L2O-ILT: Learning to Optimize Inverse Lithography Techniques

Binwu Zhu, Su Zheng, Ziyang Yu, Guojin Chen, Yuzhe Ma, Fan Yang, Bei Yu*, Martin D.F. Wong

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review


Inverse lithography technique (ILT) is one of the most widely used resolution enhancement techniques (RETs) to compensate for the diffraction effect in the lithography process. However, ILT suffers from runtime overhead issues with the shrinking size of technology nodes. In this paper, our proposed L2O-ILT framework unrolls the iterative ILT optimization algorithm into a learnable neural network with high interpretability, which can generate a high-quality initial mask for fast refinement. Experimental results demonstrate that our method achieves better performance on both mask printability and runtime than previous methods.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Publication statusE-pub ahead of print - 10 Oct 2023

Scopus Subject Areas

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

User-Defined Keywords

  • Adaptive optics
  • Computational modeling
  • Kernel
  • Lithography
  • Optical diffraction
  • Optical imaging
  • Optimization


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