강의명

Computational Lithography with Machine Learning Techniques (권용휘 박사)

강의 요약

Machine learning models provide a convenient solution for quickly predicting results from complex and time-consuming processes, making them ideal for lithography modeling and optimization. In this short lecture, we will explore several applications where machine learning can be utilized to enhance computational lithography processes. These applications include optical proximity correction (OPC), etch proximity correction (EPC), 3D resist modeling, layout pattern synthesis, sub-resolution assist feature (SRAF) generation and printability check, hotspot detection and fix, and optic and resist modeling. By utilizing machine learning techniques in these areas, we can significantly reduce computation time and improve accuracy, making the lithography process more efficient and effective.