CN116339081B - Modeling method, device, equipment and medium of optical proximity correction model - Google Patents
Modeling method, device, equipment and medium of optical proximity correction model Download PDFInfo
- Publication number
- CN116339081B CN116339081B CN202310587451.9A CN202310587451A CN116339081B CN 116339081 B CN116339081 B CN 116339081B CN 202310587451 A CN202310587451 A CN 202310587451A CN 116339081 B CN116339081 B CN 116339081B
- Authority
- CN
- China
- Prior art keywords
- optical proximity
- proximity correction
- correction model
- cost
- spec
- 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
Links
- 238000012937 correction Methods 0.000 title claims abstract description 189
- 230000003287 optical effect Effects 0.000 title claims abstract description 189
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000012549 training Methods 0.000 claims abstract description 26
- 238000005457 optimization Methods 0.000 claims abstract description 17
- 238000004088 simulation Methods 0.000 claims abstract description 17
- 230000006870 function Effects 0.000 claims description 47
- 230000003068 static effect Effects 0.000 claims description 43
- 238000004590 computer program Methods 0.000 claims description 17
- 238000003860 storage Methods 0.000 claims description 11
- NGVDGCNFYWLIFO-UHFFFAOYSA-N pyridoxal 5'-phosphate Chemical compound CC1=NC=C(COP(O)(O)=O)C(C=O)=C1O NGVDGCNFYWLIFO-UHFFFAOYSA-N 0.000 claims description 8
- 238000005259 measurement Methods 0.000 claims description 6
- 230000000875 corresponding effect Effects 0.000 description 27
- 230000007547 defect Effects 0.000 description 6
- 238000001259 photo etching Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000001459 lithography Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 230000001276 controlling effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
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
- G03F7/70425—Imaging strategies, e.g. for increasing throughput or resolution, printing product fields larger than the image field or compensating lithography- or non-lithography errors, e.g. proximity correction, mix-and-match, stitching or double patterning
- G03F7/70433—Layout for increasing efficiency or for compensating imaging errors, e.g. layout of exposure fields for reducing focus errors; Use of mask features for increasing efficiency or for compensating imaging errors
- G03F7/70441—Optical proximity correction [OPC]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Software Systems (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Exposure And Positioning Against Photoresist Photosensitive Materials (AREA)
Abstract
The application provides a modeling method, a device, equipment and a medium of an optical proximity correction model, which comprise the following steps: acquiring a wafer layout data set and an initial optical proximity correction model; setting a desired cost in the initial optical proximity correction model; inputting the wafer layout data set into the initial optical proximity correction model for optimization training to generate an intermediate optical proximity correction model; and generating a target optical proximity correction model according to a comparison result of the expected cost in the intermediate optical proximity correction model and a preset standard threshold value. By the modeling method, the modeling device, the modeling equipment and the modeling medium of the optical proximity correction model, disclosed by the application, the simulation accuracy of the optical proximity correction model can be improved.
Description
Technical Field
The present application relates to the field of semiconductor technologies, and in particular, to a method, an apparatus, a device, and a medium for modeling an optical proximity correction model.
Background
In the modeling process of the optical proximity correction (Optical Proximity Correction, OPC) model, a plurality of groups of wafer layout data are required to be input into a modeling program for training, a cost function representing the fitting degree is set in the modeling program, and the OPC model can be obtained through repeated iterative computation, so that the OPC model can be used for replacing a photoetching machine to carry out a simulation experiment. As the pattern types of the wafer layout data are increasingly rich, the fitting degrees of the patterns of the wafer layout data of different types are also different, so that the simulation accuracy of the conventional OPC model is reduced.
Disclosure of Invention
In view of the above-described drawbacks of the prior art, an object of the present application is to provide a modeling method of an optical proximity correction model capable of improving the simulation accuracy of the optical proximity correction model.
To achieve the above and other related objects, the present application provides a modeling method of an optical proximity correction model, including:
acquiring a wafer layout data set and an initial optical proximity correction model;
setting a desired cost in the initial optical proximity correction model;
inputting the wafer layout data set into the initial optical proximity correction model for optimization training to generate an intermediate optical proximity correction model; and
and generating a target optical proximity correction model according to a comparison result of the expected cost in the intermediate optical proximity correction model and a preset standard threshold value.
In one embodiment of the present application, the wafer layout data set includes a static random access memory pattern data set, two line pattern data sets, a two-dimensional pattern data set, a thick-thin line pattern data set, and other pattern data sets.
In one embodiment of the present application, the step of setting a desired cost in the initial optical proximity correction model includes:
acquiring a cost function of the initial optical proximity correction model;
acquiring cost functions of a static random access memory graphic set, two line graphic sets, a two-dimensional graphic set and a thick and thin line graphic set in the initial optical proximity correction model;
acquiring corresponding expected graph cost according to cost functions of the static random access memory graph set, the two line graph sets, the two-dimensional graph set and the thick and thin line graph set; and
and acquiring the expected cost according to all the expected cost of the graph.
