CN112558426B - Photoetching machine matching method based on covariance matrix adaptive evolution strategy - Google Patents

Photoetching machine matching method based on covariance matrix adaptive evolution strategy Download PDF

Info

Publication number
CN112558426B
CN112558426B CN202011457382.2A CN202011457382A CN112558426B CN 112558426 B CN112558426 B CN 112558426B CN 202011457382 A CN202011457382 A CN 202011457382A CN 112558426 B CN112558426 B CN 112558426B
Authority
CN
China
Prior art keywords
photoetching machine
light source
photoetching
machine
matched
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202011457382.2A
Other languages
Chinese (zh)
Other versions
CN112558426A (en
Inventor
李思坤
陈俞光
唐明
王向朝
陈国栋
胡少博
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai Institute of Optics and Fine Mechanics of CAS
Original Assignee
Shanghai Institute of Optics and Fine Mechanics of CAS
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shanghai Institute of Optics and Fine Mechanics of CAS filed Critical Shanghai Institute of Optics and Fine Mechanics of CAS
Priority to CN202011457382.2A priority Critical patent/CN112558426B/en
Publication of CN112558426A publication Critical patent/CN112558426A/en
Application granted granted Critical
Publication of CN112558426B publication Critical patent/CN112558426B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
    • G03F—PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
    • G03F7/00—Photomechanical, e.g. photolithographic, production of textured or patterned surfaces, e.g. printing surfaces; Materials therefor, e.g. comprising photoresists; Apparatus specially adapted therefor
    • G03F7/70—Microphotolithographic exposure; Apparatus therefor
    • G03F7/70483—Information management; Active and passive control; Testing; Wafer monitoring, e.g. pattern monitoring
    • G03F7/70491—Information management, e.g. software; Active and passive control, e.g. details of controlling exposure processes or exposure tool monitoring processes
    • G03F7/705—Modelling or simulating from physical phenomena up to complete wafer processes or whole workflow in wafer productions
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Exposure And Positioning Against Photoresist Photosensitive Materials (AREA)
  • Exposure Of Semiconductors, Excluding Electron Or Ion Beam Exposure (AREA)

Abstract

A lithography machine matching method based on a covariance matrix adaptive evolution strategy is disclosed. The method takes the key size of the space image as a parameter for describing the imaging performance of the photoetching machine, optimizes the illumination light source of the photoetching machine described in a pixelization manner through a covariance matrix adaptive evolution strategy, and realizes high-precision matching between photoetching. The method fully utilizes the advantages of the covariance matrix adaptive evolution strategy on the complex optimization problem of the medium scale degree, improves the matching method of the free illumination system photoetching machine, and improves the matching precision and efficiency of the existing method. The method is suitable for matching between immersion lithography machines with free illumination systems.

