AdaOPC 2.0: Enhanced Adaptive Mask Optimization Framework for Via Layers

Wenqian Zhao, Xufeng Yao, Shuo Yin, Yang Bai, Ziyang Yu, Yuzhe Ma, Bei Yu, Martin D.F. Wong

Research output: Contribution to journalArticlepeer-review


Optical proximity correction (OPC) is a widely used technique to enhance the printability of designs in various foundaries. Recently, there has been a growing interest in using rigorous numerical optimization and machine learning to improve the robustness and efficiency of OPC. Our research focuses on developing a self-adaptive OPC framework that leverages the properties of pattern distribution and repetition in design layouts to optimize the correction process. We observe that different sub-regions in a design layer have varying pattern complexities, and many patterns repeat themselves throughout the layout. By exploiting these properties, we propose a framework that adaptively selects the most suitable OPC solvers from an extensible pool to optimize the correction process for each pattern based on its complexity. This approach allows for a co-optimization of speed and accuracy. Additionally, we introduce a graph-based dynamic pattern library that reuses optimized masks for repeated patterns, further accelerating the OPC flow. Our experimental results demonstrate a significant improvement in both performance and efficiency using our proposed framework.

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
StateAccepted/In press - 2024
Externally publishedYes


  • Complexity theory
  • Layout
  • Libraries
  • Lithography
  • Optical imaging
  • Pattern matching
  • Resists

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering


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