论文标题
Damo：深度敏捷面膜优化用于全芯片秤
DAMO: Deep Agile Mask Optimization for Full Chip Scale
论文作者
论文摘要
VLSI系统的连续缩放在制造上留下了巨大的挑战，并且光学接近校正（OPC）被广泛应用于常规设计流中，以进行可制造性优化。传统技术通过利用光刻模型并遭受了高度的计算开销而进行的OPC进行，并且主要侧重于优化单个剪辑而无需解决如何应对完整芯片。在本文中，我们介绍了Damo，这是一种用于完整芯片量表的高性能和可扩展的深度学习OPC系统。它是一种端到端掩模优化范式，其中包含用于光刻建模的深光刻模拟器（DLS）和用于掩模模式生成的深面膜生成器（DMG）。此外，提议为Damo定制的一种新颖的布局拆分算法来解决完整的芯片OPC问题。广泛的实验表明，Damo的表现优于学术界和工业商业工具包中最先进的OPC解决方案。
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.