Description

Photoetching machine matching method based on covariance matrix adaptive evolution strategy
Technical Field
The invention relates to a free illumination system lithography machine, in particular to a lithography machine matching method based on a covariance matrix adaptive evolution strategy.
Background
Photolithography is the core process in integrated circuit manufacturing. To reduce the economic and time costs, lithographic processes are often developed on reference lithography machines. And applying a new photoetching process to a production line when the chip is produced in quantity. Generally, a production line has a plurality of lithography machines, which may have different models and suppliers, and the performances of the lithography machines in different models generally have obvious differences. Even the same model of lithography machine may cause large differences in lithography performance including imaging quality due to slight differences in hardware specifications. The performance of different photoetching machines can be different due to the difference, so that the process migration fails, and the production efficiency and the yield of products are influenced. In order to realize the quick transfer of the photoetching process, expand the capacity and improve the chip manufacturing yield, matching of a photoetching machine is needed, and the imaging performance of the photoetching machine to be matched and the imaging performance of a reference photoetching machine are consistent as much as possible by adjusting the adjustable parameters of the photoetching machine (the photoetching machine to be matched) on a production line.
Common lithography machine matching techniques are Critical Dimension (CD) measurement based matching techniques, photoresist model based matching techniques, and optical model based matching techniques. In the former two technologies, CD data on a silicon wafer is needed to represent the imaging performance difference between two lithography machines, and sensitivity information of adjustable parameters for matching is calculated to realize lithography machine matching. To ensure matching accuracy requires repeated multiple measurements of the CD under multiple conditions (e.g., multiple illumination patterns, multiple mask patterns, multiple pattern cycles, multiple exposure doses, multiple defocus positions), respectively, which consumes a significant amount of lithography time and measurement time. The matching technology based on the optical model (prior art 1, Yuan He, Erik Byers, and Scatt Light et al, Simulation-based patterning using scanner calibration and design data to reduce calibration on CD metrology, Proc. SPIE.7640,764014(2010)) utilizes the optical model of the photoetching machine to carry out adjustable parameter sensitivity calculation, does not need to carry out time-consuming CD measurement, avoids the influence of measurement noise and the calibration error of the photoresist model on the matching precision, is accurate and quick, has higher matching precision when the photoetching machine is the main influencing factor causing the unmatched graphs, and is a common technology in the production of high-end chips. The adjustable parameters comprise light source, projection objective numerical aperture, projection objective wave aberration and the like. The light source has high degree of freedom and is a main parameter for adjustment in matching of the photoetching machine. The prior art optimizes the Light source by Newton method or least square method to achieve Matching of lithography machines (prior art 2, Yuan He, Alexander Serebryakov and Scott Light et al, A Study on the Automation of Scanner Matching, Proc. SPIE.7973,79731H (2013); prior art 3, Lesikun, Lespajie, Dynasty, Yangxi, Yangxing, lithography machine Matching method, CN 108170006A). With the use of a pixilated light source with higher degree of freedom, the realization of lithography machine matching based on illumination sensitivity calculation is not applicable any more, but is realized by calculating the change of a CD and adding or deleting pixels of the light source according to the change, the prior art adopts a genetic algorithm (prior art 4, Higger, Lisikun, the dynasty, a lithography machine matching method, CN109031892B) to code the pixilated light source into a chromosome, and the difference between the lithography machine to be matched and a reference lithography machine is reduced by continuously updating the light source information through the genetic algorithm. However, for the high-precision pixelized light source matching, when the sampling precision of the light source is increased, the search space expanded by the genetic algorithm is exponentially multiplied, the search time is greatly increased, and the optimization efficiency is seriously influenced. And in some cases genetic algorithms tend to be locally optimal rather than globally optimal, adversely affecting the accuracy of the match.
Disclosure of Invention
The invention provides a photoetching machine performance matching method aiming at a photoetching machine with a free illumination system. The method carries out coding sampling on a pixilated light source, and continuously searches for an optimal solution in a sampling-updating-resampling mode through a covariance matrix self-adaptive evolution strategy, so that the difference of the photoetching performance between a photoetching machine to be matched and a reference photoetching machine is reduced. Aiming at the characteristics of high freedom degree of a light source and large parameter scale of a free illumination system, the method fully utilizes the advantages of the method on the medium-scale complex optimization problem and improves the matching precision and efficiency of the photoetching machine.
The technical solution of the invention is as follows:
a lithography machine matching method based on Covariance Matrix adaptive Evolution Strategy (CMA-ES) comprises the following steps:
1) and (3) checking a photoetching machine and a gluing developing machine:
checking the states of the reference photoetching machine and the photoetching machine to be matched: the method comprises the following steps of photoetching machine characteristic information such as projection objective cold aberration, illumination ellipticity, illumination partial coherence factors, laser light source stability, stray light level, illumination uniformity, mask table and workpiece table synchronization errors and the like. Checking and confirming that the parameters of the reference photoetching machine and the photoetching machine to be matched are correctly set, and if the parameters are inconsistent with the parameters specified in the specification, adjusting the parameters in time to ensure that the reference photoetching machine and the photoetching machine to be matched work normally and are in the optimal working state. And checking the working state of the gluing developing machine, the working state of the CD detection system and the photoresist batch to ensure that the gluing developing machine works normally and is in the optimal working state. And confirming that the photoresist batches are the same and the working state of the CD detection system is normal.
