Loading [a11y]/accessibility-menu.js
AdaOPC: A Self-Adaptive Mask Optimization Framework For Real Design Patterns | IEEE Conference Publication | IEEE Xplore

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


Abstract:

Optical proximity correction (OPC) is a widely-used resolution enhancement technique (RET) for printability optimization. Recently, rigorous numerical optimization and fa...Show More

Abstract:

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.
Date of Conference: 29 October 2022 - 03 November 2022
Date Added to IEEE Xplore: 22 March 2023
ISBN Information:

ISSN Information:

Conference Location: San Diego, CA, USA

References

References is not available for this document.