CN116385346A - Method and device for analyzing and processing critical dimension data of mask - Google Patents
Method and device for analyzing and processing critical dimension data of mask Download PDFInfo
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- CN116385346A CN116385346A CN202211733698.9A CN202211733698A CN116385346A CN 116385346 A CN116385346 A CN 116385346A CN 202211733698 A CN202211733698 A CN 202211733698A CN 116385346 A CN116385346 A CN 116385346A
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- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000012545 processing Methods 0.000 title claims abstract description 26
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- 238000012549 training Methods 0.000 claims abstract description 20
- 238000007405 data analysis Methods 0.000 claims description 12
- 238000003672 processing method Methods 0.000 claims description 2
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- 238000007781 pre-processing Methods 0.000 description 12
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- XUIMIQQOPSSXEZ-UHFFFAOYSA-N Silicon Chemical compound [Si] XUIMIQQOPSSXEZ-UHFFFAOYSA-N 0.000 description 7
- 229910052710 silicon Inorganic materials 0.000 description 7
- 239000010703 silicon Substances 0.000 description 7
- 238000001259 photo etching Methods 0.000 description 6
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- 238000000206 photolithography Methods 0.000 description 2
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- 230000011664 signaling Effects 0.000 description 2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
- G06T7/001—Industrial image inspection using an image reference approach
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- 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
- G03F1/00—Originals for photomechanical production of textured or patterned surfaces, e.g., masks, photo-masks, reticles; Mask blanks or pellicles therefor; Containers specially adapted therefor; Preparation thereof
- G03F1/36—Masks having proximity correction features; Preparation thereof, e.g. optical proximity correction [OPC] design processes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30148—Semiconductor; IC; Wafer
Abstract
According to the method and the device for analyzing and processing the critical dimension data of the mask, after the critical dimension data of the mask feature pattern under different gray-scale thresholds are obtained by measuring the acquired mask feature pattern by using measuring equipment, the effective gray-scale threshold and photoresist morphology information of the same feature pattern are determined by using the difference value between the critical dimension data of the same feature pattern; and training the optical proximity effect model by using the effective critical dimension data and the photoresist morphology corresponding to the effective gray level threshold value, and sending mask pattern data to be processed to the model after the model is obtained to obtain the photoresist critical dimension and morphology of the mask pattern to be processed, so that the fitting precision of the existing model can be improved, and meanwhile, the problem of incomplete data utilization is solved.
Description
Technical Field
The present disclosure relates to semiconductor technology, and more particularly, to a method and apparatus for analyzing and processing critical dimension data of a mask.
Background
Photolithography is the most critical process in chip fabrication and enables the transfer of a designed pattern onto a silicon wafer through a photolithographic imaging system. With the continuous reduction of the chip size, the exposure pattern on the silicon wafer will be distorted, so that a class of algorithm model named optical proximity effect (OPC) must be adopted to optimize the exposure pattern before the photoetching manufacture of chips with the size of 90nm or even below 180nm, and the accuracy of the OPC model plays an important role in the photoetching process.
In the conventional OPC modeling process, each measurement graph is quantized into only one value to participate in the establishment of a model, a large number of measurement images are not fully utilized, and only the size information of the feature graph in the plane of the silicon wafer is obtained, but no shape information in the vertical direction is obtained. And with the improvement of the complexity of the photoetching process and the requirement of fitting precision, more detailed data analysis processing is required to be carried out on the measured image so as to improve the data utilization rate, and the photoresist morphology is incorporated into the model.
Disclosure of Invention
The application provides a method and a device for analyzing and processing critical dimension data of a mask plate, which are used for solving the problems of incomplete data utilization and low fitting precision in the existing OPC model.
In one aspect, the present application provides a method for analyzing and processing critical dimension data of a mask, including:
acquiring mask pattern features, wherein the mask pattern features comprise mask pattern features to be trained and mask pattern features to be processed;
measuring the mask characteristic graph by using measuring equipment to obtain critical dimension data of the mask characteristic graph under different gray scale thresholds, and analyzing the difference value after calculating the difference value between the critical dimension data of the same characteristic graph to determine the effective gray scale threshold of the same characteristic graph;
training the optical proximity effect model by utilizing the effective critical dimension data of the mask characteristic pattern to be trained and the photoresist morphology to be trained to obtain an accurate optical proximity effect model, wherein the effective critical dimension data is the critical dimension data corresponding to an effective gray level threshold value;
and sending the effective critical dimension data of the mask pattern to be processed to the accurate optical proximity effect model to obtain the photoresist critical dimension and morphology corresponding to the mask pattern to be processed.