2) And (3) exposure verification:
one-dimensional through-pitch pattern masks or a part of mass production two-dimensional pattern masks screened in advance are used as test masks, and the number of the test masks is M. And adjusting the adjustable parameters of the reference photoetching machine and the photoetching machine to be matched to the same value, wherein the adjustable parameters comprise the shape of a light source, the numerical aperture of the projection objective and the wave aberration of the projection objective. Sequentially loading test masks by a reference photoetching machine and a photoetching machine to be matched for exposure and development, respectively measuring the CDs of the photoresist patterns on the silicon wafer by using a CD detection system, and if the mean square difference value of the difference between the CDs of the photoresist patterns generated by the exposure of the two photoetching machines is larger than a target value CDRMSOr the maximum value of the difference between CDs is greater than the target value CDMAXThen the lithography machine needs to be matched.
3) Matching a photoetching machine:
the state file (SFF) of the reference lithography machine is read. The state file comprises the characteristic information of the photoetching machine, such as the Numerical Aperture (NA) of the projection objective lens of the photoetching machine, a partial coherence factor of the illumination system, the pupil distribution of the actually measured illumination system, the exposure dose, the defocusing amount, the aberration of the actually measured projection objective lens, the inclination factor of the workpiece table, the mechanical vibration level of the optical system of the photoetching machine, the laser bandwidth and the like. Setting an aerial image intensity threshold Tr. Setting the photoetching simulation software according to the reference photoetching machine state file, and calculating the threshold value TrTest mask aerial image CD values of
Figure BDA0002829370000000031
And reading the state file of the photoetching machine to be matched, and setting photoetching simulation software according to the state file. Setting initial sampling step size (standard deviation) sigmainitAnd an evaluation threshold value Fs。
And optimizing the light source of the photoetching machine to be matched by utilizing a covariance adaptive evolution strategy. The reference photoetching machine measures the obtained light source pattern JRef(size N)S×NS) Go forward and go forwardLine coding calculation to generate initial coded light source
Figure BDA0002829370000000032
The coding mode is real number coding, and the coded light source is as follows:
Figure BDA0002829370000000033
wherein the content of the first and second substances,
Figure BDA0002829370000000034
the intensity value of the ith pixel point in the kth (k is 1, 2, …, N) sample light source is shown, the luminance value of the light-emitting area is 1, the luminance value of the non-light-emitting area is 0, and N is the sum of the number of the discrete light source point pixels. The specific steps of iterative calculation of the target light source graph of the photoetching machine to be matched are as follows:
(ii) decomposing the population of the g (g 1, 2, …)
Figure BDA0002829370000000035
The k (k is 1, 2, …, lambda) individual decoding calculation corresponds to the corresponding pair of light source patterns
Figure BDA0002829370000000036
According to the pattern of the light source
Figure BDA0002829370000000037
Calculating the threshold T by using photoetching simulation software which is set by the state file of the photoetching machine to be matchedrLower aerial image CD value, noted
Figure BDA0002829370000000038
Figure BDA0002829370000000039
And calculating an evaluation function
Figure BDA00028293700000000310
The calculation formula is as follows:
Figure BDA00028293700000000311
② selecting the best (namely, the g is 1, 2, …) solution in the g generation
Figure BDA00028293700000000312
Minimum) of individuals
Figure BDA00028293700000000313
Its evaluation value is recorded as
Figure BDA00028293700000000314
If it is
Figure BDA00028293700000000315
If the evaluation value is less than the evaluation threshold value, the step (b) is carried out, otherwise, the step (c) is carried out.
Third, according to the global step length (standard deviation) sigma of the g generation(g)Collecting the g +1 generation population
Figure BDA0002829370000000041
In which the individual
Figure BDA0002829370000000042
Should follow a multivariate normal distribution
Figure BDA0002829370000000043
Figure BDA0002829370000000044
In the above formula, m(g)Is the mean of the solution vectors of the g-th generation,
Figure BDA0002829370000000045
is a multivariate normal distribution with a mean value of 0, C(g)Is the solution vector of the g generation
Figure BDA0002829370000000046
The covariance matrix of (2).
Substituting all the obtained individual solutions into an evaluation function to obtain corresponding evaluation values
Figure BDA0002829370000000047
It is sorted in the following order:
Figure BDA0002829370000000048
wherein the subscript i: λ denotes the ith position in λ individuals, and the weighted mean is calculated by taking the first μ ═ λ/2 individuals to update the mean, i.e.
Figure BDA0002829370000000049
Wherein ω isiIs a weight and
Figure BDA00028293700000000410
ωi＞0(i＝1，2，…，μ)。
adaptive update step size (standard deviation) sigma(g). In order to realize the overall scaling of the covariance matrix and improve the convergence speed of the algorithm, the accumulation path length control independent of the covariance matrix update, namely step length update, needs to be introduced. Firstly, learning the evolution path of the accumulation step length from the evolution information of the previous generation (the g generation)
Figure BDA00028293700000000411
Figure BDA00028293700000000412
Wherein
Figure BDA00028293700000000413
For the g-th generation cumulative evolution path, cσ＝(μeff+2)/(N+μeff+3) < 1 is the step accumulation constant,
Figure BDA00028293700000000414
selecting a quality, C, for the effective variance(g)Is the g-th generation covariance matrix. Then, according to the accumulated evolution path, the step length sigma is updated(g)
Figure BDA0002829370000000051
Wherein d isσIs a damping coefficient, approximately 1;
Figure BDA0002829370000000052
normally distributed random vector norm expected length:
Figure BDA0002829370000000053
and sixthly, adaptively updating the covariance matrix. It is important to be able to update the covariance matrix by fully utilizing a series of evolving intergenerative information, so by introducing an evolution path
Figure BDA0002829370000000054
Intergenerative information at cumulative covariance update:
Figure BDA0002829370000000055
thereby constructing a Rank-1-Update updating mechanism to fully utilize the correlation relationship between the continuously evolving generation variation step sizes:
Figure BDA0002829370000000056
meanwhile, in order to fully utilize effective information provided by a large population and improve the global search capability, the best mu individuals in the sub-population are selected by introducing a Rank-mu-Update updating mechanism, and the mu individuals are utilized to be relative to the mean value m(g)So that the solution of the most recent algebra has a higher valueThereby updating the covariance matrix:
Figure BDA0002829370000000057
in conclusion, the covariance matrix is updated by combining two updating processes of Rank-mu-Update and Rank-1-Update, so that the information between generations is fully utilized, and the information of the whole population is fully utilized:
Figure BDA0002829370000000058
in the above formula cc，cμ，c1Respectively representing the learning rate or the accumulation constant of the updating process of the covariance matrix, the Rank-mu-Update and the Rank-1-Update.
Seventhly, sampling based on multivariate normal distribution is carried out according to the updated step length, the covariance matrix and the corresponding evolution path, and lambda random samples are generated
Figure BDA0002829370000000059
And returning to the step I.
Stopping iteration and marking the obtained individual as xbestDecoding it to produce a light source shape JbestAnd outputting the target light source shape as the target light source shape of the photoetching machine to be matched.
4) Exposure verification
According to the solved target light source J of the photoetching machine to be matchedbestAnd generating a parameter submenu of the photoetching machine to be matched, which needs to be adjusted, namely the nominal parameters set by the light source of the photoetching machine to be matched. And inputting the parameter submenu into the photoetching machine to be matched to adjust the adjustable parameters. And loading a test mask to be matched with the photoetching machine for exposure and development. The CD of the photoresist pattern on the silicon wafer is measured using a CD detection system. If the mean square deviation value of the difference between the photoresist pattern CDs generated by the exposure of the two photoetching machines is smaller than the target value CDRMSOr the maximum value of the difference between CDs is smaller than the target value CDMAXIf the matching is not successful, the matching is requiredThe mask is redesigned by performing Optical Proximity Correction (OPC) or optical Source Mask Optimization (SMO) again.
Compared with the prior art, the method uses a covariance adaptive evolution strategy (CMA-ES) to carry out photoetching machine matching, and the CMA-ES adopts a preferred truncation selection strategy, so that the matching efficiency and precision are higher, premature convergence to a certain degree can be avoided, and the method is suitable for the matching problem of a pixelized light source.
Drawings
FIG. 1 is a flow chart of a method for matching performance of a lithography machine using the present invention.
FIG. 2 is an illumination source of a reference lithography machine used in an embodiment of the present invention.
FIG. 3 is a test mask and matching mask pattern employed by an embodiment of the present invention.
FIG. 4 is a diagram of a matching lithography machine pupil used in an embodiment of the present invention.
FIG. 5 shows an illumination source of a lithography machine to be matched after the present invention has been implemented.
FIG. 6 is an illumination source of a lithography machine to be matched after matching of the lithography machine by using a genetic algorithm.
FIG. 7 is a CD error of line empty patterns of different periods of a reference lithography machine and a lithography machine to be matched before and after matching.
Detailed Description
The present invention will be further described with reference to the following examples and drawings, but the scope of the present invention should not be limited by these examples.
In the present embodiment, referring to the intensity distribution of the illumination light source of the lithography machine, as shown in fig. 2, the luminance value of the white area is 1, the luminance value of the black area is 0, the light source grid is 101 × 101, and the number of effective light source points S in the pupil range is 8048. The adopted test mask and the matching mask are one-dimensional through-pitch line empty pattern masks shown in figure 3, the line width of a mask pattern is 45nm, the type is a binary mask, the transmissivity of a white area is 1, and the transmissivity of a black area is 0. The mask pattern has a period of 45 in total of 120nm, 140nm, 160nm, … and 1000nm, i.e., M is 45, and the horizontal line segment marked in the figure is the cross-sectional position of the computed aerial image CD. The reference photoetching machine and the photoetching machine to be matched are both in an immersion type, and the working wavelength of the photoetching machine is 193 nm. The numerical aperture of a projection objective lens of the photoetching machine is set to be 1.35, the refractive index of the immersion liquid is 1.44, and the zoom magnification R is 0.25. The matching steps of the photoetching machine are as follows:
1) and (3) checking a photoetching machine and a gluing developing machine:
checking the states of the reference photoetching machine and the photoetching machine to be matched: the inspected portions include projection objective cold aberration, ellipticity of illumination, partial coherence factor of illumination, laser source stability, stray light level, illumination uniformity, mask stage workpiece stage synchronization error, and the like. Checking and confirming that the parameters of the reference photoetching machine and the photoetching machine to be matched are correctly set, and if the parameters are inconsistent with the parameters specified in the specification, adjusting the parameters in time to ensure that the reference photoetching machine and the photoetching machine to be matched work normally and are in the optimal working state; and checking the work flow of the glue spreading developing machine, the working state of the CD detection system and the photoresist batch to ensure that the glue spreading developing machine works normally and is in the optimal working state, the photoresist batch is the same, and the working state of the CD detection system is normal.
