DAMO: Deep Agile Mask Optimization for Full Chip Scale

TL;DR

DAMO introduces a scalable deep learning-based OPC system for full chip mask optimization, significantly improving efficiency and performance over traditional methods by integrating a deep lithography simulator and mask generator.

Contribution

The paper presents DAMO, an end-to-end deep learning framework for full chip OPC, including a novel layout splitting algorithm and integrated lithography modeling and mask generation.

Findings

DAMO outperforms existing OPC solutions in accuracy and speed.

The layout splitting algorithm effectively handles full chip scale optimization.

Deep learning components significantly reduce computational overhead.

Abstract

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.