In another aspect, the present application provides a reticle critical dimension data analysis processing apparatus, including:
the acquisition module is used for acquiring and acquiring mask characteristic patterns, wherein the mask characteristic patterns comprise mask characteristic patterns to be trained and mask characteristic patterns to be processed;
the measuring module is used for measuring the mask characteristic patterns by using measuring equipment to obtain key size data of the mask characteristic patterns under different gray level thresholds, and analyzing the difference value after calculating the difference value between the key size data of the same characteristic pattern to determine the effective gray level threshold of the same characteristic pattern;
the training module is used for training the optical proximity effect model by utilizing the effective critical dimension data of the mask characteristic pattern to be trained and the photoresist morphology to be trained to obtain an accurate optical proximity effect model, wherein the effective critical dimension data is the critical dimension data corresponding to an effective gray level threshold value;
and the learning module is used for sending the effective critical dimension data of the mask characteristic pattern to be processed to the accurate optical proximity effect model to obtain the photoresist morphology corresponding to the mask characteristic pattern to be processed.
In yet another aspect, the present application provides an electronic device, including: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method as described above.
In yet another aspect, the present application provides a computer readable storage medium having stored therein computer executable instructions that when executed by a processor are configured to implement a reticle critical dimension data analysis processing method as described above.
According to the method and the device for analyzing and processing the critical dimension data of the mask, the critical dimension data of the mask characteristic pattern under different gray scale thresholds are obtained by measuring the acquired mask characteristic pattern by using the measuring equipment, and after the difference value between the critical dimension data of the same characteristic pattern is calculated, the difference value is analyzed, so that the effective gray scale threshold value of the same characteristic pattern is determined; training the optical proximity effect model by using the effective critical dimension data of the feature pattern of the mask to be trained and the acquired photoresist morphology to be trained to obtain an accurate optical proximity effect model; and finally, transmitting the effective critical dimension data of the feature pattern of the mask to be processed to the model to obtain the photoresist morphology corresponding to the feature pattern of the mask to be processed, so that the fitting precision of the OPC model can be effectively improved, and the problem of long weight setting time of the OPC model is solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
FIG. 1 is a schematic diagram of a reticle CD data analysis architecture according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method for analyzing and processing critical dimension data of a mask according to an embodiment of the present disclosure;
FIG. 3 is a signaling interaction schematic diagram of a method for analyzing and processing critical dimension data of a mask according to an embodiment of the present application;
FIG. 4 is a block diagram of a reticle critical dimension data analysis processing apparatus according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Specific embodiments thereof have been shown by way of example in the drawings and will herein be described in more detail. These drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
Photolithography is the most critical process in chip fabrication and enables the transfer of a designed pattern onto a silicon wafer through a photolithographic imaging system. With the continuous reduction of the chip size, the exposure pattern on the silicon wafer will be distorted, so that a class of algorithm model named optical proximity effect (OPC) must be adopted to optimize the exposure pattern before the photoetching manufacture of chips with the size of 90nm or even below 180nm, and the accuracy of the OPC model plays an important role in the photoetching process. In the conventional OPC modeling process, each measurement graph is quantized into only one value to participate in the establishment of a model, a large number of measurement images are not fully utilized, and only the size information of the feature graph in the plane of the silicon wafer is obtained, but no shape information in the vertical direction is obtained. And with the improvement of the complexity of the photoetching process and the requirement of fitting precision, more detailed data analysis processing is required to be carried out on the measured image so as to improve the data utilization rate, and the photoresist morphology is incorporated into the model.
Fig. 1 is a schematic diagram of a reticle critical dimension data analysis processing architecture according to an embodiment of the present application, and referring to fig. 1, the method mainly includes: a scanning electron microscope (CD-SEM) metrology device 101 for feature size measurement, a data preprocessing server 102, and a model server 103. The CD-SEM metrology device 101 may transmit reticle feature critical dimension data to the data preprocessing server 102, and the data preprocessing server 102 may retransmit the processed reticle feature critical dimension data to the model server 103.
The CD-SEM measuring apparatus 101 is a scanning electron microscope for determining the boundary of a pattern according to the gray scale of an image, and further calculating the line width.
The data preprocessing server 102 is a server dedicated to preprocessing data. Data preprocessing refers to the necessary processing of sorting, screening, sorting, etc. of collected data or performed before grouping.
