AdaOPC: A Self-Adaptive Mask Optimization Framework For Real Design Patterns
This work addresses efficiency challenges in semiconductor manufacturing for industry, but it is incremental as it builds on existing OPC methods.
The paper tackles the efficiency problem in optical proximity correction (OPC) for semiconductor design by proposing a self-adaptive framework that selects solvers based on pattern complexity and reuses optimized masks for repeated patterns, achieving substantial improvements in performance and efficiency.
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.