Optimal feature vector design method for carrying out reverse photolysis based on machine learning

Abstract

The invention discloses an optimal feature vector design method for carrying out reverse photolysis based on machine learning. The method comprises the following steps: dividing a design target pattern into a plurality of grid units; calculating a characteristic function set {Ki (x, y)} according to imaging conditions, i = 1, 2,... N1; establishing a neural network model, and selecting a trainingsample and a verification sample which need to be included in training; calculating a signal set {Si (x, y)} of each grid unit by using a characteristic function set {Ki (x, y)}; taking the value of the strict reverse photoetching at the corresponding position as a target value of neural network training; training, different input end dimensions N1, the number N2 of hidden layers and the number M1of neurons of each hidden layer are adopted; training the neural network model according to different combinations of the input end dimension N1, the hidden layer number N2 and the neuron number M1,M2,... MN2 by using a training sample, and verifying the neural network model by using a verification sample until the neural network model with satisfactory combinations of the input end dimension N1, the hidden layer number N2 and the neuron number M1, M2,... MN2 of each hidden layer is found. Therefore, according to the design method, the neural network does not need a feature extraction layerany more, so that the network architecture is simplified, and the training time is shortened.

Description

technical field [0001] The invention belongs to the field of integrated circuit manufacturing, and in particular relates to a method for designing an optimal feature vector for reverse lithography based on machine learning. Background technique [0002] Computational lithography plays a vital role in the semiconductor industry. When the semiconductor technology node shrinks to 14nm and below, the lithography technology is gradually approaching its physical limit. As a new resolution enhancement technology, Source Mask Optimization (SMO) can significantly improve the The overlapping process window of semiconductor lithography effectively extends the life cycle of current conventional lithography technology. SMO is not only an important part of 193nm immersion lithography technology, but also an essential technology in EUV lithography. [0003] The basic principle of simulation calculation of light source mask co-optimization is similar to that of model-based proximity effec...

AI Technical Summary

Problems solved by technology

[0006] However, in DCNN, in order to extract feature vectors with sufficient resolution and near complete representation ability, the feature vector extraction layer is very complicated and lacks real physical meaning

In order to extract feature vectors with sufficient resolution and approximately complete representation capabilities, the training of the DCNN network requires a large number of balanced samples, which makes the training more difficult and time-consuming

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