In one embodiment of the present application, the cost function RMS_all of the initial optical proximity correction model is expressed asWherein W is i Weights expressed as critical dimensions of different wafer layout patterns, CD i (simulation) CD, expressed as a measure of critical dimensions of a simulated wafer layout pattern i (measurement) Represented as a measure of the critical dimension of the actual wafer layout pattern.
In one embodiment of the present application, cost functions of the static random access memory pattern set, the two line pattern sets, the two-dimensional pattern set, and the thick and thin line pattern set are respectively represented as rms_sram, rms_2bar, rms_ D, RMS _plp, and pattern expected costs of the static random access memory pattern set, the two line pattern sets, the two-dimensional pattern set, the thick and thin line pattern set, and the wafer layout data set are respectively represented as sram_spec, 2bar_spec, 2d_spec, plp_spec, rms_all_spec, wherein 0.5rms_sram < = sram_spec < = 0.75rms_sram,
0.5RMS_2Bar<=2Bar_spec<=0.75RMS_2Bar，0.5RMS_2D<=2D_spec<=0.75RMS_2D，0.5RMS_PLP<=PLP_spec<=0.75RMS_PLP，RMS_all_spec=RMS_all。
in one embodiment of the present application, the desired cost_function is expressed as: cost_function=0.8×rms_all/rms_all_spec+0.2×rms_sram/sram_spec+rms_2 Bar/2bar_spec+rms_2d/2d_spec+rms_plp/plp_spec.
In one embodiment of the present application, the step of generating the target optical proximity correction model according to a comparison result of the expected cost in the intermediate optical proximity correction model and a preset standard threshold value includes:
acquiring a desired cost of the intermediate optical proximity correction model;
judging whether the expected cost is smaller than a preset standard threshold value or not;
if the target optical proximity correction model is smaller than the preset standard threshold, the intermediate optical proximity correction model is indicated to be optimized, and an optimized target optical proximity correction model is obtained;
and if the expected cost of the corresponding graph in the intermediate optical proximity correction model is greater than or equal to the preset standard threshold, repeatedly adjusting the expected cost value of the corresponding graph in the intermediate optical proximity correction model, and repeatedly optimizing and training the expected cost until the expected cost of the generated intermediate optical proximity correction model is less than the preset standard threshold, thereby obtaining an optimized target optical proximity correction model.
The application also provides a modeling device of the optical proximity correction model, which comprises:
the acquisition module is used for acquiring a wafer layout data set and an initial optical proximity correction model;
a setting module for setting a desired cost in the initial optical proximity correction model;
the training module is used for inputting the wafer layout data set into the initial optical proximity correction model for optimization training so as to generate an intermediate optical proximity correction model; and
and the generation module is used for generating a target optical proximity correction model according to a comparison result of the expected cost in the intermediate optical proximity correction model and a preset standard threshold value.
The application also provides a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method of modeling an optical proximity correction model when executing the computer program.
The present application also provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of a modeling method of an optical proximity correction model.
As described above, the modeling method of the optical proximity correction model provided by the application is used for setting expected cost in the initial optical proximity correction model in the modeling process of the initial optical proximity correction model, and has the unexpected effect that the fitting degree of different patterns and different sizes is influenced by the expected cost of the patterns, so that the fitting degree of the different patterns is controllable to a certain extent, the prediction accuracy of the trained target optical proximity correction model on the critical size is improved, the target optical proximity correction accuracy can be effectively improved, the accuracy of the critical size is greatly improved, and the product yield is further ensured.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a modeling method of an optical proximity correction model according to the present application;
FIG. 2 is a flowchart of step S20 in FIG. 1;
FIG. 3 is a flowchart showing step S40 in FIG. 1;
FIG. 4 is a schematic diagram showing different wafer layouts in a modeling method of an optical proximity correction model according to the present application;
FIG. 5 is a graph showing the comparison of fitting degrees in a modeling method of an optical proximity correction model according to the present application;
FIG. 6 is a graph showing the comparison of the fitting results of the static random access memory pattern in the modeling method of the optical proximity correction model according to the present application;
FIG. 7 is a schematic diagram of a modeling apparatus for an optical proximity correction model according to the present application;
FIG. 8 is a schematic diagram of a computer device of the present application;
fig. 9 shows a schematic diagram of another computer device of the present application.
