TY - JOUR
T1 - L2O-ILT: Learning to Optimize Inverse Lithography Techniques
T2 - Learning to Optimize Inverse Lithography Techniques
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 was supported in part by the Research Grants Council of Hong Kong SAR under Project CUHK14208021; in part by the National Key R&D Program of China under Grant 2020YFA0711900 and Grant 2020YFA0711903; and in part by the National Natural Science Foundation of China under Grant 62204066.
PY - 2024/3
Y1 - 2024/3
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 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.
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 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.
KW - mask optimization
KW - design for manufacture
KW - learning to optimize
UR - http://www.scopus.com/inward/record.url?scp=85174847215&partnerID=8YFLogxK
U2 - 10.1109/TCAD.2023.3323164
DO - 10.1109/TCAD.2023.3323164
M3 - Journal article
AN - SCOPUS:85174847215
SN - 0278-0070
VL - 43
SP - 944
EP - 955
JO - IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
JF - IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IS - 3
ER -