Details

Autor(en) / Beteiligte

• Zhao, Wenqian
• Yao, Xufeng
• Yu, Ziyang
• Chen, Guojin
• Ma, Yuzhe
• Yu, Bei
• Wong, Martin D.F.

Titel

AdaOPC: A Self-Adaptive Mask Optimization Framework For Real Design Patterns

Ist Teil von

• 2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD), 2022, p.1-9

Ort / Verlag

ACM

Erscheinungsjahr

2022

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• Optical proximity correction (OPC) is a widely-used resolution enhancement technique (RET) for printability optimization. Recently, rigorous numerical optimization and fast machine learning are the research focus of OPC in both academia and industry, each of which complements the other in terms of robustness or efficiency. We inspect the pattern distribution on a design layer and find that different sub-regions have different pattern complexity. Besides, we also find that many patterns repetitively appear in the design layout, and these patterns may possibly share optimized masks. We exploit these properties and propose a self-adaptive OPC framework to improve efficiency. Firstly we choose different OPC solvers adaptively for patterns of different complexity from an extensible solver pool to reach a speed/accuracy co-optimization. Apart from that, we prove the feasibility of reusing optimized masks for repeated patterns and hence, build a graph-based dynamic pattern library reusing stored masks to further speed up the OPC flow. Experimental results show that our framework achieves substantial improvement in both performance and efficiency.