Description of element numbers: 10. static random access memory graphics data; 20. two line graphic data; 30. two-dimensional graphic data; 40. thick and thin line graph data; 100. an acquisition module; 200. setting a module; 300. a training module; 400. and generating a module.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, the present application discloses a modeling method of an optical proximity correction model, which can be applied to a modeling process of the optical proximity correction model. The optical proximity correction model can be used for replacing a photoetching machine to carry out simulation experiments, and different wafer layout data can be input into the optical proximity correction model to generate corresponding simulation data. The closer the simulation data is to the corresponding wafer layout data, the better the fitting degree of the optical proximity correction model is, and the closer the optical proximity correction model is to the real photoetching machine. Whether the corresponding wafer layout data can achieve the corresponding effect in the photoetching machine or not can be judged through the simulation data. The modeling method of the optical proximity correction model disclosed by the application can optimize the existing optical proximity correction model, so that the optimized optical proximity correction model can simulate the patterns of different types of wafer layout data, and the simulation accuracy is higher. The modeling method of the optical proximity correction model disclosed by the application can comprise the following steps:
s10, acquiring a wafer layout data set and an initial optical proximity correction model, wherein the wafer layout data set comprises a static random access memory graphic data set, two line graphic data sets, a two-dimensional graphic data set, a thick and thin line graphic data set and other graphic data sets;
step S20, setting expected cost in an initial optical proximity correction model;
s30, inputting a wafer layout data set into an initial optical proximity correction model for optimization training so as to generate an intermediate optical proximity correction model;
and S40, generating an optimized target optical proximity correction model according to a comparison result of the expected cost in the intermediate optical proximity correction model and a preset standard threshold.
Referring to fig. 1 and 4, in one embodiment of the present application, when step S10 is performed, a wafer layout data set and an initial optical proximity correction model are obtained, wherein the wafer layout data set includes a static random access memory pattern data set, two line pattern data sets, a two-dimensional pattern data set, a thick-thin line pattern data set, and other pattern data sets. Specifically, the initial optical proximity correction model may be an existing optical proximity correction model that is initially trained, and the initial optical proximity correction model may be retrained by using the wafer layout data set, so that the target optical proximity correction model obtained by training may satisfy different requirements. The wafer layout data set may include a static random access memory pattern data set, two line pattern data sets, a two-dimensional pattern data set, a thick-thin line pattern data set, and other pattern data sets. Wherein the Static Random Access Memory (SRAM) graphic data set may include a plurality of SRAM graphic data 10, the two line graphic data set may include a plurality of two line (2 Bar) graphic data 20, the two-dimensional graphic data set may include a plurality of two-dimensional (2D) graphic data 30, and the thick-thin line graphic data set may include a plurality of thick-thin line (PLP) graphic data 40.
Referring to FIG. 4, in one embodiment of the present application, for the SRAM graphic data 10, since the SRAM graphic data 10 may include a plurality of graphic units, and the distance pitch between centers of two adjacent graphic units is smaller, such as the distance X between centers of two adjacent graphic units 1 、X 2 、X 3 、Y 1 、Y 2 、Y 3 Smaller, and therefore the sram graphics data 10 belongs to critical dimensions (Critical Dimension, CD), a higher degree of fit is required. For the two line pattern data 20, since the two line pattern data 20 may be a pattern composed of two parallel lines, the critical dimension of the two line pattern data 20 is easy to cause peeling defect (peeling defect) at a smaller time, so a higher fitting degree is required, and the critical dimension may be properly diverged at a larger time. For the two-dimensional graphic data 30, since the critical dimension of the two-dimensional graphic data 30 is liable to cause a broken line defect (bridge defect) at a small time, a high fitting degree is required, and the critical dimension thereof can be properly diverged at a large time. For the thick-thin line pattern data 40, it may include two thick lines and one thin line, where the thin line is located between the two thick lines, and since the critical dimension of the thick-thin line pattern data 40 is easy to cause pinch defect (pin defect) at a smaller time, a higher fitting degree is required, and the critical dimension may be properly diverged at a larger time. Wherein the critical dimension is in the integrated circuitIn the manufacture of the photomask and the photoetching process, a special line pattern reflecting the width of the characteristic line of the integrated circuit is designed for evaluating and controlling the pattern processing precision of the process.
Referring to fig. 2, when step S20 is performed, a desired cost is set in the initial optical proximity correction model. Specifically, step S20 may include the following steps:
step S21, obtaining a cost function RMS_all of the initial optical proximity correction model, expressed asWherein W is i Weights expressed as critical dimensions of different wafer layout patterns, CD i (simulation) CD, expressed as a measure of critical dimensions of a simulated wafer layout pattern i (measurement) A measure of critical dimensions expressed as actual wafer layout patterns;
step S22, obtaining cost functions of a static random access memory pattern set, two line pattern sets, a two-dimensional pattern set and a thick and thin line pattern set in an initial optical proximity correction model, wherein the cost functions are respectively expressed as RMS_SRAM, RMS_2Bar and RMS_ D, RMS _PLP;
step S23, corresponding graph expected cost is obtained according to the cost function of the static random access memory graph set, the two line graph sets, the two-dimensional graph set and the thick and thin line graph set, and the graph expected cost is respectively expressed as the graph expected cost SRAM_spec of the static random access memory graph set, the graph expected cost 2Bar_spec of the two line graph sets, the graph expected cost 2D_spec of the two-dimensional graph set, the graph expected cost PLP_spec of the thick and thin line graph set and the graph expected cost RMS_all_spec of the wafer layout data set;
step S24, according to the expected Cost of the graph, acquiring an expected cost_function, which is expressed as: cost_function=0.8×rms_all/rms_all_spec+0.2×rms_sram/sram_spec+rms_2 Bar/2bar_spec+rms_2d/2d_spec+rms_plp/plp_spec.
