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

Research output: Contribution to journalArticlepeer-review

Abstract

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 article, 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 the previous methods.

Original languageEnglish (US)
Pages (from-to)944-955
Number of pages12
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Volume43
Issue number3
DOIs
StatePublished - Mar 1 2024
Externally publishedYes

Keywords

  • Design for manufacture
  • learning to optimize
  • mask optimization

ASJC Scopus subject areas

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

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