41 results about "Computational lithography" patented technology

A recording medium stores a program for causing a computer to execute a method of calculating a resist pattern. The method includes: a first step of calculating a light intensity distribution of an optical image formed on the resist, based on the reticle pattern and an exposure condition; a second step of convoluting, using a first diffusion length, the calculated light intensity distribution; a third step of calculating a representative light intensity from the calculated light intensity distribution or the convoluted light intensity distribution; a fourth step of correcting the convoluted light intensity distribution by adding, to the convoluted light intensity distribution, a correction function including a first function given by:{∑k=0n⁢(ak⁢Jk)}⁢exp⁡(-α⁢⁢J)where J is the distribution of the representative light intensity; and a fifth step of calculating the resist pattern based on the corrected light intensity distribution and a slice level set in advance.

The present application discloses a mask plate processing method and device in computational lithography. The method comprises acquiring the length and the direction of the side of a polygon by the vertex data of the polygon; defining the data format of the polygon according to the length and the direction of the side; and enabling a mask plate pattern to perform storage, reading, and transmissionin accordance with the data format. Compared with the prior art, the method disclosed in the present application reduces the storage space of the mask plate pattern by storing the sides of the polygon in the mask plate pattern, and enables the fast reading and transfer of the data of the mask plate pattern between computer programs. Moreover, when a mask plate pattern function is obtained, a basic pattern function is linearly combined based on the direction of the sides in the polygon, thereby improving the efficiency of the computational lithography.

This application relates to the technical field of integrated circuit manufacturing, and discloses a computational lithography modeling method and device. In this method, the optical module group is determined by obtaining the type of the graphic unit contained in the test pattern, and then according to the optical module group, the obtained Parameters of the physical optics model. Calculate the ideal light intensity distribution of the optical module group on the photoresist, and obtain the parameters of the photochemical model according to the ideal light intensity distribution and the photochemical reaction excited by the optical module group on the photoresist. Then simulate the boundary position of the test pattern formed on the photoresist, and obtain the critical dimension simulation data of the test pattern, if the fitting error between the critical dimension measurement data and the critical dimension simulation data is not greater than the preset allowable error, a computational lithography model is established. The computational lithography modeling method disclosed in the present application effectively improves the accuracy of the computational lithography model by establishing different optical modules to simulate different graphics units.

The invention relates to the technical field of the photolithographic process for semiconductor production and particularly relates to a photolithographic process resolution enhancing method and device based on multi-target optimization. The method comprises steps of obtaining regional division of an illumination light source through a method of dividing a plurality of circles to be overlapped foroptimizing the light source; determining an initial position of a sub-resolution auxiliary graph SRAF position variable for optimizing a mask; establishing a population of optimization variables by using a real number encoding method; determining an evaluation standard function of a multi-target optimization strategy for a single chromosome in the population by calculating a photolithographic model; repeatedly carrying out evaluation-selection-crossover-variation calculation of the current population by using the genetic evolution algorithm to obtain iterative update of the evaluation standard function; decoding a final population to obtain a solution set Pareto support solution of the multi-target optimization strategy.

The present invention provides a computational lithography method for optical proximity effect correction. The computational lithography method decomposes the input layout design file into a first layout part and a second layout part according to the value of the process factor, wherein the process factor of the first layout part is lower than a preset threshold, and the second layout part is The process factor of the layout part is greater than the preset threshold; then the first layout part and the second layout part are processed respectively; then, the processed first layout part and the second layout part are matched and merged layout section. Since the first layout part and the second layout part are processed separately, the processing quality is improved, so it is no longer necessary to iteratively perform the step of correcting the optical proximity effect and the step of correcting the hot spot, thereby improving the output speed of the layout design file. The present invention also provides a computational lithography system for optical proximity effect correction.

The invention discloses a deep learning method for computational lithography, which can improve the optimization speed and convergence performance of the traditional OPC method, and can be used for both deep ultraviolet DUV lithography and extreme ultraviolet EUV lithography. Firstly, the gradient iterative algorithm is expanded and its first steps are intercepted, combined with the imaging model of the lithography system and the photoresist model, a forward model-driven convolutional neural network MCNN, namely the encoder, is created. Create a decoder corresponding to the encoder, connect the output of the MCNN to the input of the decoder, use a certain distance between the input of the MCNN and the output of the decoder as a cost function, and train the parameters of the MCNN. The training set samples are input into MCNN, and the parameters in MCNN are optimized to minimize the error between the input of MCNN and the output of the decoder. After the training is completed, the MCNN is separated from the decoder, and the OPC mask graphics corresponding to other circuit layouts can be obtained by using the MCNN.

The invention discloses a computational lithography method for a model-driven convolution neural network (MCNN), and the method can improve the computation speed and convergence performance of OPC (optical proximity correction) method. The technical scheme includes: expanding and truncating the gradient iterative algorithm, and constructing a model-driven convolution neural network (MCNN); based on an imaging model of a lithography system, constructing a decoder corresponding to the MCNN; bringing the MCNN and the decoder to end-to-end connection, and subjecting the MCNN to the following training: optimizing the parameters of the MCNN by back propagation algorithm to minimize the error between the input data of MCNN and the decoder; separating the decoder from the MCNN at the end of the training; inputting a to-be-optimized circuit layout into the trained MCNN so as to obtain an estimated result of an OPC mask; taking the estimated result of OPC mask as the initial value, and carryingout iterative updating on the set number of times of the mask by gradient iterative algorithm, thus obtaining the final OPC mask optimization result.

The invention belongs to the field of computational lithography, and discloses a method for correcting the proximity effect of an electron beam based on a neural network. The present invention first corrects the dose of the designed training layout through any kind of proximity effect dose correction method, then adjusts the neural network parameters, and performs neural network training on the training layout according to the prescribed input method to obtain an adaptive neural network. Network, and finally predict the correction dose of any exposure layout according to the specified input method. The invention realizes the correction of the proximity effect of the electron beam based on the neural network, and solves the correction calculation of the proximity effect of the electron beam exposure. On the basis of ensuring high-precision correction, the calculation efficiency of the correction is greatly improved, and it has high calculation accuracy and high calculation efficiency. Efficiency and other characteristics.