FuILT-S: Full Chip ILT With Unified Boundary Healing and SRAF Co-Optimization

Mask optimization in lithography is becoming increasingly important as the technology node size shrinks down. Inverse Lithography Technology (ILT) remains one of the most robust and high-performance solutions adopted in industry, yet it suffers from significant computational overhead and complexity. With the growing scale of transistor integration, industry attention has shifted toward improving efficiency and workload scalability. However, most recent academic efforts remain confined to localized pattern restoration regardless of full-chip manufacturing conditions. In this work, we present FuILT-S , a practical full-chip ILT-based mask optimization framework that incorporates sub-resolution assist features (SRAFs) co-optimization to address real-world manufacturing challenges. To enable full-chip scalability, we build a multi-level partitioning strategy following the divide-and-conquer mindset, paired with a workload distribution framework for efficient multi-GPU parallelization. To fix inter-region boundary stitching artifacts, we propose a gradient-fusion technique alongside a multi-level healing strategy. Furthermore, by leveraging rich information from the fused gradient and mask intensity maps, we integrate an SRAF generation module co-optimized with the main layout patterns. Experimental results across different layers from real-world designs demonstrate that FuILT-S is both effective and generalizable. Our framework is open-sourced at https://github.com/OpenOPC/OpenFuILT.

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Publication Details

Journal
ACM Transactions on Design Automation of Electronic Systems
Published
2026-03-28
DOI
https://doi.org/10.1145/3801558
Primary Topic
Advancements in Photolithography Techniques
Type
article
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article

FuILT-S: Full Chip ILT With Unified Boundary Healing and SRAF Co-Optimization

Weihua Zhao, Lancheng Zou, Bin Yu, Shuo Yin et al.
ACM Transactions on Design Automation of Electronic Systems
Advancements in Photolithography Techniques
article

FuILT-S: Full Chip ILT With Unified Boundary Healing and SRAF Co-Optimization

Weihua Zhao, Lancheng Zou, Bin Yu, Shuo Yin, Jiayi Xu, Su Zheng, Tsungyi Ho, ZIYANG YU, Yuzhe Ma
article en

Abstract

Mask optimization in lithography is becoming increasingly important as the technology node size shrinks down. Inverse Lithography Technology (ILT) remains one of the most robust and high-performance solutions adopted in industry, yet it suffers from significant computational overhead and complexity. With the growing scale of transistor integration, industry attention has shifted toward improving efficiency and workload scalability. However, most recent academic efforts remain confined to localized pattern restoration regardless of full-chip manufacturing conditions. In this work, we present FuILT-S , a practical full-chip ILT-based mask optimization framework that incorporates sub-resolution assist features (SRAFs) co-optimization to address real-world manufacturing challenges. To enable full-chip scalability, we build a multi-level partitioning strategy following the divide-and-conquer mindset, paired with a workload distribution framework for efficient multi-GPU parallelization. To fix inter-region boundary stitching artifacts, we propose a gradient-fusion technique alongside a multi-level healing strategy. Furthermore, by leveraging rich information from the fused gradient and mask intensity maps, we integrate an SRAF generation module co-optimized with the main layout patterns. Experimental results across different layers from real-world designs demonstrate that FuILT-S is both effective and generalizable. Our framework is open-sourced at https://github.com/OpenOPC/OpenFuILT.

ACM Transactions on Design Automation of Electronic Systems
Chinese University of Hong Kong (HK)
Industry, innovation and infrastructure
Openalex Percentile: Top 4%
Advancements in Photolithography Techniques
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FuILT-S: Full Chip ILT With Unified Boundary Healing and SRAF Co-Optimization — Weihua Zhao, Lancheng Zou, et al. · ACM Transactions on Design Automation of Electronic Systems (2026) | TGRS Research Map | TGRS