The model server 103 has stored thereon a plurality of algorithm models. The algorithm model may be either a mathematical model or a simulation model. The method specifically comprises the related optical proximity effect model, the accurate optical proximity effect model and other algorithm models.
In the existing model building method for analyzing the characteristic pattern of the mask, each measured pattern is quantized into only one numerical value to participate in the building of the model, a large number of CD-SEM images are not fully utilized, and on the other hand, because a single threshold value is adopted, only the size information of the characteristic pattern in the XY plane of the silicon wafer can be obtained, but no Z-direction information exists. These problems have been present but not highlighted, but as device dimensions continue to shrink, the requirements for model accuracy have increased further. Therefore, the method for analyzing and processing the critical dimension data of the mask plate is provided, which considers the three-dimensional effect of the photoresist and can fully utilize the measured data.
Fig. 2 is a flow chart of a method for analyzing and processing critical dimension data of a mask according to an embodiment of the present application, and referring to fig. 2, the method for analyzing and processing critical dimension data of a mask according to an embodiment of the present application includes:
s201, acquiring a mask characteristic pattern.
The mask characteristic patterns not only comprise mask characteristic patterns to be trained, but also comprise mask characteristic patterns to be processed. The mask characteristic pattern to be trained is used for model training of the mask characteristic pattern, and the mask characteristic pattern to be processed is used for model training, and the mask characteristic pattern of the photoresist morphology corresponding to the mask characteristic pattern is not obtained yet.
The type, line width size and period size of the characteristic pattern of the mask plate cover the pattern range of the current level in the actual product.
Preferably, the reticle feature pattern includes both one-dimensional and two-dimensional patterns.
Preferably, the reticle feature pattern includes both dense and semi-dense patterns, and also isolated patterns.
According to the method, the mask characteristic patterns for analysis and processing are more comprehensive by receiving the mask characteristic patterns of different types, so that the mask characteristic pattern utilization rate is improved.
S202, measuring the characteristic patterns of the mask by using measuring equipment to obtain critical dimension data of the characteristic patterns of the mask under different gray scale thresholds, calculating differences among the critical dimension data of the same characteristic pattern, and analyzing the differences to determine the effective gray scale threshold of the same characteristic pattern.
Each gray level threshold has a key size data corresponding to it.
At least two gray-scale thresholds are set for the characteristic pattern of the mask plate to be trained. The gray level threshold includes a reference threshold and a plurality of common thresholds, and the number of the common thresholds may be one or three, and the specific number of the common thresholds is not limited herein.
Different thresholds can obtain different key sizes, the difference values of the key sizes are analyzed to determine whether the difference values are larger than a preset difference value, and if so, the effective gray-scale thresholds of the same feature pattern are all gray-scale thresholds of the same feature pattern; and if the effective gray level threshold value of the same feature pattern is smaller than or equal to the reference threshold value of the same feature pattern.
Preferably, the different gray-scale thresholds may be specifically set as an outer low threshold, an outer medium threshold, a high threshold, an inner medium threshold, and an inner low threshold.
In one implementation, the outer middle threshold is used as a reference threshold when measuring photoresist linewidths.
In another implementation, an inboard medium threshold is used as a reference threshold when measuring trench or via linewidths.
The method expands and screens the gray level threshold value, so that the usability of the critical dimension data is ensured while the use ratio of the critical dimension data is improved.
And S203, training the optical proximity effect model by utilizing the effective critical dimension data of the feature pattern of the mask to be trained and the photoresist morphology to be trained to obtain an accurate optical proximity effect model.
The effective critical dimension data is the critical dimension data corresponding to the effective gray level threshold.
Before training the optical proximity effect model, fitting weights of critical dimension data under different gray scale thresholds are required to be set.
The fitting weight of the critical dimension data under the reference threshold is greater than or equal to the fitting weight of the critical dimension data under the common threshold.
Before training the optical proximity effect model, initial values of position points of the photoresist morphology to be trained are set to be variable. The number of the position points of the photoresist morphology to be trained is equal to the number of the effective gray level thresholds.
After the accurate optical proximity effect model is obtained, a functional corresponding relation between the position points of the photoresist morphology to be trained and the effective gray level threshold corresponding to the photoresist morphology to be trained can be obtained.
According to the method, the input data of the optical proximity effect model is optimized, so that the learning result of the accurate optical proximity effect model obtained through training is more accurate.
