DAMO: Deep Agile Mask Optimization for Full-Chip Scale

Abstract

Continuous scaling of the very-large-scale integration system leaves a significant challenge on manufacturing; thus optical proximity correction (OPC) is widely applied in conventional design flow for manufacturability optimization. Traditional techniques conduct OPC by leveraging a lithography model but may suffer from prohibitive computational overhead. In addition, most of them focus on optimizing a single and local clip instead of addressing how to tackle the full-chip scale. In this article, we present DAMO, a high-performance and scalable deep-learning-enabled OPC system for full-chip scale. It is an end-to-end mask optimization paradigm that contains a deep lithography simulator (DLS) for lithography modeling and a deep mask generator (DMG) for mask pattern generation. Moreover, a novel layout splitting algorithm customized for DAMO is proposed, composed of DBSCAN clustering and KMeans++ clustering, to handle the full-chip OPC problem. Further, graph-based computation and parallelism techniques are proposed to deploy our GPU algorithms to accelerate computations. Extensive experiments show that DAMO outperforms state-of-the-art OPC solutions in both academia and industrial commercial toolkit.