In one embodiment of the present application, when step S21 is performed, specifically, a cost function rms_all of the initial optical proximity correction model may be obtained first, expressed asWherein W is i Weights expressed as critical dimensions of different wafer layout patterns, CD i (simulation) CD, expressed as a measure of critical dimensions of a simulated wafer layout pattern i (measurement) Represented as a measure of the critical dimension of the actual wafer layout pattern.
In one embodiment of the present application, when step S22 is performed, in particular, the wafer layout data set may include a static random access memory pattern data set, two line pattern data sets, a two-dimensional pattern data set, a thick-thin line pattern data set, and other pattern data sets. For four types of graphics, namely a static random access memory graphic data set, two line graphic data sets, a two-dimensional graphic data set and a thick and thin line graphic data set, the initial optical proximity correction model has poor fitting degree, so that corresponding cost functions are required to be adjusted to improve corresponding fitting degree. The cost function of the static random access memory pattern set, the cost function of the two line pattern sets, the cost function of the two-dimensional pattern set, and the cost function of the thick and thin line pattern data set in the initial optical proximity correction model can be obtained and are respectively expressed as RMS_SRAM, RMS_2Bar and RMS_ D, RMS _PLP. Wherein RMS is expressed as root mean square.
In one embodiment of the present application, when step S23 and step S24 are executed, specifically, after the cost function of the sram pattern set, the cost function of the two line pattern sets, the cost function of the two-dimensional pattern set, and the cost function of the thick-thin line pattern data set in the initial optical proximity correction model are obtained, the cost function of the two-dimensional pattern set, and the cost function of the thick-thin line pattern data set may be adjusted accordingly, so as to obtain the corresponding expected cost of the pattern. For example, the expected cost of the corresponding static random access memory pattern set may be obtained from the cost function of the static random access memory pattern set, 0.5rms_sram < = sram_spec < = 0.75rms_sram. The expected graph cost of the two corresponding line graph sets can be obtained according to the cost function of the two line graph sets, wherein 0.5RMS_2Bar < = 2Bar_spec < = 0.75RMS_2Bar. The expected graph cost of the corresponding two-dimensional graph set can be obtained according to the cost function of the two-dimensional graph set, and 0.5rms_2d < = 2d_spec < = 0.75rms_2d. The expected graph cost of the corresponding thick-thin line graph dataset can be obtained according to the cost function of the thick-thin line graph dataset, and 0.5rms_plp < = plp_spec < = 0.75rms_plp. The expected graph cost of the corresponding wafer layout dataset may be obtained according to a cost function of the wafer layout dataset, rms_all_spec=rms_all. After the expected Cost of the static random access memory graphics set, the expected Cost of the two line graphics sets, the expected Cost of the two-dimensional graphics set, the expected Cost of the thick and thin line graphics data set and the expected Cost of the wafer layout data set are obtained, the expected Cost of the static random access memory graphics set, the expected Cost of the two line graphics sets, the expected Cost of the thick and thin line graphics data set and the expected Cost of the wafer layout data set can be integrated to obtain corresponding expected Cost cost_function, which is expressed as: cost_function=0.8×rms_all/rms_all_spec+0.2×rms_sram/sram_spec+rms_2 Bar/2bar_spec+rms_2d/2d_spec+rms_plp/plp_spec.
When step S30 is executed, specifically, the expected graphics cost of the sram graphics set, the expected graphics cost of the two line graphics sets, the expected graphics cost of the two-dimensional graphics set, and the expected graphics cost of the thick-thin line graphics data set are all within a preset range, so that the values of the two line graphics sets and the two-dimensional graphics sets can be respectively adjusted accordingly to meet the training requirement. After the specific values of the graphic expected cost of the set static random access memory graphic set, the graphic expected cost of the two line graphic sets, the graphic expected cost of the two-dimensional graphic set and the graphic expected cost of the thick and thin line graphic data set are set, the wafer layout data set can be input into an initial optical proximity correction model for iterative optimization training, and can be trained for N times in an iterative manner to generate a corresponding intermediate optical proximity correction model.
Referring to fig. 3, when step S40 is performed, an optimized target optical proximity correction model is generated according to a comparison result between the expected cost in the intermediate optical proximity correction model and a preset standard threshold. Specifically, step S40 may include the following steps:
step S41, acquiring expected cost of an intermediate optical proximity correction model;
step S42, judging whether the expected cost is smaller than a preset standard threshold value;
step S43, if the target optical proximity correction model is smaller than the preset standard threshold, the target optical proximity correction model is obtained after the intermediate optical proximity correction model is optimized;
and S44, if the expected cost of the corresponding graph in the intermediate optical proximity correction model is greater than or equal to the preset standard threshold, the intermediate optical proximity correction model is not optimized, the expected cost of the corresponding graph in the intermediate optical proximity correction model is repeatedly adjusted, and the optimization training is repeated until the expected cost of the generated intermediate optical proximity correction model is less than the preset standard threshold, and the optimized target optical proximity correction model is obtained at the moment.
