Machine Learning based Optical Proximity Correction Techniques

The shrinking of the size of the advanced technological nodes brings up new challenges to the semiconductor manufacturing community. The optical proximity correction (OPC) is invented to reduce the errors of the lithographic process. The conventional OPC techniques rely on the empirical models and optimization methods of iterative type. Both the accuracy and computing speed of the existing OPC techniques need to be improved to fulfill the stringent requirement of the research and design for latest technological nodes. The emergence of machine learning technologies inspires novel OPC algorithms. More accurate forward simulation of the lithographic process and single turn optimization methods are enabled by the machine learning based OPC techniques. We discuss the latest progress made by the OPC community in the process simulation and optimization based on machine learning techniques.

Abstract licence: CC BY

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