2) And (3) exposure verification:
adjusting the adjustable parameters of the reference photoetching machine and the photoetching machine to be matched to the same value, respectively loading the test masks for exposure and developing, measuring the CD of the photoresist pattern on the silicon wafer by using a CD detection system, wherein the difference between the CDs of the photoresist patterns generated by the exposure of the two photoetching machines is shown in the figure, the root mean square value is 1.3566nm, and the CD is usedRMSFor example, if the standard of 1nm is out of the allowable range of the process, the current lithography machine to be matched needs to be matched, and the next step is performed.
3) Matching a photoetching machine:
reading the state file (SFF) of the reference photoetching machine, setting photoetching simulation software according to the state file of the reference photoetching machine, and calculating the threshold value TrThe CD value of the space image of the test mask is recorded
Figure BDA0002829370000000071
And reading the state file of the photoetching machine to be matched, and setting photoetching simulation software according to the state file. Setting initial samplingStep size (standard deviation) σinit0.008 and a fitness threshold Fs＝0.07nm。
The reference photoetching machine measures the obtained light source pattern JRef(size N)S×NS) Performing coding calculation to generate initial coding light source
Figure BDA0002829370000000072
The coding mode is real number coding, and the coded light source is as follows:
Figure BDA0002829370000000073
wherein the content of the first and second substances,
Figure BDA0002829370000000074
the method comprises the following steps of (1) obtaining an intensity value of an ith pixel point in a kth (k is 1, 2, …, N) sample light source, iteratively calculating a target light source graph of the photoetching machine to be matched, wherein the luminance value of a light-emitting area is 1, the luminance value of a non-light-emitting area is 0, and N is the sum of the number of discrete light source point pixels, and the method comprises the following specific steps:
(ii) breeding the g (g is 1, 2, …) th generation
Figure BDA0002829370000000075
Respectively decoding and calculating corresponding light source patterns
Figure BDA0002829370000000076
According to the figure
Figure BDA0002829370000000077
Calculating threshold T by using photoetching simulation software which is set by a photoetching machine state file to be matchedrLower aerial image CD value, noted
Figure BDA0002829370000000081
And calculating an evaluation function
Figure BDA0002829370000000082
The calculation formula is as follows:
Figure BDA0002829370000000083
② calculating the individual with the minimum fitness in the k (k is 1, 2, …, lambda is more than or equal to 2) th generation solution
Figure BDA0002829370000000084
Its fitness is recorded as
Figure BDA0002829370000000085
If it is
Figure BDA0002829370000000086
Entering step (b), otherwise entering step (c).
③ according to the step length sigma(g)Collecting samples from the multivariate normal distribution of the kth generation solution
Figure BDA0002829370000000087
Figure BDA0002829370000000088
The number of child solutions of the first generation is generally λ ═ 4+ floor (3 × log (n)), where floor denotes rounding up.
Substituting all the solutions into an evaluation function to obtain corresponding evaluation values
Figure BDA0002829370000000089
They are sorted in the following order.
Figure BDA00028293700000000810
Taking the updated mean of the first mu-lambda/2 individuals (rounding down), and taking the super-linear weight omegai＝log[max(i，λ/2)]-log(i)。
Self-adapting step length updating. Calculating an evolution path from the accumulated information
Figure BDA00028293700000000811
Figure BDA00028293700000000812
Updating the step size σ(g)：
Figure BDA00028293700000000813
Step-size integration constant set to cσ＝(μeff+2)/(N+μeff+3) effective variance selection quality of
Figure BDA00028293700000000814
Sixthly, updating the adaptive covariance matrix. Calculating an evolution path from the accumulated information
Figure BDA0002829370000000091
Figure BDA0002829370000000092
Updating the covariance matrix of the g +1 th generation of samples:
Figure BDA0002829370000000093
the Rank-1-Update learning rate is set to
Figure BDA0002829370000000094
The Rank-mu-Update learning rate is set as
Figure BDA0002829370000000095
The covariance accumulation constant is set to
Figure BDA0002829370000000096
Seventhly, according to the new step length, the covariance matrix and the corresponding evolution path, carrying out a sampling process based on multivariate normal distribution to generate lambda random samples
Figure BDA0002829370000000097
And returning to the step I.
Stopping iteration, and marking the obtained solution vector as xbestDecoding it to produce a light source shape JbestOutputting the target light source shape as a target light source shape of the photoetching machine to be matched; the matched light source graph is shown in FIG. 5, the CD error obtained by simulation of the photoetching simulation software is shown in FIG. 7, and the CD error after matching is not more than 0.17 nm. For the same reference photoetching machine and photoetching machine to be matched, the time is reduced from 1043.2 seconds to 304.76 seconds and is reduced by about 70.8 percent when the reference photoetching machine and the pixelized light source matching method based on the genetic algorithm (the prior art 4, the council Jie, Liskun, the dynasty and the photoetching machine matching method, CN109031892B), and the mean square root value of the CD error is reduced from 0.1894nm to 0.0695nm and is reduced by about 63.3 percent.
4) Exposure verification
And finally, producing a parameter submenu of the photoetching machine to be matched according to the matched light source pattern of the illumination system, and inputting the parameter submenu into the photoetching machine to be matched to adjust corresponding adjustable parameters. After adjustment, the photoetching machine to be matched exposes the test mask, and the CD of the photoresist pattern on the silicon wafer is measured. The difference between the measured CD and the reference lithography CD is smaller than the CDRMSAnd completing matching of the photoetching machine.
The above description is only one specific embodiment of the present invention, and the embodiment is only used to illustrate the technical solution of the present invention and not to limit the present invention. The technical solutions available to those skilled in the art through logical analysis, reasoning or limited experiments according to the concepts of the present invention are all within the scope of the present invention.

