removed.
d. Repeating steps a to c until all diffraction orders are removed.
And performing the operation on all the periodic mask patterns until the main frequencies corresponding to all the periodic mask patterns are recorded.
Of the dominant frequencies of all recorded periodic mask patterns, an equal dominant frequency is found. The equal primary frequency is recorded as a primary frequency, and all periodic patterns corresponding to all the same primary frequencies are recorded.
Step 2.4. extraction of main frequency of non-periodic mask pattern:
the aperiodic mask pattern is a mask pattern having no periodicity, and its diffraction spectrum ζiConsisting of consecutive diffraction peaks. Extracting the pretreated diffraction spectrum ZetaiAs the main frequency of the mask pattern. For one of the main frequencies, the peak frequency is found
The profile c of the dominant frequency is recorded. The profile c is defined as the boundary of the projection of the main frequencies of the non-periodic pattern onto the diffraction spectrum plane. The main frequency being derived from the peak frequency thereof
And the profile c, recording the dominant frequency, expressed as
The above operation is performed for all the main frequencies of the current mask pattern, and all the main frequencies of the current mask pattern are recorded. The above operation is performed for all the aperiodic mask patterns until the dominant frequencies of all the aperiodic mask patterns are recorded.
Finding equal dominant frequencies among the dominant frequencies corresponding to all recorded non-periodic mask patterns. The equal primary frequency is recorded as a primary frequency, and all non-periodic patterns corresponding to all the same primary frequencies are recorded.
And clustering the main frequencies according to the coverage rule among the main frequencies.
The coverage rules between the main frequencies for which the present invention is designed are as follows:
two main frequencies are arbitrarily selected from the main frequencies obtained in the step 2 and are marked as SAAnd SB。
Setting a distance thresholddT. Distance threshold dTThe value range is 0.01 NA/lambda-0.2 NA/lambda, wherein NA represents the numerical aperture of the projection objective of the photoetching machine, and lambda represents the exposure wavelength of the photoetching machine.
i. When the main frequency SAAnd SBAll are the main frequencies of the non-periodic pattern: if S isBHas a peak frequency of SAC and dTWithin a jointly determined range, then SBIs just covered by SAAnd covering, otherwise, not covering. Contours c and dTThe commonly determined range is defined as the extension d of the contour cTThe distance (d) is within the range enclosed.
When the main frequency SAAnd SBAre the main frequencies of the periodic pattern: if the primary frequency SAAnd SBIf the peak frequencies are different, S is determinedAPeak frequency and S ofBWhether the higher harmonic frequencies of the peak frequency are the same or not, if so, SACovering SB(ii) a Or SBPeak frequency and S ofAThe higher harmonic frequencies of the peak frequency are the same, then SBCovering SA(ii) a Otherwise, it is not covered.
When the main frequency SAIs the main frequency, S, of a periodic patternBFor the main frequencies of the non-periodic pattern: if S isAHas a peak frequency of SBC and dTWithin a jointly determined range, then SBQuilt SAAnd (6) covering. Otherwise, it is not covered.
The clustering method of the main frequency comprises the following steps:
and 3.1, determining the coverage relation between every two main frequencies according to the coverage rule, and recording all the main frequencies covered by each main frequency and the number of the main frequencies.
And 3.2, marking the states of all the main frequencies as unvisited.
And 3.3, searching the main frequency with the maximum number of the covered main frequencies from the main frequencies with the univisioned states, and if a plurality of main frequencies exist, taking the main frequency with the maximum element absolute value in the peak frequency, and calling the main frequency as the central main frequency. The central main frequency and the main frequency covered by the central main frequency are taken as a group of main frequencies, which are called main frequency grouping, and the state of all the main frequencies in the group is modified into a visited state.
And 3.4, repeating the step 3.3 until the states of all the main frequencies are modified into visited.
And 3.5, searching the repeatedly grouped main frequencies in all the main frequencies, calculating the distance between the peak frequency of the main frequencies and the peak frequency of the central main frequency of the group in which the main frequencies are positioned, dividing the main frequencies into the group with the minimum distance, and deleting the main frequencies in other groups.
Step 3.6 finds the primary frequencies in the primary frequency packet covering all the primary frequencies except the center primary frequency. The dominant frequency is taken as the central dominant frequency. The above operation is performed for all the primary frequency packets.
