Enabling Scalable AI Computational Lithography with Physics-Inspired Models

Abstract

Computational lithography is a critical research area for the continued scaling of semiconductor manufacturing process technology by enhancing silicon printability via numerical computing methods. Today's solutions for these problems are primarily CPU-based and require many thousands of CPUs running for days to tape out a modern chip. We seek AI/GPU-assisted solutions for the two problems, aiming at improving both runtime and quality. Prior academic research has proposed using machine learning for lithography modeling and mask optimization, typically represented as image-to-image mapping problems, where convolution layer backboned UNets and ResNets are applied. However, due to the lack of domain knowledge integrated into the framework designs, these solutions have been limited by their application scenarios or performance. Our method aims to tackle the limitations of such previous CNN-based solutions by introducing lithography bias into the neural network design, yielding a much more efficient model design and significant performance improvements.