In one embodiment of the present application, when step S40 is performed, that is, after generating the intermediate optical proximity correction model after a round of iterative optimization training, a corresponding desired Cost cost_function may be obtained. At this time, it is necessary to determine whether the expected cost is less than a preset standard threshold. If the expected cost is smaller than the preset standard threshold, the fitting degree of the intermediate optical proximity correction model to different wafer layout figures is higher, and the intermediate optical proximity correction model is optimized, and the optimized target optical proximity correction model is obtained. If the expected cost is greater than or equal to the preset standard threshold, the intermediate optical proximity correction model is indicated to have lower fitting degree on different wafer layout graphs, and the intermediate optical proximity correction model is not optimized at this time, and the value of the expected cost of the corresponding graph in the intermediate optical proximity correction model needs to be repeatedly adjusted, and repeated optimization training is performed on the intermediate optical proximity correction model until the expected cost of the generated intermediate optical proximity correction model is smaller than the preset standard threshold, and at this time, the optimized target optical proximity correction model is obtained.
Referring to fig. 5 and 6, in one embodiment of the present application, after the optimized target optical proximity correction model is obtained, simulation may be performed on different Static Random Access Memory (SRAM) patterns, two line (2 Bar) patterns, two-dimensional (2D) patterns, and thick and thin line (PLP) patterns. It can be seen that, for a Static Random Access Memory (SRAM) pattern, two line (2 Bar) pattern, and two-dimensional (2D) pattern, the fitting degree of the target optical proximity correction model is significantly lower than that of the initial optical proximity correction model, i.e., the closer the simulation data of the target optical proximity correction model is to the corresponding wafer layout data. The target optical proximity correction model is closer to a real lithography machine than the initial optical proximity correction model. For Static Random Access Memory (SRAM) patterns, the simulation accuracy of the target optical proximity correction model is much closer to the pattern data of a real lithography machine.
Therefore, in the above scheme, for the modeling process of the initial optical proximity correction model, by setting the expected cost in the initial optical proximity correction model, the unexpected effect is that the fitting degree of different patterns and different sizes is affected by the expected cost of the patterns, so that the fitting degree of different patterns is controllable to a certain extent, the prediction accuracy of the trained target optical proximity correction model on the critical size is improved, the target optical proximity correction accuracy can be effectively improved, the accuracy of the critical size is greatly improved, and the product yield is further ensured.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.
Referring to fig. 7, the present application further provides a modeling apparatus for an optical proximity correction model, where the optimizing apparatus corresponds to the optimizing method in the above embodiment one by one. The optimizing means may comprise an acquisition module 100, a setting module 200, a training module 300 and a generating module 400.
In one embodiment of the present application, the acquisition module 100 may be configured to acquire a wafer layout data set and an initial optical proximity correction model, where the wafer layout data set includes a static random access memory pattern data set, two line pattern data sets, a two-dimensional pattern data set, a thick-thin line pattern data set, and other pattern data sets. The static random access memory graphic data set may include a plurality of static random access memory graphic data 10, the two line graphic data sets may include a plurality of two line graphic data 20, the two-dimensional graphic data set may include a plurality of two-dimensional graphic data 30, and the thick-thin line graphic data set may include a plurality of thick-thin line graphic data 40.
In one embodiment of the application, the setup module 200 may be used to set up the desired cost in the initial optical proximity correction model. The setup module 200 may be specifically configured to obtain a cost function rms_all of the initial optical proximity correction model, denoted asWherein W is i Weights expressed as critical dimensions of different wafer layout patterns, CD i (simulation) CD, expressed as a measure of critical dimensions of a simulated wafer layout pattern i (measurement) Represented as a measure of the critical dimension of the actual wafer layout pattern. The setting module 200 may obtain a cost function of a static random access memory pattern set, a cost function of two line pattern sets, a cost function of a two-dimensional pattern set, and a cost function of a thick-thin line pattern data set in the initial optical proximity correction model, which are respectively denoted as rms_sram, rms_2bar, and rms_ D, RMS _plp. The setting module 200 obtains corresponding expected graph cost according to the cost function of the static random access memory graph set, the cost function of the two line graph sets, the cost function of the two-dimensional graph set and the cost function of the thick and thin line graph data set, wherein the expected graph cost is respectively expressed as expected graph cost SRAM_spec of the static random access memory graph set, expected graph cost 2Bar_spec of the two line graph sets, expected graph cost 2D_spec of the two-dimensional graph set, expected graph cost PLP_spec of the thick and thin line graph set and expected graph cost RMS_all_spec of the wafer layout data set. The setting module 200 may obtain the desired Cost cost_function according to the graphic desired Cost, expressed as: cost_function=0.8×rms_all/rms_all_spec+0.2×rms_sram/sram_spec+rms_2 Bar/2bar_spec+rms_2d/2d_spec+rms_plp/plp_spec.
