Optical proximity correction by using unsupervised learning and the patch loss function

Abstract

The miniaturization of nodes poses new challenges in semiconductor manufacturing. Optical proximity correction (OPC) is typically performed to satisfy technical requirements through iterative optimization. However, this method is expensive and slow. This study proposes a framework based on patch loss and a generative adversarial network through unsupervised learning to address these problems. The target pattern is used as the input of the model to avoid dependence on OPC tools. Thus, a fast approach is proposed for realizing OPC swiftly.