The key graph screening comprises the following steps:
step 4.1. the states of all central main frequencies are marked as unvisited and divided into a group, called an intermediate group.
And 4.2, according to the main frequency information of each mask pattern extracted in the step 2, finding out the mask patterns with the maximum number of central main frequencies in the unvisited state, wherein the number of the mask patterns is represented as alpha 1 (alpha 1 is more than or equal to 1). The mask patterns with different center main frequencies from those of other mask patterns are found out from the alpha 1 mask patterns, and the number of the mask patterns is expressed as xi 1 (xi 1 ≧ 0).
If xi 1 is greater than 1, then take any one mask pattern from xi 1 mask patterns, and use the mask pattern and the mask patterns except xi 1 mask patterns from alpha 1 mask patterns as key mask patterns.
If ξ 1 is less than or equal to 1, all the α 1 mask patterns are used as key mask patterns.
The number of key mask patterns is uniformly expressed as eta 1 (alpha 1 is not less than eta 1 not less than 1).
The intermediate packets are replicated in η 1 shares, yielding η 1 intermediate packets.
The η 1 key mask patterns are assigned to the η 1 intermediate groupings, i.e., one key mask pattern is added to each intermediate grouping.
Step 4.3. any one of the η 1 intermediate packets is marked as Ω, and the following operations are performed:
the state of the central main frequencies of the key mask patterns assigned to Ω is modified to visited among the central main frequencies of Ω, and the states of the other central main frequencies belonging to one main frequency group with these central main frequencies are modified to visited.
And 4.4, judging whether all the states of the central main frequencies in the omega are all visual, if so, finishing the screening of the key mask pattern, wherein the key mask pattern in the omega is the screening result of the key mask pattern.
Otherwise, the steps 4.3-4.4 are performed one by one on the remaining intermediate packets in the η 1 intermediate packets, and if not, the step 4.5 is entered.
And 4.5, according to the main frequency information of each mask pattern extracted in the step 2, finding out the mask patterns with the most number of central main frequencies in the unvisited state, wherein the number of the mask patterns is represented as alpha 2 (alpha 2 is more than or equal to 1). (ii) a The mask patterns with central main frequency of the unvisited state and the central main frequencies of other mask patterns with central main frequencies of the unvisited state are found out from the alpha 2 mask patterns, and the number of the mask patterns is expressed as xi 2 ([ xi ] 2 ≧ 0). If xi 2>1, one of xi 2 mask patterns is taken out, and the mask pattern and the mask patterns except xi 2 mask patterns in alpha 2 mask patterns are used as key mask patterns. If ξ 2 is less than or equal to 1, all the alpha 2 mask patterns are taken as key mask patterns; the number of key mask patterns is expressed as η 2(α 2 ≧ η 2 ≧ 1).
The intermediate packets are replicated in η 2 shares, yielding η 2 intermediate packets.
The η 2 key mask patterns are assigned to the η 2 intermediate groupings, i.e., one key mask pattern is added to each intermediate grouping.
After the execution of the η 1 intermediate packets is completed one by one, η intermediate packets are obtained in total, and the step 4.6 is entered.
And 4.6, returning the eta 1 to the step 4.3.
Compared with the prior art, the invention has the following advantages:
compared with the prior art 1, the method effectively reduces the number of the screened key patterns, the number of the key patterns is less than that of the key patterns screened in the prior art 1, and the process window obtained by optimizing the full-chip light source mask by using the screened key mask patterns is larger than that obtained by using the screening result of the prior art 1.