In one embodiment of the application, the training module 300 may be used to input the wafer layout dataset into an initial optical proximity correction model for optimization training to generate an intermediate optical proximity correction model. Specifically, the expected graph cost of the static random access memory graph set, the expected graph cost of the two line graph sets, the expected graph cost of the two-dimensional graph set and the expected graph cost of the thick and thin line graph data set are all within a preset range, so that the values of the two line graph sets and the thin line graph data set can be respectively and correspondingly adjusted to meet the training requirement. After the specific values of the graphic expected cost of the set static random access memory graphic set, the graphic expected cost of the two line graphic sets, the graphic expected cost of the two-dimensional graphic set and the graphic expected cost of the thick and thin line graphic data set are set, the wafer layout data set can be input into an initial optical proximity correction model for iterative optimization training, and can be trained for N times in an iterative manner to generate a corresponding intermediate optical proximity correction model.
In one embodiment of the application, the generation module 400 may be configured to generate an optimized target optical proximity correction model based on a comparison of the expected cost within the intermediate optical proximity correction model to a preset standard threshold. The generating module 400 may be specifically configured to obtain the expected cost of the intermediate optical proximity correction model, determine whether the expected cost is smaller than a preset standard threshold, if so, indicate that the intermediate optical proximity correction model is optimized, and obtain an optimized target optical proximity correction model, and if so, indicate that the intermediate optical proximity correction model is not optimized, repeatedly adjust the value of the expected cost of the corresponding graph in the intermediate optical proximity correction model, and repeatedly perform optimization training until the expected cost of the generated intermediate optical proximity correction model is smaller than the preset standard threshold, and obtain the optimized target optical proximity correction model at this time.
According to the modeling device of the optical proximity correction model, aiming at the modeling process of the initial optical proximity correction model, through setting the expected cost in the initial optical proximity correction model, the unexpected effect is that the fitting degree of different patterns and different sizes is influenced by the expected cost of the patterns, so that the fitting degree of the different patterns is controllable to a certain extent, the prediction accuracy of the trained target optical proximity correction model on the critical size is improved, the target optical proximity correction accuracy can be effectively improved, the accuracy of the critical size is greatly improved, and the product yield is further ensured.
For specific limitations of the optimization means, reference may be made to the limitations of the optimization method hereinabove, and no further description is given here. The respective modules in the above-described optimizing apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in hardware or independent of an optimizer in computer equipment, and can also be stored in a memory in the computer equipment in a software mode, so that the optimizer can call and execute the operations corresponding to the modules.
Referring to fig. 8, the present application further provides a computer device. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes non-volatile and/or volatile storage media and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is for communicating with an external client via a network connection. The computer program is executed by a processor to implement the functions or steps of a modeling method of an optical proximity correction model.
Referring to FIG. 9, the present application also provides another computer device. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is for communicating with an external server via a network connection. The computer program is executed by a processor to implement the functions or steps of a modeling method of an optical proximity correction model.
In one embodiment of the application, a computer device is provided comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring a wafer layout data set and an initial optical proximity correction model, wherein the wafer layout data set comprises a static random access memory graphic data set, two line graphic data sets, a two-dimensional graphic data set, a thick and thin line graphic data set and other graphic data sets;
setting a desired cost in the initial optical proximity correction model;
inputting the wafer layout data set into an initial optical proximity correction model for optimization training to generate an intermediate optical proximity correction model;
and generating an optimized target optical proximity correction model according to a comparison result of the expected cost in the intermediate optical proximity correction model and a preset standard threshold value.
In one embodiment of the present application, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring a wafer layout data set and an initial optical proximity correction model, wherein the wafer layout data set comprises a static random access memory graphic data set, two line graphic data sets, a two-dimensional graphic data set, a thick and thin line graphic data set and other graphic data sets;
setting a desired cost in the initial optical proximity correction model;
inputting the wafer layout data set into an initial optical proximity correction model for optimization training to generate an intermediate optical proximity correction model;
and generating an optimized target optical proximity correction model according to a comparison result of the expected cost in the intermediate optical proximity correction model and a preset standard threshold value.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program, which may be stored on a non-transitory computer readable storage medium and which, when executed, may comprise the steps of the above-described embodiments of the methods. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
In the description of the present specification, the descriptions of the terms "present embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The embodiments of the application disclosed above are intended only to help illustrate the application. The examples are not intended to be exhaustive or to limit the application to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the application and the practical application, to thereby enable others skilled in the art to best understand and utilize the application. The application is limited only by the claims and the full scope and equivalents thereof.