Claims (3)

1. A lithography machine matching method based on covariance matrix adaptive evolution strategy is characterized by comprising the following steps:
1) preparation work:
checking and adjusting the working state parameters of the reference photoetching machine and the photoetching machine to be matched so that the reference photoetching machine and the photoetching machine to be matched are in the optimal working state;
checking the working state of the gluing developing machine, the working state of a CD detection system and the photoresist batch to ensure that the gluing developing machine is in the optimal working state, and confirming that the photoresist batch is the same and the working state of the CD detection system is normal;
2) and (3) exposure verification:
adopting a one-dimensional through-pitch graphic mask or a two-dimensional graphic mask as test masks, wherein the number of the test masks is M;
adjusting the adjustable parameters of the reference photoetching machine and the photoetching machine to be matched to the same value, wherein the adjustable parameters comprise the shape of a light source, the numerical aperture of a projection objective and the wave aberration of the projection objective;
sequentially loading test masks to a reference photoetching machine and a photoetching machine to be matched for exposure and development, respectively measuring photoresist patterns CD of the photoresist patterns on a silicon wafer by using a CD detection system, and if the mean square difference value of the difference between the photoresist patterns CD generated by the exposure of the two photoetching machines is larger than a preset target value CDRMSOr the maximum value of the difference between the photo-resist pattern CDs is larger than a preset maximum target value CDMAxMatching the photoetching machine;
3) matching a photoetching machine:
reading a state file SFF of a reference photoetching machine, wherein the state file SFF comprises photoetching machine characteristic information such as Numerical Aperture (NA) of a photoetching machine projection objective, partial coherence factor of an illumination system, actually measured pupil distribution of the illumination system, exposure dose, defocusing amount, actually measured projection objective aberration, workpiece table inclination factor, mechanical vibration level of a photoetching machine optical system, laser bandwidth and the like;
carrying out space image intensity threshold T on photoetching simulation software according to reference photoetching machine state filerSetting, calculatingThreshold value TrTest mask aerial image values of
Figure FDA0003568831660000011
Reading the state file of the photoetching machine to be matched, setting photoetching simulation software according to the state of the file, and setting the initial sampling step length (standard deviation) sigmainitAnd an evaluation threshold value Fs；
Optimizing a light source of the photoetching machine to be matched by utilizing a covariance adaptive evolution strategy: the reference photoetching machine measures the obtained light source pattern JRef(size N)S×NS) Performing coding calculation to generate initial coded light source
Figure FDA0003568831660000012
The coding mode is real number coding, and the coded light source is as follows:
Figure FDA0003568831660000021
wherein the content of the first and second substances,
Figure FDA0003568831660000022
the intensity value of the ith pixel point in the kth (k is 1, 2, …, N) sample light source is obtained, the luminance value of the light-emitting area is 1, the luminance value of the non-light-emitting area is 0, and N is the sum of the number of the discrete light source point pixels; the specific steps of iterative calculation of the target light source graph of the photoetching machine to be matched are as follows:
(ii) decomposing the population of the g (g 1, 2, …)
Figure FDA0003568831660000023
The k (k is 1, 2, …, lambda) individual decoding calculation corresponds to the corresponding pair of light source patterns
Figure FDA0003568831660000024
According to the pattern of the light source
Figure FDA0003568831660000025
Calculating the threshold T by using photoetching simulation software which is set by the state file of the photoetching machine to be matchedrThe lower spatial image value is recorded as
Figure FDA0003568831660000026
And calculating an evaluation function
Figure FDA0003568831660000027
The formula is as follows:
Figure FDA0003568831660000028
② selecting the best (namely, the g is 1, 2, …) solution in the g generation
Figure FDA0003568831660000029
Minimum) of individuals
Figure FDA00035688316600000210
Its evaluation value is recorded as
Figure FDA00035688316600000211
If it is
Figure FDA00035688316600000212
If the evaluation value is less than the evaluation threshold value, the step (b) is carried out, otherwise, the step (c) is carried out;
third, according to the global step length (standard deviation) sigma of the g generation(g)Collecting the g +1 generation population
Figure FDA00035688316600000213
Wherein the individual
Figure FDA00035688316600000214
Following a multivariate normal distribution
Figure FDA00035688316600000215
Figure FDA00035688316600000216