Drawings
FIG. 1 exemplary diagram of diffraction Spectrum preprocessing of the present invention
FIG. 2 is a diagram illustrating the extraction of dominant frequencies from a periodic pattern according to the present invention
FIG. 3 is a flow chart of the present invention for extracting dominant frequencies of a periodic pattern
FIG. 4 is a diagram of an example of the extraction of dominant frequencies from a non-periodic pattern according to the present invention
FIG. 5 is a schematic diagram of the present invention for characterizing the dominant frequencies of a non-periodic pattern
FIG. 6 is an exemplary diagram of primary frequency clustering in accordance with the present invention
Detailed Description
The invention will be further illustrated by the following examples and figures, but the scope of the invention should not be limited by these examples
The embodiment of the invention adopts the similar technology in the commercial computing software Tachyon of the ASML company in the Netherlands, namely the prior art 1, as a comparison object. The simulation sets the model of the photoetching machine as NXT 1950i photoetching machine of ASML company in Netherlands, the exposure wavelength lambda is 193nm, the polarization mode is XY polarization, the numerical aperture NA of the projection objective is 1.35, and the mask is a dark field binary mask. 40 mask patterns needing to be subjected to light source mask optimization are designed, and the mask patterns comprise 10 one-dimensional periodic patterns, 8 two-dimensional periodic patterns and 22 non-periodic patterns. The present invention and prior art 1 screen out key patterns from 40 patterns, respectively, and then use two sets of key patterns to perform SMO, respectively, to obtain free illumination. Mask Optimization (MO) was performed for all 40 patterns using the resulting free illumination as the illumination condition, and the sizes of the common process windows were compared.
computing all patterns P using fast Fourier transform FFT1,P2,...P40Respectively, is represented as F1,F2,...F40。
step 2.1, preprocessing the diffraction spectrum of the mask pattern:
a. calculating the intensity of the diffraction spectrum, wherein the intensity of the diffraction spectrum is FiIs expressed as | FiAnd build a list of all intensity values Ilist。
b. And removing diffraction orders and zero orders which cannot enter the projection objective of the photoetching machine in all diffraction orders of the diffraction spectrum.
c. A threshold τ of 95% was set, which indicates a removal below the intensity IτThe similarity between the diffraction spectrum of the component (2) and the original diffraction spectrum.
d. In the intensity value list I according to the value of τlistIn-traversal search strength IτRemoving intensity lower than I in diffraction spectrumτThe diffraction order of (a).
The diffraction spectrum of the mask pattern after pretreatment was recorded as ζi. All mask patterns are preprocessed.
Step 2.2 step 2.3 is entered when the mask pattern has periodicity. Otherwise, go to step 2.4.
Step 2.3. extraction of the main frequency of the periodic mask pattern:
the periodic mask pattern refers to a mask pattern having periodicity. Zeta of diffraction spectrum of periodic mask patterniIs composed of discrete diffraction peaks, each diffraction peak corresponding to a diffraction order.
When the periodic mask pattern contains only a single period, the first diffraction order is extracted as the dominant frequency. Finding the frequency coordinate corresponding to the intensity peak of the main frequency, expressed as
Referred to as the peak frequency. Record the dominant frequency, expressed as
Where j denotes the number of the current primary frequency, j ═ 1, …, M.
When the periodic mask pattern includes a plurality of periods, the embodiment of the present invention takes the periodic mask pattern as shown in fig. 2 as an example to perform the main frequency extraction. The mask pattern includes a plurality of periods. Zeta diffraction spectrum of the pretreated mask patterniAs an input, the main frequency extraction flow is shown in fig. 3, and the steps are as follows:
a. the diffraction order with the greatest intensity, i.e., order 2, is found.
b. Extracting the diffraction order with the maximum intensity as the main frequency, and finding the peak frequency of the main frequency
Record the dominant frequency, expressed as
c. At the diffraction spectrum ζiThe 2 nd order extracted in the step b and the corresponding harmonic diffraction order are removed to be the 4 th order.
d. Repeating steps a to c until all diffraction orders are removed, extracting the main frequencies as order 1 and order 2.
And performing the operation on all the periodic mask patterns until the main frequencies corresponding to all the periodic mask patterns are recorded.
Of the dominant frequencies of all recorded periodic mask patterns, an equal dominant frequency is found. The equal primary frequency is recorded as a primary frequency, and all periodic patterns corresponding to all the equal primary frequencies are recorded.
Step 2.4 main frequency extraction of the non-periodic mask pattern:
the aperiodic mask pattern is a mask pattern having no periodicity, and its diffraction spectrum ζiConsisting of consecutive diffraction peaks. FIG. 4 shows an example of extracting the main frequency of the non-periodic pattern according to the embodiment of the present invention, and extracting the preprocessed diffraction spectrum ζiAs the main frequency of the mask pattern. For one of the main frequencies, recording the profile c formed by the main frequency at the boundary projected on the diffraction spectrum plane, and searching the peak frequency
The main frequency being derived from the peak frequency thereof
And profile c, as shown in FIG. 5, the dominant frequency is recorded, denoted as
The above operation is performed for all the main frequencies of the current mask pattern, and all the main frequencies of the current mask pattern are recorded. The above operation is performed for all the aperiodic mask patterns until the dominant frequencies of all the aperiodic mask patterns are recorded.