S204, sending the mask pattern data to be processed to an accurate optical proximity effect model to obtain the photoresist key size and morphology corresponding to the mask pattern to be processed.
The data preprocessing server 102 module sends the mask data to be processed to the accurate optical proximity effect model on the model server 103, so as to obtain the photoresist key size and morphology corresponding to the mask pattern to be processed.
According to the method, the effective critical dimension data to be processed is learned by using the accurate optical proximity effect model, so that the learning result is more accurate, and the fitting precision can be effectively improved.
Fig. 3 is a signaling interaction schematic diagram of a method for analyzing and processing critical dimension data of a mask according to an embodiment of the present application, and referring to fig. 3, with reference to fig. 2, the method for analyzing and processing critical dimension data of a mask according to an embodiment of the present application includes the following steps:
s301, the CD-SEM measuring equipment 101 receives mask characteristic patterns uploaded by workers.
S302, the CD-SEM measuring equipment 101 measures the mask pattern to obtain critical dimension data of the mask pattern under different gray-scale thresholds.
S303, the CD-SEM metrology tool 101 sends CD data to the data preprocessing server 102.
S304, the data preprocessing server 102 calculates the difference value between the key size data of the same feature pattern.
S305, the data preprocessing server 102 analyzes the difference value to obtain an effective gray level threshold value of the same feature pattern.
S306, the data preprocessing server 102 sends the effective key size corresponding to the effective gray level threshold of the feature pattern of the mask to be trained to the model server 103.
S307, training the optical proximity effect model by the model server 103 to obtain an accurate optical proximity effect model.
S308, the data preprocessing server 102 sends the effective key size corresponding to the effective gray level threshold of the feature pattern of the mask to be processed to the model server 103.
S309, the model server 103 learns by using the accurate optical proximity effect model to obtain the photoresist morphology.
Fig. 4 is a block diagram of a reticle critical dimension data analysis processing apparatus according to an embodiment of the present application, and for convenience of explanation, only a portion related to the embodiment of the present application is shown. Referring to fig. 4, a reticle critical dimension data analysis processing apparatus provided in an embodiment of the present application includes: an acquisition module 401, a measurement module 402, a training module 403, and a learning module 404.
An acquisition module 401, configured to acquire a feature pattern of a mask;
the measurement module 402 is configured to measure the feature pattern of the mask by using measurement equipment, obtain critical dimension data of the feature pattern of the mask under different gray-scale thresholds, calculate a difference value between the critical dimension data of the same feature pattern, and then analyze the difference value to determine an effective gray-scale threshold of the same feature pattern;
the training module 403 is configured to train the optical proximity effect model by using the effective critical dimension data of the feature pattern of the mask to be trained and the photoresist morphology to be trained, so as to obtain an accurate optical proximity effect model;
and the learning module 404 is configured to send the effective critical dimension data of the feature pattern of the mask to be processed to the accurate optical proximity effect model, so as to obtain a photoresist morphology corresponding to the feature pattern of the mask to be processed.
According to the mask key size data analysis processing device, the acquired mask characteristic patterns are measured by using the measuring equipment, key size data of the mask characteristic patterns under different gray scale thresholds are obtained, and after difference values among the key size data of the same characteristic pattern are calculated, effective gray scale thresholds of the same characteristic pattern are determined; training the optical proximity effect model by using the effective critical dimension data of the feature pattern of the mask to be trained and the acquired photoresist morphology to be trained to obtain an accurate optical proximity effect model; and finally, transmitting the effective critical dimension data of the feature pattern of the mask to be processed to the model to obtain the photoresist morphology corresponding to the feature pattern of the mask to be processed, so that the fitting precision of the OPC model can be effectively improved, and the problem that the OPC model data is not fully utilized is solved.
Fig. 5 is a schematic structural diagram of an electronic device provided in an embodiment of the present application, and referring to fig. 5, the electronic device includes: memory 501, processor 502 and computer program; wherein the computer program is stored in the memory 501 and configured to perform the steps of fig. 2, 3 by the processor 502. The processor 502 is used to implement the modules of fig. 4.
Wherein the memory 501 and the processor 502 are connected by a bus 503.
The relevant descriptions and effects corresponding to the steps in the embodiments corresponding to fig. 2 to fig. 4 may be understood correspondingly, and are not repeated here.
Embodiments of the present application also provide a computer-readable storage medium comprising computer code which, when run on a computer, causes the computer to perform a method as provided by any of the implementations corresponding to fig. 2 to 5.