Claims (9)
1. A method of modeling an optical proximity correction model, comprising:
acquiring a wafer layout data set and an initial optical proximity correction model;
acquiring a cost function of the initial optical proximity correction model;
acquiring cost functions of a static random access memory graphic set, two line graphic sets, a two-dimensional graphic set and a thick and thin line graphic set in the initial optical proximity correction model;
acquiring corresponding expected graph cost according to cost functions of the static random access memory graph set, the two line graph sets, the two-dimensional graph set and the thick and thin line graph set;
acquiring expected cost according to all the expected cost of the graph;
inputting the wafer layout data set into the initial optical proximity correction model for optimization training to generate an intermediate optical proximity correction model; and
and generating a target optical proximity correction model according to a comparison result of the expected cost in the intermediate optical proximity correction model and a preset standard threshold value.
2. The method of modeling an optical proximity correction model of claim 1, wherein the wafer layout dataset comprises a static random access memory pattern dataset, two line pattern datasets, a two-dimensional pattern dataset, a thick-thin line pattern dataset.
3. Modeling method of an optical proximity correction model according to claim 1, characterized in that the cost function rms_all of the initial optical proximity correction model is expressed asWherein,,
W i expressed as weights for critical dimensions of different wafer layout patterns,
CD i (simulation) Represented as a measurement of critical dimensions of a simulated wafer layout pattern,
CD i (measurement) Represented as a measure of the critical dimension of the actual wafer layout pattern.
4. The modeling method of an optical proximity correction model according to claim 1, wherein cost functions of the static random access memory pattern set, the two line pattern sets, the two-dimensional pattern set, and the thick and thin line pattern set are represented as rms_sram, rms_2Bar, rms_ D, RMS _plp, respectively, pattern expected costs of the static random access memory pattern set, the two line pattern sets, the two-dimensional pattern set, the thick and thin line pattern set, and the wafer layout data set are represented as sram_spec, 2bar_spec, 2d_spec, plp_spec, rms_all_spec, respectively, wherein sram_sram < = sram_spec < = 0.75rms_spec,
0.5RMS_2Bar<＝2Bar_spec<＝0.75RMS_2Bar，
0.5RMS_2D<＝2D_spec<＝0.75RMS_2D，
0.5RMS_PLP<＝PLP_spec<＝0.75RMS_PLP，
RMS_all_spec＝RMS_all。
5. the method of modeling an optical proximity correction model according to claim 1, characterized in that the desired cost_function is expressed as: cost_function=0.8×rms_all/rms_all_spec+0.2×rms_all_spec
(RMS_SRAM/SRAM_spec+RMS_2Bar/2Bar_spec+RMS_2D/2D_spec+RMS_PL P/PLP_spec)。
6. The method of modeling an optical proximity correction model according to claim 1, wherein the step of generating a target optical proximity correction model based on a comparison of expected costs within the intermediate optical proximity correction model and a preset standard threshold value comprises:
acquiring a desired cost of the intermediate optical proximity correction model;
judging whether the expected cost is smaller than a preset standard threshold value or not;
if the target optical proximity correction model is smaller than the preset standard threshold, the intermediate optical proximity correction model is indicated to be optimized, and an optimized target optical proximity correction model is obtained;
and if the expected cost of the corresponding graph in the intermediate optical proximity correction model is greater than or equal to the preset standard threshold, repeatedly adjusting the expected cost value of the corresponding graph in the intermediate optical proximity correction model, and repeatedly optimizing and training the expected cost until the expected cost of the generated intermediate optical proximity correction model is less than the preset standard threshold, thereby obtaining an optimized target optical proximity correction model.
7. A modeling apparatus for an optical proximity correction model, comprising:
the acquisition module is used for acquiring a wafer layout data set and an initial optical proximity correction model;
the setting module is used for acquiring a cost function of the initial optical proximity correction model, acquiring a cost function of a static random access memory graph set, two line graph sets, a two-dimensional graph set and a thick-thin line graph set in the initial optical proximity correction model, acquiring corresponding graph expected cost according to the cost function of the static random access memory graph set, the two line graph sets, the two-dimensional graph set and the thick-thin line graph set, and acquiring expected cost according to all the graph expected cost;
the training module is used for inputting the wafer layout data set into the initial optical proximity correction model for optimization training so as to generate the intermediate optical proximity correction model; and
and the generation module is used for generating a target optical proximity correction model according to a comparison result of the expected cost in the intermediate optical proximity correction model and a preset standard threshold value.
8. Computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, realizes the steps of the method of modeling an optical proximity correction model according to any of claims 1 to 6.