Finding equal dominant frequencies among the dominant frequencies corresponding to all recorded non-periodic mask patterns. The equal primary frequency is recorded as a primary frequency, and all non-periodic patterns corresponding to all the equal primary frequencies are recorded.
The primary frequencies are clustered according to the coverage rules between the primary frequencies designed by the present invention.
In the embodiment of the present invention, the main frequencies shown in fig. 6 are taken as an example to perform main frequency clustering. The dominant frequency A, B, C, D, E, F, G, H, I, L is the dominant frequency of the non-periodic pattern, and J and K are the dominant frequencies of the periodic pattern. Wherein the profile of the main frequencies A and E of the non-periodic pattern and dTThe determined range is larger, and the range corresponding to the main frequency of other non-periodic patterns is smaller. The main frequency clustering process of the invention is shown in fig. 6, and the clustering steps are as follows:
step 3.1 determines the coverage relationship between two primary frequencies according to the coverage rule mentioned above, wherein A covers B, C, D and L, E covers D, F, G and H, and K covers J and I.
Step 3.2 marks the status of all primary frequencies as unvisited.
Step 3.3 finds the primary frequencies a and E covering the largest number of primary frequencies (number 4) among all the primary frequencies in the unvisited state. Since the maximum value of the absolute value of the peak frequency element of E is larger than the maximum value of the absolute value of the peak frequency element of a, E is selected as the center main frequency. And divides the center primary frequency E into a primary frequency group with its covered primary frequencies D, F, G and H. And modifies the state of all the dominant frequencies in the group to visited.
Step 3.4 repeats step 3.3, and finds the primary frequency a with the largest number of covered primary frequencies (number 4) among all primary frequencies with unknown status. A is the central dominant frequency. The center primary frequency a and its covered primary frequencies B, C, D and L are divided into a primary frequency group and the status of all primary frequencies in the group is modified to a visited. And repeating the step 3.3, and searching the primary frequency K with the largest number (the number is 2) of the covered primary frequencies in all the primary frequencies with the unknown states. K as the central dominant frequency. The central main frequency K and the main frequencies J and I covered by it are divided into a main frequency group, and the states of all main frequencies in the group are modified by visited. Eventually, the states for all major frequencies have been visited.
Step 3.5 is repeated grouping of all primary frequencies with primary frequency D. Primary frequency D is grouped into groups with center primary frequencies a and E. The distances of the peak frequency of the main frequency D to the peak frequencies of a and E are calculated and compared, the distances of the peak frequencies of the main frequencies D and E being smaller. The primary frequency D is divided into closer-distant packets of the center primary frequency E and deleted from the packets of the center primary frequency a. Finally, the primary frequencies are grouped as: A. b, C and L group, D, E, F, G and H group, I, J and K group.
Step 3.6 finds the primary frequencies in the primary frequency packet covering all the primary frequencies except the center primary frequency. After the above operation is performed on all the primary frequency groups, no primary frequency that can be the center primary frequency is generated.
In the embodiment of the invention, 81 main frequencies are extracted. The main frequencies are clustered to obtain 20 main frequency groups, and the total number of the main frequencies is 42.
In this embodiment, the key pattern screening process is described by taking 9 central main frequencies as an example.
Step 4.1. all states of the 9 central dominant frequencies are marked as unvisited and divided into a group, called an intermediate group.
And 4.2, searching the mask patterns with the maximum number of central main frequencies in the unvisited state according to the main frequency information of each mask pattern extracted in the step 2, wherein the number of the mask patterns is represented as alpha 1, and alpha 1 is 4 in the embodiment. The found 4 mask patterns are denoted as M1, M2, M3, and M4, respectively. Mask patterns with center main frequencies different from those of other mask patterns are found out from the 4 mask patterns, and are M1 and M2, and the number of such mask patterns is expressed as ξ 1, that is, ξ 1 ═ 2. Since ξ 1>1, one of ξ 1 mask patterns, namely, M1, is taken out, and the mask patterns other than ξ 1 mask patterns, namely, M1 and M2, among α 1 mask patterns, namely, M3 and M4 are taken as key mask patterns. The number of key mask patterns is denoted as η 1, η 1 ═ 3.