In the above formula, m(g)Is the mean of the solution vectors of the g-th generation,
Figure FDA00035688316600000217
is a multivariate normal distribution with a mean value of 0, C(g)Is the solution vector of the g generation
Figure FDA00035688316600000218
The covariance matrix of (a);
substituting all the obtained individual solutions into an evaluation function to obtain corresponding evaluation values
Figure FDA00035688316600000219
It is sorted in the following order:
Figure FDA00035688316600000220
wherein the subscript i: λ denotes the ith position in λ individuals, and the weighted mean is calculated by taking the first μ ═ λ/2 individuals to update the mean, i.e.
Figure FDA0003568831660000031
Wherein ω isiAre weights and
Figure FDA0003568831660000032
adaptive update step size (standard deviation) sigma(g)：
Firstly, an accumulated step evolution path is learned from evolution information of a previous generation (g generation)
Figure FDA0003568831660000033
Figure FDA0003568831660000034
Wherein
Figure FDA0003568831660000035
For the g-th generation cumulative evolution path, cσ＝(μeff+2)/(N+μeff+3) < 1 is the step accumulation constant,
Figure FDA0003568831660000036
selecting a quality, C, for the effective variance(g)Is a covariance matrix of the g generation;
then, according to the accumulated evolution path, the step length sigma is updated(g)
Figure FDA0003568831660000037
Wherein d isσFor the damping coefficient, approximately 1,
Figure FDA0003568831660000038
for a normally distributed random vector norm expected length, the formula is as follows:
Figure FDA0003568831660000039
adaptive updating covariance matrix C(g+1)：
Introduction of evolution path
Figure FDA00035688316600000310
Accumulating the inter-generation information during covariance updating, and constructing a Rank-1-Update updating mechanism:
Figure FDA00035688316600000311
utilizing the correlation relationship between the continuous evolution generation variation step sizes:
Figure FDA00035688316600000312
introducing a Rank-mu-Update updating mechanism, selecting the best mu individuals in the sub-population, and utilizing the mu individuals relative to the mean value m(g)Such that the solution of the most recent algebra has a higher weight, thereby updating the covariance matrix:
Figure FDA0003568831660000041
updating the covariance matrix by combining two updating processes of Rank-mu-Update and Rank-1-Update, thereby not only fully utilizing the information between generations, but also fully utilizing the information of the whole population:
Figure FDA0003568831660000042
in the formula cc，cμ，c1Respectively representing the learning rate or the accumulation constant of the Update process of the covariance matrix, the Rank-mu-Update and the Rank-1-Update;
seventhly, sampling based on multivariate normal distribution is carried out according to the updated step length, the covariance matrix and the corresponding evolution path, and lambda random samples are generated
Figure FDA0003568831660000043
Returning to the step I;
stopping iteration and marking the obtained individual as xbestDecoding it to produce a light source shape JbestAnd outputting the target light source shape as the target light source shape of the photoetching machine to be matched.
2. The lithography machine matching method based on the covariance matrix adaptive evolution strategy as claimed in claim 1, further comprising:
4) exposure verification
According to the solved target light source J of the photoetching machine to be matchedbestGenerating a parameter submenu of the photoetching machine to be matched, which needs to be adjusted, namely a nominal parameter set by a light source of the photoetching machine to be matched;
inputting the parameter submenu into a photoetching machine to be matched to adjust adjustable parameters, and loading a test mask by the photoetching machine to be matched to expose and develop;
measuring the photoresist pattern CD on the silicon wafer by using a CD detection system: if the mean square deviation value of the difference between the photoresist pattern CDs generated by the exposure of the two photoetching machines is smaller than the target value CDRMSOr the maximum value of the difference between CDs is smaller than the target value CDMAXIf the mask matching fails, the mask needs to be redesigned by performing Optical Proximity Correction (OPC) or light Source Mask Optimization (SMO).
3. The matching method of the lithography machine based on the covariance matrix adaptive evolution strategy as claimed in claim 1 or 2, wherein the working state parameters of the reference lithography machine and the lithography machine to be matched comprise cold aberration of the projection objective, ellipticity of illumination, partial coherence factor of illumination, stability of laser light source, stray light level, illumination uniformity, and mask stage and stage synchronization error information.
CN202011457382.2A 2020-12-10 2020-12-10 Photoetching machine matching method based on covariance matrix adaptive evolution strategy Active CN112558426B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011457382.2A CN112558426B (en) 2020-12-10 2020-12-10 Photoetching machine matching method based on covariance matrix adaptive evolution strategy

