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

TL;DR

AdaOPC introduces a self-adaptive framework for optical proximity correction that leverages pattern complexity and repetition to optimize mask design efficiently, combining solver adaptivity and mask reuse.

Contribution

It proposes a novel self-adaptive OPC framework that dynamically selects solvers and reuses masks based on pattern analysis, enhancing efficiency and accuracy.

Findings

Significant speedup in OPC process

Improved mask optimization accuracy

Effective reuse of patterns for efficiency

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.