Fast and Accurate Machine Learning Inverse Lithography Using Physics Based Feature Maps and Specially Designed DCNN

Abstract

Inverse lithography technology (ILT) is intended to achieve optimal mask design to print a lithography target for a given lithography process. Full chip implementation of rigorous inverse lithography remains a challenging task because of enormous computational resource requirements and long computational time. To achieve full chip ILT solution, attempts have been made by using machine learning techniques based on deep convolution neural network (DCNN). The reported input for such DCNN is the rasterized images of the lithography target; such pure geometrical input requires DCNN to possess considerable number of layers to learn the optical properties of the mask, the nonlinear imaging process, and the rigorous ILT algorithm as well. To alleviate the difficulties, we have proposed the physics based optimal feature vector design for machine learning ILT in our early report. Although physics based feature vector followed by feed-forward neural network can provide the solution to machine learning ILT, the feature vector is long and it can consume considerable amount of memory resource in practical implementation. To improve the resource efficiency, we proposed a hybrid approach in this study by combining first few physics based feature maps with a specially designed DCNN structure to learn the rigorous ILT algorithm. Our results show that this approach can make machine learning ILT easy, fast and more accurate.

Keywords

• optimal feature maps
• inverse lithography technology (ilt)
• deep convolution neural network (dcnn)