The intermediate packets are replicated in η 1 shares, yielding η 1 intermediate packets, denoted as Z11, Z12, Z13, respectively.
The η 1 key mask patterns are assigned to the η 1 intermediate groupings, i.e., one key mask pattern is added to each intermediate grouping.
Step 4.3. any one of the η 1 intermediate packets is marked as Ω, and the following operations are performed:
the state of the central main frequencies of the key mask patterns assigned to Ω is modified to visited among the central main frequencies of Ω, and the states of the other central main frequencies belonging to one main frequency group with these central main frequencies are modified to visited.
And 4.4, judging whether all the states of the central main frequencies in the omega are all visual, if so, finishing the screening of the key mask pattern, wherein the key mask pattern in the omega is the screening result of the key mask pattern.
Otherwise, steps 4.3-4.4 are performed on the remaining ones of the η 1 intermediate packets one by one. If not, go to step 4.5.
Step 4.5, find the central main frequency with the largest number of states as unvisited in the intermediate packet Z11, get α 2 ═ 2, ξ 2 ═ 0, η 2 ═ 2, and represent the resulting intermediate packets as Z21 and Z22. By analogy, intermediate grouping Z12 is looked up, resulting in α 2 ═ 3, ξ 2 ═ 1, η 2 ═ 3, and the resulting intermediate groupings are denoted Z23, Z24, and Z25. The intermediate packet Z13 is looked up to get α 2 ═ 3, ξ 2 ═ 2, η 2 ═ 2, and the resulting intermediate packets are denoted Z26 and Z27.
Step 4.5 is performed on η 1 intermediate packets, i.e., Z11, Z12, Z13, one by one, resulting in η ═ 7 intermediate packets, Z21, Z22, Z23, Z24, Z25, Z26, and Z27, respectively.
Step 4.6. for η intermediate groupings, i.e. Z21, Z22, Z23, Z24, Z25, Z26 and Z27, steps 4.3-4.6 are performed one by one, i.e. η 1 is η: in execution, steps 4.3-4.4 are performed for Z21, and critical mask pattern screening is not completed. And continuing to perform steps 4.3-4.4 on Z22, finishing the screening of the key mask patterns, wherein the key mask patterns in Z22 are the screening results of the key mask patterns, and the total number of the key mask patterns is 2.
The invention screens out 12 key graphs, and screens out 15 key graphs by the similar technology in the Tachyon software. It is clear that the number of key patterns screened by the present invention is less than that of Tachyon software. Free illumination was then obtained with SMO with two sets of key patterns, respectively. MO was performed on all 40 patterns using the resulting free illumination as illumination condition, and the size of the common process window was compared. The light source mask optimization is carried out by using the key mask pattern screened by the method, the Depth of focus (Depth of focus) is 90.32nm under the condition of common 5% exposure latitude (exposure latitude), and the Depth of focus (Depth of focus) is 75.67nm greater than the Depth of focus obtained by using the screening result of tachhon software, which indicates that the method increases the process window of the light source mask optimization. Compared with the similar technology in the Tachyon software, the method not only effectively reduces the number of key graphs, but also obtains a larger process window, and shows that the method is superior to the prior art 1.