Embodiments of the present application also provide a computer program product comprising program code which, when the computer runs the computer program product, performs the method provided by any of the implementations corresponding to fig. 2 to 5.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A method for analyzing and processing critical dimension data of a mask plate is characterized by comprising the following steps:
acquiring mask characteristic patterns, wherein the mask characteristic patterns comprise mask characteristic patterns to be trained and mask characteristic patterns to be processed;
measuring the mask characteristic graph by using measuring equipment to obtain critical dimension data of the mask characteristic graph under different gray scale thresholds, and analyzing the difference value after calculating the difference value between the critical dimension data of the same characteristic graph to determine the effective gray scale threshold of the same characteristic graph;
training the optical proximity effect model by utilizing the effective critical dimension data of the mask characteristic pattern to be trained and the photoresist morphology to be trained to obtain an accurate optical proximity effect model, wherein the effective critical dimension data is the critical dimension data corresponding to an effective gray level threshold value;
and sending the mask pattern data to be processed to the accurate optical proximity effect model to obtain the photoresist key size and morphology corresponding to the mask pattern to be processed.
2. The method of claim 1, comprising, prior to said measuring the reticle feature pattern to be trained using a metrology device:
and setting at least two gray-scale thresholds for the characteristic pattern of the mask to be trained, wherein the gray-scale thresholds comprise a reference threshold and at least one common threshold.
3. The method of claim 1 or 2, wherein analyzing the critical dimension differences resulting from the different gray scale thresholds to determine the effective gray scale threshold for the same feature pattern comprises:
determining whether the difference is greater than a preset difference;
if the effective gray level threshold value of the same feature pattern is larger than the effective gray level threshold value of the same feature pattern, the effective gray level threshold value of the same feature pattern is all gray level threshold values of the same feature pattern;
and if the effective gray level threshold value of the same feature pattern is smaller than or equal to the reference threshold value of the same feature pattern.
4. The method of claim 1 or 2, further comprising, prior to training the optical proximity model using the valid critical dimension data of the reticle feature pattern to be trained and the photoresist profile to be trained:
setting fitting weights of the critical dimension data under different gray scale thresholds, wherein the fitting weights of the critical dimension data under the reference threshold are larger than or equal to the fitting weights of the critical dimension data under the common threshold.
5. The method of claim 4, further comprising, prior to training the optical proximity model using the valid critical dimension data of the reticle feature pattern to be trained and the photoresist topography to be trained:
and setting the initial value of the vertical position point of the photoresist morphology to be trained to be variable.
6. The method of claim 5, wherein the number of vertical position points of the photoresist profile to be trained is equal to the number of effective gray level thresholds.
7. The method according to claim 1 or 2, further comprising, after obtaining the accurate optical proximity effect model:
and obtaining a functional corresponding relation between the vertical position points of the photoresist morphology to be trained and the effective gray level threshold corresponding to the photoresist morphology to be trained.
8. A reticle critical dimension data analysis processing apparatus comprising:
the acquisition module is used for acquiring and acquiring mask characteristic patterns, wherein the mask characteristic patterns comprise mask characteristic patterns to be trained and mask characteristic patterns to be processed;
the measuring module is used for measuring the mask characteristic patterns by using measuring equipment to obtain key size data of the mask characteristic patterns under different gray level thresholds, and analyzing the difference value after calculating the difference value between the key size data of the same characteristic pattern to determine the effective gray level threshold of the same characteristic pattern;
the training module is used for training the optical proximity effect model by utilizing the effective critical dimension data of the mask characteristic pattern to be trained and the photoresist morphology to be trained to obtain an accurate optical proximity effect model, wherein the effective critical dimension data is the critical dimension data corresponding to an effective gray level threshold value;
and the learning module is used for sending the effective critical dimension data of the mask characteristic pattern to be processed to the accurate optical proximity effect model to obtain the photoresist morphology corresponding to the mask characteristic pattern to be processed.
9. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1-7.
10. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the reticle critical dimension data analysis processing method according to any of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117372567A (en) * | 2023-12-06 | 2024-01-09 | 华芯程(杭州)科技有限公司 | Layout generation method, device, equipment and medium |
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| CN117372567A (en) * | 2023-12-06 | 2024-01-09 | 华芯程(杭州)科技有限公司 | Layout generation method, device, equipment and medium |
| CN117372567B (en) * | 2023-12-06 | 2024-02-23 | 华芯程(杭州)科技有限公司 | Layout generation method, device, equipment and medium |
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