  Continuous scaling of the VLSI system leaves a great challenge on
manufacturing and optical proximity correction (OPC) is widely applied in
conventional design flow for manufacturability optimization. Traditional
techniques conducted OPC by leveraging a lithography model and suffered from
prohibitive computational overhead, and mostly focused on optimizing a single
clip without addressing how to tackle the full chip. In this paper, 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 which 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 to handle the full chip OPC problem.
Extensive experiments show that DAMO outperforms the state-of-the-art OPC
solutions in both academia and industrial commercial toolkit.