Claims (2)
1. A key graph screening method for full-chip light source mask optimization is characterized by comprising the following steps:
step 1, calculating a diffraction spectrum of a mask pattern:
calculating each mask pattern PiThe formula is as follows:
Fi＝FFT{Pi}，
wherein, FiIndicates the ith mask pattern PiI ═ 1, …, N;
step 2, main frequency extraction:
step 2.1, preprocessing the diffraction spectrum of the mask pattern, and recording the preprocessed diffraction spectrum of the mask pattern as zetai：
Step 2.2, when the mask pattern has periodicity, entering step 2.3; otherwise, entering step 2.4;
step 2.3 cycle mask pattern main frequency extraction:
zeta of diffraction spectrum of periodic mask patterniThe diffraction grating consists of discrete diffraction peaks, and each diffraction peak corresponds to one diffraction order;
when the periodic mask pattern only contains a single period, extracting the first diffraction order as the main frequency, and searching the frequency coordinate corresponding to the intensity peak of the main frequency
Called the peak frequency, the dominant frequency is recorded and expressed as
Where j denotes the number of the current primary frequency, j ═ 1, …, M;
when the periodic mask pattern includes a plurality of periods, the main frequency extraction step is as follows:
a. searching the diffraction order with the maximum intensity;
b. extracting the diffraction order with the maximum intensity as the main frequency, and finding the peak frequency of the main frequency
Record the dominant frequency, expressed as
c. At the diffraction spectrum ζiRemoving the main frequency extracted in the step b and the corresponding harmonic diffraction order;
d. repeating steps a to c until all diffraction orders are removed;
performing the operation on all the periodic mask patterns until the main frequencies corresponding to all the periodic mask patterns are recorded;
searching for equal main frequency in the main frequencies of all recorded periodic mask patterns, recording all periodic patterns corresponding to all the same main frequencies by recording the equal main frequency as one main frequency;
step 2.4. extraction of main frequency of non-periodic mask pattern:
zeta of diffraction spectrum of aperiodic mask patterniComposed of consecutive diffraction peaks, extracting the pretreated diffraction spectrum zetaiAs the dominant frequency of the aperiodic mask pattern; then, one of the main frequencies is selected, and the peak frequency is searched
And the boundary of the projection of the main frequency on the diffraction spectrum plane, as profile c, the main frequency is recorded and is represented as
Performing the above operation on all the main frequencies of the current non-periodic mask pattern, and recording the current non-periodAll the main frequencies of the desired mask pattern; until the dominant frequencies of all non-periodic mask patterns are recorded;
searching for equal main frequency in the main frequencies corresponding to all recorded non-periodic mask patterns, recording the main frequency as one main frequency, and recording all non-periodic patterns corresponding to all the same main frequencies;
step 3. Primary frequency clustering
Step 3.1, determining the coverage relation between every two main frequencies according to the coverage rule, and recording all the main frequencies covered by each main frequency and the number of the main frequencies;
the coverage rule in step 3.1 specifically includes:
two main frequencies are arbitrarily selected from the main frequencies obtained in the step 2 and are marked as SAAnd SB；
Setting a distance threshold dTDistance threshold dTThe value range is 0.01 NA/lambda-0.2 NA/lambda, wherein NA represents the numerical aperture of the projection objective of the photoetching machine, and lambda represents the exposure wavelength of the photoetching machine;
i. when S isAAnd SBAll are the main frequencies of the non-periodic pattern: if S isBHas a peak frequency of SAC and dTWithin a jointly determined range, then SBIs just covered by SACovering, otherwise, not covering; contours c and dTThe commonly determined range is defined as the extension d of the contour cTThe range encompassed by the distance of (d);
when the main frequency SAAnd SBAre the main frequencies of the periodic pattern: if the primary frequency SAAnd SBIf the peak frequencies are different, S is determinedAPeak frequency and S ofBWhether the higher harmonic frequencies of the peak frequency are the same or not, if so, SACovering SB(ii) a Or SBPeak frequency and S ofAThe higher harmonic frequencies of the peak frequency are the same, then SBCovering SA(ii) a Otherwise, not covering;
when the main frequency SAIs the main frequency, S, of a periodic patternBFor the main frequencies of the non-periodic pattern: if it is notSAHas a peak frequency of SBC and dTWithin a jointly determined range, then SBQuilt SACovering; otherwise, not covering;
step 3.2, marking the states of all the main frequencies as unvisited;
step 3.3, searching the main frequency with the maximum number of the covered main frequencies from the main frequencies with the unknown states, and if a plurality of main frequencies exist, taking the main frequency with the maximum absolute value of the element in the peak frequency, and calling the main frequency as the central main frequency; the central main frequency and the main frequency covered by the central main frequency are used as a group of main frequencies, which are called as main frequency groups, and the states of all the main frequencies in the group are modified into visited;
