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

doi:10.1109/TCAD.2023.3323164

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

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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 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 language	English
Pages (from-to)	1-12
Number of pages	12
Journal	IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
DOIs	https://doi.org/10.1109/TCAD.2023.3323164
Publication status	E-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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10.1109/TCAD.2023.3323164

Cite this

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title = "L2O-ILT: Learning to Optimize Inverse Lithography Techniques",

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 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.",

keywords = "Adaptive optics, Computational modeling, Kernel, Lithography, Optical diffraction, Optical imaging, Optimization",

author = "Binwu Zhu and Su Zheng and Ziyang Yu and Guojin Chen and Yuzhe Ma and Fan Yang and Bei Yu and Wong, {Martin D.F.}",

note = "Funding Information: This work is supported in part by The Research Grants Council of Hong Kong SAR (Project No. CUHK14208021) and National Key R&D Program of China 2020YFA0711900, 2020YFA0711903.",

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doi = "10.1109/TCAD.2023.3323164",

language = "English",

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AU - Zhu, Binwu

AU - Zheng, Su

AU - Yu, Ziyang

AU - Chen, Guojin

AU - Ma, Yuzhe

AU - Yang, Fan

AU - Yu, Bei

AU - Wong, Martin D.F.

N1 - Funding Information: This work is supported in part by The Research Grants Council of Hong Kong SAR (Project No. CUHK14208021) and National Key R&D Program of China 2020YFA0711900, 2020YFA0711903.

PY - 2023/10/10

Y1 - 2023/10/10

N2 - 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.

AB - 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.

KW - Adaptive optics

KW - Computational modeling

KW - Kernel

KW - Lithography

KW - Optical diffraction

KW - Optical imaging

KW - Optimization

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DO - 10.1109/TCAD.2023.3323164

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JO - IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

JF - IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

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L2O-ILT: Learning to Optimize Inverse Lithography Techniques

Abstract

Scopus Subject Areas

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