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011457382.2A CN112558426B (en) 2020-12-10 2020-12-10 Photoetching machine matching method based on covariance matrix adaptive evolution strategy

Publications (2)

Publication Number Publication Date
CN112558426A CN112558426A (en) 2021-03-26
CN112558426B true CN112558426B (en) 2022-05-31

Family

ID=75061761

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011457382.2A Active CN112558426B (en) 2020-12-10 2020-12-10 Photoetching machine matching method based on covariance matrix adaptive evolution strategy

Country Status (1)

Country Link
CN (1) CN112558426B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113189851B (en) * 2021-04-23 2022-05-31 中国科学院上海光学精密机械研究所 Photoetching machine matching method based on BFGS quasi-Newton-interior point algorithm
CN114488719B (en) * 2022-02-23 2023-11-21 武汉大学 OPC method based on three-dimensional feature reinforcement
CN117454831B (en) * 2023-12-05 2024-04-02 武汉宇微光学软件有限公司 Mask pattern optimization method and system and electronic equipment

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104133348A (en) * 2014-08-07 2014-11-05 北京理工大学 Light source optimization method for adaptive photoetching system
CN109031892A (en) * 2018-07-23 2018-12-18 中国科学院上海光学精密机械研究所 Litho machine matching process
CN110320764A (en) * 2019-06-24 2019-10-11 中国科学院上海光学精密机械研究所 Litho machine matching process
CN111781804A (en) * 2020-07-16 2020-10-16 中国科学院上海光学精密机械研究所 Light source mask optimization method based on covariance matrix adaptive evolution strategy algorithm

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7954071B2 (en) * 2008-10-31 2011-05-31 Synopsys, Inc. Assist feature placement based on a focus-sensitive cost-covariance field

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104133348A (en) * 2014-08-07 2014-11-05 北京理工大学 Light source optimization method for adaptive photoetching system
CN109031892A (en) * 2018-07-23 2018-12-18 中国科学院上海光学精密机械研究所 Litho machine matching process
CN110320764A (en) * 2019-06-24 2019-10-11 中国科学院上海光学精密机械研究所 Litho machine matching process
CN111781804A (en) * 2020-07-16 2020-10-16 中国科学院上海光学精密机械研究所 Light source mask optimization method based on covariance matrix adaptive evolution strategy algorithm

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
A study on the automation of scanner matching;He，Yuan等;《proceedings of SPIE》;20130412;第8683卷;全文 *
基于差分进化算法的光刻机匹配方法;茅言杰等;《光学学报》;20191231;第39卷(第12期);全文 *

Also Published As

Publication number Publication date
CN112558426A (en) 2021-03-26

Similar Documents

Publication Publication Date Title
CN112558426B (en) Photoetching machine matching method based on covariance matrix adaptive evolution strategy
US20220027437A1 (en) Method and apparatus for inspection and metrology
US20230185990A1 (en) Method and apparatus for inspection and metrology
JP6735794B2 (en) Model-based process simulation method
US7652758B2 (en) Method of performing resist process calibration/optimization and DOE optimization for providing OPE matching between different lithography systems
CN100543588C (en) Generation has the method and the device making method of the mask of optical proximity correction features
TWI570523B (en) A method of performing model-based scanner tuning
KR20200113240A (en) Method for training machine learning models for computational lithography
US9971478B2 (en) Method and apparatus for inspection and metrology
CN109031892B (en) Matching method of photoetching machine
CN110320764B (en) Matching method of photoetching machine
CN113189851B (en) Photoetching machine matching method based on BFGS quasi-Newton-interior point algorithm
TW202418147A (en) Deep learning models for determining mask designs associated with semiconductor manufacturing
WO2024017808A1 (en) Deep learning models for determining mask designs associated with semiconductor manufacturing

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant