58 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.

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 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.

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 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.

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.

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.

The invention belongs to the technical field of holographic lithography, and provides a mask construction method based on digital holographic calculation lithography, which comprises the following steps: 1, reversely constructing complex plane distribution of mask emergent waves based on a target lithography image; 2, modeling is carried out according to a light field regulation and control method, and an illumination wavefront initial value and a mask initial value are calculated through numerical values; 3, alternately optimizing the mask Um and the illumination wavefront U1 through variational inference; and 4, repeating the step 3 until the constructed mask Um meets a process processing constraint condition. According to the method, the technical problem that a mask construction method is lacked in digital holographic calculation photoetching is effectively solved, and a reliable solution is provided for construction of a next-generation high-precision holographic calculation photoetching mask.

The invention provides a mask pattern, a semiconductor device, and a preparation method and equipment thereof, and the preparation method of the mask pattern comprises the steps: recognizing a first pattern region and a second pattern region in a design layout, a lower metal layer of the first pattern region is a dense pattern region smaller than a first line width, and a lower metal layer of the second pattern region is a dense pattern region smaller than a second line width; the lower metal layer of the second pattern region is a pattern region greater than or equal to the first line width; solving a maximum photoetching size window connected with the through hole in the first pattern area through an inverse calculation photoetching method, and generating a first photomask pattern according to the maximum photoetching size window; solving a second photomask pattern matched with the second pattern area through an optical proximity correction algorithm; and integrating the first photomask pattern and the second photomask pattern. The invention provides a mixed calculation photoetching method, which can effectively solve the difficulty of a photoetching process window with a specific complex structure, and also can give consideration to the production efficiency and the cost.

A layout is used in a computational lithography process. For example, the layout may be the layout of the lithographic mask or the layout of the desired resist shape. The layout is made up of multiple disjoint shapes. At least some of the disjoint shapes are represented by parametric curve representations, rather than polygons or other rectilinear representations. The parametric curve representations of the shapes are then used in the computational lithography process.

The invention relates to a photoresist resorption device and a resorption method, and belongs to the technical field of semiconductor manufacturing, and the photoresist resorption method comprises the following steps: when the photoresist resorption device is in a first resorption working mode, a resorption unit resorbs photoresist, an image acquisition unit acquires a photoresist nozzle image and sends the photoresist nozzle image to a control unit; the control unit calculates the height of an air column in the photoresist nozzle according to the photoresist nozzle image, and controls the adjusting unit to adjust or not adjust the position of photoresist in the photoresist nozzle; when the photoresist resorption device is in a second resorption working mode, the resorption unit resorbs the photoresist, the image acquisition unit acquires a photoresist nozzle image and sends the photoresist nozzle image to the control unit, and the control unit calculates the height of a first air column and the height of a second air column according to the photoresist nozzle image; and the control unit controls the adjusting unit to adjust or not adjust the position of the photoresist in the photoresist nozzle according to the height of the first air column and the height of the second air column. According to the invention, the position of the photoresist in the photoresist nozzle can be controlled.

The invention relates to the technical field of computational lithography, and discloses a mask optimization method and device and exposure equipment, and the method comprises the following steps: collecting a wavefront aberration parameter of an illumination beam of mask pattern transfer equipment; calculating the wavefront aberration of the illumination beam according to the wavefront aberration parameter; calculating an image surface wavefront matrix located on an imaging surface based on the wavefront aberration and a preset initial transmittance function; and optimizing the initial transmissivity function according to the image surface wavefront matrix and a loss function of a preset imaging graph to obtain a target transmissivity function which is used for optimizing a mask plate to realize wavefront aberration compensation. According to the invention, the wavefront aberration is compensated in the process of designing the mask plate, so that an illumination system with a complex structure and a complex wavefront aberration adjustment process are avoided, the holographic photoetching idea is perfectly matched, and the optical path complexity and the technical implementation difficulty of holographic photoetching are reduced.

The invention belongs to the technical field of computational lithography, and particularly discloses a network training method for generating an EUV optimal mask, which is characterized in that a DUV mask technology is applied to the generation of an EUV mask, and the mask generated by a DUV mask generation model guides a generator to correct in training, so that the training advances towards an optimized direction; adding a compensation part generated by an EUV mask in the generator so as to realize maximum optimization; a key graph screening technology is applied to a target image, an EUV mask technology and a DUV mask technology at the same time, and the speed and effect of full-chip mask optimization in training are guaranteed; according to the method, under the frame of the CGAN, a mask generated by a DUV mask generation model is used as additional information during training, an EUV compensation part is added on the basis of a general generator, and a model capable of generating an EUV optimal mask is obtained through training by a discriminator.

The invention belongs to the technical field of computational lithography, and particularly discloses a sub-resolution auxiliary pattern two-dimensional structure thick mask near-field modeling method, which comprises the following steps: generating a two-dimensional reference mask corresponding to each size in a size sequence based on the size sequence, the size sequence comprising a plurality of sizes, and the difference between two adjacent sizes in the size sequence being a preset step length; performing strict physical simulation on the two-dimensional reference masks corresponding to the sizes to construct a near-field distribution function library; based on the size of the two-dimensional structure of each SRAF graph in the design layout, an adaptive near-field distribution function is extracted from the near-field distribution function library through an interpolation method, corresponding multi-channel image convolution is carried out, and a near-field distribution image of the two-dimensional structure of each SRAF graph in the design layout is obtained; and constructing a thick mask near-field distribution image of a whole-layout SRAF graph two-dimensional structure through near-field mutual superposition. According to the method and the device, the SRAF graph two-dimensional structure thick mask near field can be quickly and accurately constructed.

The present application relates to the field of semiconductor manufacturing and computer lithography, and particularly relates to a contour extraction method and device of SEM image, computer equipment and storage medium. The contour extraction method of SEM image comprises the following steps: extracting M discrete contour points from the SEM image, wherein M is an integer; randomly selecting N initial control points, and obtaining an initial Bezier curve based on the N initial control points, wherein N is an integer and N is less than or equal to M; calibrating the N initial control points to gradually fit the initial Bezier curve with the M contour points to obtain a calibrated Bezier curve; and outputting the calibrated Bezier curve as the contour information extracted from the SEM image. The contour extraction method of the present application solves the technical problems of large data volume of contour extraction results and loss of contour information in the existing contour extraction technology of SEM image. The contour extraction device, computer equipment and storage medium of the present application have the same beneficial effects as the contour extraction method.