9. A computer-readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the modeling method of an optical proximity correction model according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310587451.9A CN116339081B (en) | 2023-05-24 | 2023-05-24 | Modeling method, device, equipment and medium of optical proximity correction model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310587451.9A CN116339081B (en) | 2023-05-24 | 2023-05-24 | Modeling method, device, equipment and medium of optical proximity correction model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116339081A CN116339081A (en) | 2023-06-27 |
| CN116339081B true CN116339081B (en) | 2023-09-12 |
Family
ID=86882704
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310587451.9A Active CN116339081B (en) | 2023-05-24 | 2023-05-24 | Modeling method, device, equipment and medium of optical proximity correction model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116339081B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101788760A (en) * | 2009-01-23 | 2010-07-28 | 中芯国际集成电路制造(上海)有限公司 | Optimization method of optical proximity correction rule |
| CN101995763A (en) * | 2009-08-17 | 2011-03-30 | 上海宏力半导体制造有限公司 | Optical proximity correction method |
| CN111077726A (en) * | 2018-10-19 | 2020-04-28 | 三星电子株式会社 | Optical proximity correction method and method of manufacturing a photolithographic mask using the same |
| CN112099309A (en) * | 2019-06-18 | 2020-12-18 | 中芯国际集成电路制造(上海)有限公司 | Correction method of optical proximity correction model |
| CN112889005A (en) * | 2018-10-17 | 2021-06-01 | Asml荷兰有限公司 | Method for generating characteristic patterns and training machine learning models |
| CN115793380A (en) * | 2021-09-09 | 2023-03-14 | 中芯国际集成电路制造(上海)有限公司 | Optical proximity correction method |
| CN115981115A (en) * | 2023-03-21 | 2023-04-18 | 墨研计算科学（南京）有限公司 | Optical proximity correction method, apparatus, computer device and storage medium |
| CN115993754A (en) * | 2023-03-24 | 2023-04-21 | 长鑫存储技术有限公司 | Model calibration method and device, electronic equipment and computer storage medium |
-
2023
- 2023-05-24 CN CN202310587451.9A patent/CN116339081B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101788760A (en) * | 2009-01-23 | 2010-07-28 | 中芯国际集成电路制造(上海)有限公司 | Optimization method of optical proximity correction rule |
| CN101995763A (en) * | 2009-08-17 | 2011-03-30 | 上海宏力半导体制造有限公司 | Optical proximity correction method |
| CN112889005A (en) * | 2018-10-17 | 2021-06-01 | Asml荷兰有限公司 | Method for generating characteristic patterns and training machine learning models |
| CN111077726A (en) * | 2018-10-19 | 2020-04-28 | 三星电子株式会社 | Optical proximity correction method and method of manufacturing a photolithographic mask using the same |
| CN112099309A (en) * | 2019-06-18 | 2020-12-18 | 中芯国际集成电路制造(上海)有限公司 | Correction method of optical proximity correction model |
| CN115793380A (en) * | 2021-09-09 | 2023-03-14 | 中芯国际集成电路制造(上海)有限公司 | Optical proximity correction method |
| CN115981115A (en) * | 2023-03-21 | 2023-04-18 | 墨研计算科学（南京）有限公司 | Optical proximity correction method, apparatus, computer device and storage medium |
| CN115993754A (en) * | 2023-03-24 | 2023-04-21 | 长鑫存储技术有限公司 | Model calibration method and device, electronic equipment and computer storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116339081A (en) | 2023-06-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9471746B2 (en) | Sub-resolution assist feature implementation with shot optimization | |
| US11205251B2 (en) | Method of image completion | |
| US10402524B2 (en) | Prediction of process-sensitive geometries with machine learning | |
| CN113343630B (en) | Modeling method and modeling device, electronic equipment and storage medium | |
| US11379647B2 (en) | Multilayer optical proximity correction (OPC) model for OPC correction | |
| TW202121050A (en) | Applying reticle enhancement technique recipes based on failure modes predicted by an artificial neural network | |
| US20230375916A1 (en) | Inverse lithography and machine learning for mask synthesis | |
| TWI836186B (en) | Method, system, and non-transitory computer readable medium for developing lithographic masks using skeleton representation of layouts | |
| CN116819898A (en) | Mask pattern correction method and device and semiconductor device | |
| CN113779779A (en) | Method, apparatus and computer-readable storage medium for optimizing a mask | |
| CN102759862B (en) | Optical proximity correction method | |
| CN115457350A (en) | Optical proximity correction etching model training method and optical proximity correction method | |
| CN117669473B (en) | Method for model calibration, electronic device and storage medium | |
| CN106707681A (en) | Method for increasing OPC (Optical Proximity Correction) processing accuracy | |
| CN116339081B (en) | Modeling method, device, equipment and medium of optical proximity correction model | |
| CN114326288A (en) | Method for enlarging photoetching process window, electronic equipment and storage medium | |
| CN115933305B (en) | Photomask pattern correction method, device, equipment and medium | |
| CN117389114A (en) | Mask design layout blurring detection method, storage medium and pattern repairing equipment | |
| US20230136021A1 (en) | Method and system for three-dimensional modeling | |
| CN111651904B (en) | Flow field data calculation method and device based on machine learning | |
| US20150143304A1 (en) | Target Point Generation for Optical Proximity Correction | |
| CN114690540A (en) | Optical proximity effect correction method and system and mask | |
| CN115185165B (en) | Construction method of assistant model, optical correction method and device and terminal | |
| CN114898169B (en) | Deep learning-based photoetching OPC database establishment method | |
| US11797739B2 (en) | Endpoint path margin based integrated circuit design using sub-critical timing paths |
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 |