70 results about "Computational lithography" patented technology

The disclosure provides a method and system for mask feature optimization and belongs to the field of computational lithography. The method includes: acquiring a new contour after the edges of the main pattern of a mask are moved inward or outward by a predetermined distance; setting a region inside the new contour as the shadow region when the movement is an inward movement and setting a region outside the new contour as the shadow region when the movement is an outward movement; screening an acquired mask gradient field solved by inverse lithography based on the shadow region to keep only the mask gradient field inside the region; and generating a sub resolution assist / inverse feature of the mask based on the screened mask gradient field.

The invention discloses a unified multi-task calculation photoetching modeling and defect detection method assisted by a visual large model, which not only has high-precision modeling capability and defect identification performance, but also remarkably reduces the model deployment cost, and has the advantages of high automation, unified modeling, engineering expandability and the like. Comprising the following steps: constructing an image generation and defect detection dual-task data set; the method comprises the following steps of: constructing a two-stage image generation model fusing process conditions on the basis of a unified Transform architecture, and generating mask and contour images from a layout image; the method comprises the following steps: constructing a DETR-based defect detection model for three types of images of layout, mask and contour, and carrying out DRC, MRC and LRC detection; an end-to-end photoetching large model framework is constructed, and after layout is supported to be input, mask, touch and DRC / MRC / LRC detection results are output at the same time.

The invention relates to the technical field of computational lithography, in particular to a curve OPC verification method, device and equipment and a storage medium. Determining a target curve segment on the target curve graph; determining sampling points on the target curve segment, and acquiring deviation distances from the sampling points to the to-be-checked simulation curve graph along normal directions of corresponding tangent lines, and curvatures corresponding to the sampling points; according to the deviation distance and curvature of each sampling point, determining a curve EPE of the simulation curve graph to be checked on the target curve segment; and when the curve EPE exceeds the EPE threshold value, determining the to-be-checked simulation curve graph as a to-be-corrected simulation curve graph. According to the method, different weights are given to the sampling points with different curvatures according to the corresponding curvatures, so that the accuracy of curve OPC verification is greatly improved, and the calculation power consumption in the calculation process is remarkably reduced.

The invention discloses a digital mask photoresist pattern matching method and system based on optical proximity correction. The method comprises the following steps: determining a modulation coefficient of a digital mask pattern; projecting the digital mask pattern based on a Hopkinson diffraction model and a photoresist model to obtain a photoresist pattern; performing optimization processing on the modulation coefficient through a steepest descent method to obtain a modulation coefficient after preliminary optimization; and calculating an error value between the photoresist pattern and a given target pattern through an image error method, and performing iterative optimization processing on the preliminarily optimized modulation coefficient according to the error value to obtain an optimized digital mask pattern. According to the invention, the matching rate of the exposure pattern and the target pattern can be improved by optimizing the modulation coefficient of the digital mask. The digital mask photoresist pattern matching method and system based on optical proximity correction can be widely applied to the technical field of photoresist image matching.

The invention belongs to the field of computational lithography, and particularly discloses a lithography model calibration method based on a proxy model assisted hybrid genetic algorithm, which comprises the following steps: determining to-be-optimized parameters of a lithography model, and generating an initial population according to the to-be-optimized parameters; performing parallel simulation on the current population to obtain a photoetching model simulation result of the current population; performing parallel evaluation on the current population to obtain photoetching model evaluation indexes and fitness distribution of the current population; screening excellent individuals in the current population to construct an agent model, and performing optimal prediction to obtain a first type of individuals; screening excellent individuals in the current population and carrying out crossover and variation to obtain a second type of individuals; and combining the first type of individuals and the second type of individuals, carrying out parallel simulation and evaluation on the next-generation population, and carrying out loop iteration updating until a preset termination condition is met, so as to obtain a photoetching model parameter set of the optimal individual. According to the invention, the calibration process of the photoetching model can be simplified, and the automation, precision efficiency and global search capability are improved.

The invention discloses a compensation model creating method, a photoetching exposure simulation method, a photoetching exposure simulation device and photoetching exposure simulation equipment, and the compensation model creating method comprises the following steps: determining mask pattern transmission parameters corresponding to a test mask pattern according to a chip pattern sample; collecting an exposure image data set corresponding to the test mask pattern; the mask pattern transmission parameters are input into the constructed neural network model, and prediction compensation parameters are obtained; inputting the prediction compensation parameter and the mask pattern transmission parameter into the calculation photoetching model to obtain a simulation exposure image; performing error comparison on the simulation exposure image and a real exposure image in the exposure image data set to obtain a comparison error; and optimizing the neural network model according to the comparison error until the comparison error is not greater than a preset error, and obtaining a compensation neural network model. According to the invention, the training of the compensation neural network model is simple, easy to realize and high in accuracy, and facilitates the improvement of the analog simulation accuracy of the photoetching model.

Systems and methods are provided for automated generation of optical proximity correction (OPC) recipes for producing photomasks for patterning semiconductor wafers, thereby improving the overall efficiency and effectiveness of computational lithography in modern semiconductor manufacturing. According to at least one embodiment, an OPC recipe is generated by a two-stage process that includes a reinforcement learning (RL) stage, for generating OPC actions for representative design patterns, and a large language model (LLM) stage, for generating an OPC recipe based on the OPC actions provided by the RL stage.

The invention discloses an inversion photoetching method and system based on adaptive threshold regularization, and relates to the technical field of inversion photoetching and optical proximity correction in computational lithography, and the method comprises the following steps: obtaining an initial mask image; performing optimization processing on the initial mask image by adopting a CTM algorithm to obtain a gray value mask containing SRAFs; performing region division to obtain a gray value mask after region division; performing dynamic threshold adjustment and optimization on each region in the gray value mask after region division by adopting a self-adaptive threshold regularization algorithm to obtain a binary mask containing SRAFs (Static Random Access Frameworks); the dynamic threshold adjustment optimization is to adjust the threshold corresponding to each region in the gray value mask after region division in a manner of adaptively adjusting a parameter a, and determine the optimal threshold of each region; the parameter a is a parameter in a regularization function in an adaptive threshold regularization algorithm. According to the invention, the binary mask can be obtained, the industrial manufacturing requirement is met, and the mask imaging precision is improved.

The invention relates to the technical field of computational lithography, in particular to a mask pattern optimization method, a program product, a storage medium and equipment. The method comprises the following steps: providing a reticle graph; detecting defects of the reticle graph to generate a database file, wherein the database file comprises a defect data group corresponding to each detected defect; displaying the defect data set contained in the database file in real time through the first graphical user interface; receiving a selection instruction for the defect data set in the first graphical user interface, recording the selected defect data set to a second graphical user interface and displaying the selected defect data set in real time; and continuing iterative correction of the optical proximity effect, and after each round of correction in the iterative process is finished, updating the database file according to the newly corrected reticle graph. According to the method, the function of quickly repositioning the previously positioned defect information is provided for workers, the operation steps of a user in the mask graph correction process are simplified, and the working efficiency is improved.

The present application relates to the field of computing lithography, in particular to a kind of profile acquisition method, device, computer equipment and storage medium of SEM image. Method includes the following steps: obtaining the SEM image to be processed and extracting several profile points from SEM image;Using clustering algorithm to group several profile points;Using first preset algorithm to solve the connection order of each group of profile points;With the connection order obtained by first preset algorithm as heuristic, and using second preset algorithm to solve the connection order of all profile points;And with the connection order obtained by second preset algorithm to connect all profile points in order.The connection speed of the profile acquisition method of the present application is fast and the calculation amount is low.The profile acquisition device, computer equipment and storage medium of the present application have the same beneficial effects as the profile acquisition method of SEM image, which will not be described here.

The invention relates to the technical field of computational lithography, in particular to a job visualization management platform and a job execution method. A job scheduling module, a template management module and a job management module of the platform can be displayed on a display module; the template management module comprises a classification unit, and the classification unit is used for creating a job template; the operation management module comprises an operation generation unit and an operation monitoring unit, and the operation generation unit is used for receiving external operation data and importing the operation data into a corresponding operation template to generate one or more corresponding operations; the job scheduling module is used for receiving and executing jobs; the operation monitoring unit is used for monitoring the operation state of the operation on the operation scheduling module. Through visual display of the display module and classified management and operation monitoring of the operation, a user can efficiently manage the calculation photoetching operation and calculation resources, the overall efficiency of the calculation photoetching operation is improved, and a visual calculation photoetching operation visual management platform which is visual, easy to understand and excellent in experience feeling is provided for the user.

A non-transitory computer-readable medium stores a set of instructions that is executable by at least one processor of an apparatus to cause the apparatus to perform operations for optimizing an illumination source and mask used in a lithography process. The operations include obtaining a pupil profile. The operations also include simulating the lithography process using the pupil profile to generate predictions resulting from the lithography process. The operations also include calculating a cost function based on the predictions. The cost function includes a linear fading effect term that is indicative of a linear fading effect on the predictions. The operations also include adjusting the pupil profile based on the linear fading effect term.

A non-transitory computer-readable medium stores a set of instructions that is executable by at least one processor of an apparatus to cause the apparatus to perform operations for optimizing an illumination source and mask used in a lithography process. The operations include obtaining a pupil profile. The operations also include simulating the lithography process using the pupil profile to generate predictions resulting from the lithography process. The operations also include calculating a cost function based on the predictions. The cost function includes a linear fading effect term that is indicative of a linear fading effect on the predictions. The operations also include adjusting the pupil profile based on the linear fading effect term.

The present application belongs to the field of computational lithography technology, and specifically discloses a simulation method for a programmable illumination light source system and a light source mask collaborative optimization method. The simulation method includes: calibrating the parameters of the SMTM in a first simulation model based on a designed light source shape OSM sample and actual processing results; the actual processing results are obtained by inputting the OSM sample into a lithography physical machine and monitoring the processing process of the lithography physical machine, and the first simulation model is used to output a simulation processing result corresponding to the actual processing result, and the first simulation model at least includes the SMTM; the calibrated SMTM is used as a PIS model. The present application calibrates the parameters of the SMTM in the first simulation model using reference data to obtain a PIS model. When obtaining the PIS characteristics, it can effectively avoid the influence of PIS hardware distortion on the exposure effect on the lithography physical machine, thereby ensuring the yield of IC manufacturing.

The present invention relates to the field of computational lithography technology, and in particular to a method for optimizing and constructing an optical mask correction model, the method comprising the following steps: S0: acquiring multiple feature sizes of a mask layout and feature data of sub-resolution auxiliary graphics and preprocessing the feature data to obtain fitting data; S1: performing OPC modeling according to the feature sizes to obtain an initial OPC model; S2: optimizing the initial OPC model according to the fitting data to obtain an optimized OPC model. The present invention optimizes the traditional OPC model and introduces the feature data of sub-resolution auxiliary graphics, so that the optimized OPC model can more conveniently and accurately determine whether the sub-resolution auxiliary graphics are printed, so as to adjust the sub-resolution auxiliary graphics.

The present invention discloses a unified multi-task computational lithography modeling and defect detection method assisted by a large visual model. The method not only has high-precision modeling capabilities and defect recognition performance, but also significantly reduces model deployment costs and has advantages such as high automation, unified modeling, and engineering scalability. The method includes the following steps: constructing a dual-task dataset for image generation and defect detection; building a two-stage image generation model based on a unified Transformer architecture that integrates process conditions to generate mask and contour images from layout images; building a defect detection model based on DETR for layout, mask, and contour images to perform DRC, MRC, and LRC detection; and building an end-to-end lithography large model framework that supports inputting a layout and simultaneously outputting mask, contour, and DRC / MRC / LRC detection results.

This invention discloses a physical world model-driven multi-stage computational lithography evolution reasoning method, which can unfold the lithography state in the potential state space of each stage and, combined with process intervention strategies, achieve high-precision modeling and optimization of the lithography pattern evolution process. The method includes the following steps: Step 1, constructing a complete dataset by collecting lithography data under different layout and process parameter combinations; Step 2, mapping the physical state of each stage of the lithography process to the potential space, and modeling the potential state of each stage using a spatial embedding method; Step 3, establishing a process intervention strategy model in the potential space, and planning process adjustment schemes for different stages; Step 4, establishing a state transition model to simulate the continuous evolution of the potential state between stages, predicting the potential state of the next stage through the current stage potential state and intervention strategy, and achieving continuous deduction of the lithography state by iteratively updating the intervention scheme.

The present application relates to the field of computing lithography technology, in particular to a kind of curve OPC verification method, device, equipment and storage medium, by receiving target curve pattern and to be inquired analog curve pattern;Determine the target curve segment on the target curve pattern;Determine the sampling point on the target curve segment, and obtain the deviation distance of each sampling point along the normal direction of corresponding tangent to the analog curve pattern to be inquired, and the curvature corresponding to each sampling point;According to the deviation distance and curvature of each sampling point, determine the curve EPE of the analog curve pattern to be inquired on the target curve segment;When the curve EPE exceeds the EPE threshold value, the analog curve pattern to be inquired is determined as to be corrected analog curve pattern.The present application gives different weight to each curvature different sampling point according to corresponding curvature, thereby greatly improving the accuracy of curve OPC verification, significantly reducing the computing power consumption in the process of calculation.

The invention relates to a computational lithography modeling and hot spot detection method and device based on self-supervised learning, and the method comprises the steps: constructing a neural network model which comprises a layout feature encoder, a geometric reconstruction branch and a lithography space image prediction branch, and enabling a differentiable optical simulation layer to be built in the lithography space image prediction branch so as to integrate physical prior; after an input layout is masked based on label-free mask plate graph data, the model is driven to execute pre-training of geometric reconstruction and photoetching space image prediction at the same time, an original unmasked layout is processed through the differentiable optical simulation layer to generate a pseudo-true value, and a supervision signal is provided for photoetching space image prediction; and on the basis of the pre-training model, performing fine tuning by using a small amount of labeled data to realize hotspot detection. The differentiable optical simulation layer is integrated into the self-supervised learning framework, so that the model can learn the optical law from massive label-free data, the dependence on labeled data is reduced, and the hotspot detection capability and accuracy of unknown graphs are improved.

The present application relates to a kind of determining contact hole photolithography process condition method.The present application obtains the photolithography process condition by combining calculation lithography with design experiment: the target film thickness of the anti-reflection coating to be applied, the target film thickness of resist, target illumination parameter, at least one target exposure anchor point, and then the photolithography process verification is carried out on the aforementioned obtained photolithography process condition by changing energy margin and focusing depth on prefabricated substrate, to determine the contact hole photolithography process condition.The present application is suitable for 55nm-65nm technology node non-immersion photolithography process development, saves time and economic cost for 55nm-65nm technology node new product research and development and introduction, and effectively improves product yield.

The invention provides a monitoring device and a monitoring method for monitoring the quality of a coated photoresist, the monitoring device comprises a transmitting device, a receiving device and two angle adjusting devices, the transmitting device transmits laser containing S polarized light and P polarized light to the surface of the coated photoresist; the receiving device separates S polarized light and P polarized light in the reflected light reflected by the photoresist and respectively receives the S polarized light and the P polarized light; the first angle adjusting device adjusts the angle of the emitting device so as to adjust the included angle between the incident light and the vertical direction. The second angle adjusting device adjusts the included angle between the receiving device and the vertical direction to receive the reflected light. The monitoring device is used for measuring the Brewster angle of the photoresist so as to calculate the refractive index of the photoresist, and the quality of the photoresist is judged on the basis of the mapping relation between the refractive indexes under different Q-times after the photoresist is coated and key parameters and yield of a photoetching process, so that the purpose of monitoring the quality of the coated photoresist is achieved.

The invention discloses a data cleaning method, device and equipment, a medium and a product, and is applied to the field of photoetching processes. The method comprises the following steps: firstly, acquiring a plurality of photoetching modeling data, determining the distance between the photoetching modeling data by adopting a distance measurement mode suitable for the photoetching data, and then carrying out clustering processing on the photoetching modeling data based on the distance between the photoetching modeling data to obtain a plurality of clustering sets. And for each cluster set, only selecting a preset number of photoetching modeling data as representative elements, and filtering other non-representative elements except the representative elements. Compared with a traditional manual screening mode, only a small number of representative representative elements are selected from a plurality of similar elements for photoetching modeling, repeatability and useless data in photoetching modeling data are accurately removed, and the modeling accuracy and efficiency are greatly improved; high-quality, refined and representative data is provided for calculation photoetching modeling, and optimization and development of the photoetching process are powerfully promoted.

The application relates to the field of photoetching technology, in particular to a method and device for calculating a focus point of a photoetching light beam and a computer device, the method comprises the following steps: S0, acquiring a critical dimension of a mask pattern; S1, establishing a focus depth model according to the critical dimension and a preset optical modeling model; S2, under a preset metrology level, bringing the critical dimension into the focus depth model, and calculating a focus point offset of the critical dimension and feature information through a preset first algorithm; and S3, calculating a final focus point of the critical dimension through a preset second algorithm according to the focus point offset and the feature information. The method provided by the application considers the focus point offset and the focus depth information, and can more accurately find the focus point of the photoetching light beam.

The present application relates to the field of computing lithography, in particular to a kind of overlay mark screening method, readable storage medium and program product, overlay mark screening method includes the following steps: obtaining the characteristic parameter of overlay mark to be screened;Obtain the optical parameter of overlay measurement, and establish optical model according to optical parameter;Characteristic parameter is brought into optical model and at least two overlay errors in the preset range are set, and then the asymmetry result of overlay mark to be screened is simulated and calculated;Overlay error and asymmetry result are calculated by linear fitting, and fitting result is obtained;According to fitting result, overlay mark to be screened is screened.The overlay mark screening method provided by the present application can screen out overlay mark prone to larger measurement error.

A computational lithography process uses machine learning models. An aerial image produced by a lithographic mask is first calculated using a two-dimensional model of the lithographic mask. This first aerial image is applied to a first machine learning model, which infers a second aerial image. The first machine learning model was trained using a training set that includes aerial images calculated using a more accurate three-dimensional model of lithographic masks. The two-dimensional model is faster to compute than the three-dimensional model but it is less accurate. The first machine learning model mitigates this inaccuracy.

This application discloses an inversion lithography method and system based on adaptive threshold regularization, which relates to the fields of inversion lithography and optical proximity correction in computational lithography. The method includes obtaining an initial mask image; optimizing the initial mask image using a CTM algorithm to obtain a grayscale value mask containing SRAFs; performing regional division to obtain a grayscale value mask after regional division; and using an adaptive threshold regularization algorithm to dynamically adjust the thresholds of each region in the grayscale value mask after regional division to obtain a binary mask containing SRAFs. The dynamic threshold adjustment optimization is to adjust the thresholds corresponding to each region in the grayscale value mask after regional division by adaptively adjusting parameter a to determine the optimal threshold for each region. Parameter a is a parameter in the regularization function in the adaptive threshold regularization algorithm. This application can obtain a binary mask that meets industrial manufacturing needs and improves the accuracy of mask imaging.

The present invention discloses a compensation model creation method, a lithography exposure simulation method, a device and an apparatus, wherein the compensation model creation method comprises: determining the mask pattern transmission parameters corresponding to the test mask pattern according to the chip pattern sample; and collecting the exposure image data set corresponding to the test mask pattern; inputting the mask pattern transmission parameters into the constructed neural network model to obtain the predicted compensation parameters; inputting the predicted compensation parameters and the mask pattern transmission parameters into the computational lithography model to obtain the simulated exposure image; performing error comparison between the simulated exposure image and the real exposure image in the exposure image data set to obtain the comparison error; optimizing the neural network model according to the comparison error until the comparison error is not greater than the preset error, thereby obtaining the compensation neural network model. The training of the compensation neural network model in the present application is simple, easy to implement and has high accuracy, which is conducive to improving the accuracy of the simulation of the computational lithography model.

Various methods of training a machine learning model associated with a patterning process are described herein. A method for training a machine learning model configured to predict a mask pattern is described herein. The method comprises obtaining: (i) a process model of a patterning process configured to predict a pattern on a substrate, wherein the process model comprises one or more trained machine learning models; and (ii) a target pattern; and training, by the hardware computer system, a machine learning model configured to predict the mask pattern based on the process model and a cost function that determines a difference between the predicted pattern and the target pattern.

A non-transitory computer-readable medium stores a set of instructions that is executable by at least one processor of an apparatus to cause the apparatus to perform operations for simulating a lithography process. The operations include obtaining a physics-informed neural network (PINN) comprising multiple channel. A first channel of the multiple channels is associated with a first set of physical characteristics of an interaction between an electromagnetic (EM) field and a thick mask used in the lithography process. A second channel of the multiple channels is associated with a second set of physical characteristics of the interaction that is different from the first set of physical characteristics. The operations also include executing the PINN using the first channel to generate a near-field image representation of the EM field for the thick mask with the second channel disabled.

The present invention provides a method for establishing an OPC model, including: collecting focal length energy matrix data; establishing an optical model based on the focal length energy matrix data; using the optical model to simulate a photomask to obtain a simulated ADI critical dimension exposed on a chip, classifying the simulated ADI critical dimension into a first grade, a second grade, and a third grade, and recording the positions of the photomasks corresponding to the first grade, the second grade, and the third grade as a first position, a second position, and a third position respectively; calculating a lithography process window according to the optical model and exposing the chip under the lithography process window; measuring the actual ADI critical dimension of the graphic lines exposed on the chip corresponding to the first position; and establishing an OPC model based on the actual ADI critical dimension. The range of collecting ADI critical dimension measurements is reduced, the number of measured ADI critical dimensions is reduced, the accuracy of the established OPC model is improved, and the production efficiency can also be improved.