step 3.4, repeating the step 3.3 until the states of all the main frequencies are modified into visited;
step 3.5, searching the repeatedly grouped main frequencies in all the main frequencies, calculating the distance between the peak frequency of the main frequencies and the peak frequency of the central main frequency of the group in which the main frequencies are positioned, dividing the main frequencies into the group with the minimum distance, and deleting the main frequencies in other groups in which the main frequencies are positioned;
step 3.6 find out the main frequency covering all main frequencies except the central main frequency in the main frequency group; performing the above-described operation on all the primary frequency groups with the primary frequency as a center primary frequency;
step 4 Key Pattern screening
Step 4.1, marking the states of all central main frequencies as unvisited, and dividing the states into a group called an intermediate group;
step 4.2, according to the main frequency information of each mask pattern extracted in the step 2, the mask patterns with the largest number of central main frequencies in the unvisited state are found, the number of the mask patterns is represented as alpha 1, and the alpha 1 is more than or equal to 1; finding out mask patterns with different central main frequencies from the central main frequencies of other mask patterns in alpha 1 mask patterns, wherein the number of the mask patterns is expressed as xi 1, and xi 1 is more than or equal to 0;
if xi 1 is greater than 1, taking any one mask pattern from xi 1 mask patterns, and taking the mask pattern and the mask patterns except the xi 1 mask patterns in alpha 1 mask patterns as key mask patterns;
if ξ 1 is less than or equal to 1, all the α 1 mask patterns are taken as key mask patterns; the number of the key mask patterns is represented as eta 1, and alpha 1 is more than or equal to eta 1 and more than or equal to 1;
copying eta 1 parts of the intermediate packets to generate eta 1 intermediate packets;
distributing the eta 1 key mask patterns into eta 1 intermediate groups, namely adding one key mask pattern to each intermediate group;
step 4.3. any one of the η 1 intermediate packets is marked as Ω, and the following operations are performed:
modifying the state of the central main frequencies of the key mask patterns assigned to the central main frequencies in omega to visited and modifying the states of other central main frequencies belonging to one main frequency group with the central main frequencies to visited;
step 4.4, judging whether all the states of the central main frequencies in the omega are all visual, if so, finishing the screening of the key mask pattern, wherein the key mask pattern in the omega is the screening result of the key mask pattern;
otherwise, executing steps 4.3-4.4 one by one on the rest intermediate packets in the eta 1 intermediate packets, and entering step 4.5 if the intermediate packets are not finished;
step 4.5, according to the main frequency information of each mask pattern extracted in the step 2, the mask patterns with the largest number of central main frequencies in the unvisited state are found, the number of the mask patterns is represented as alpha 2, and the alpha 2 is more than or equal to 1; finding out mask patterns with central main frequency of the unvisited state and central main frequency of other mask patterns with the unvisited state which are different from the central main frequency of the unvisited state in alpha 2 mask patterns, wherein the number of the mask patterns is expressed as xi 2, and xi 2 is more than or equal to 0; if xi 2 is greater than 1, taking out one of xi 2 mask patterns, and taking the mask pattern and the mask patterns except the xi 2 mask patterns in the alpha 2 mask patterns as key mask patterns; if ξ 2 is less than or equal to 1, all the alpha 2 mask patterns are taken as key mask patterns; the number of the key mask patterns is represented as eta 2, and alpha 2 is more than or equal to eta 2 and more than or equal to 1;
copying eta 2 parts of the intermediate packets to generate eta 2 intermediate packets;
distributing the eta 2 key mask patterns into eta 2 intermediate groups, namely adding one key mask pattern to each intermediate group;
after the eta 1 intermediate groups are executed one by one, eta intermediate groups are obtained in total, and the step 4.6 is carried out;
and 4.6, returning the eta 1 to the step 4.3.
2. The method for screening key patterns for full-chip light source mask optimization according to claim 1, wherein the step 2.1 is performed on the diffraction spectrum FiThe pretreatment is carried out, and the specific steps are as follows:
a. calculating the intensity of the diffraction spectrum, wherein the intensity of the diffraction spectrum is FiIs expressed as | FiAnd build a list of all intensities Ilist；
b. Removing diffraction orders and zero orders which cannot enter a projection objective of the photoetching machine from all diffraction orders of the diffraction spectrum;
c. setting a threshold τ indicating removal below intensity IτThe similarity between the diffraction spectrum of the component (a) and the original diffraction spectrum is shown, and the value range of tau is 80-99%;
d. in intensity List I according to the value of τlistIn-traversal search strength IτRemoving intensity lower than I in diffraction spectrumτThe diffraction order of (a).
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