Deep Learning for Mask Synthesis and Verification: A Survey (Invited Paper)

Abstract

Achieving lithography compliance is increasingly difficult in advanced technology nodes. Due to complicated lithography modeling and long simulation cycles, verifying and optimizing photomasks becomes extremely expensive. To speedup design closure, deep learning techniques have been introduced to enable data-assisted optimization and verification. Such approaches have demonstrated promising results with high solution quality and efficiency. Recent research efforts show that learning-based techniques can accomplish more and more tasks, from classification, simulation, to optimization, etc. In this paper, we will survey the successful attempts of advancing mask synthesis and verification with deep learning and highlight the domainspecific learning techniques. We hope this survey can shed light on the future development of learning-based designautomation methodologies.