CLC number: TN47
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2014-04-11
Cited: 5
Clicked: 8352
Kai-sheng Luo, Zheng Shi, Xiao-lang Yan, Zhen Geng. SVM based layout retargeting for fast and regularized inverse lithography[J]. Journal of Zhejiang University Science C, 2014, 15(5): 390-400.
@article{title="SVM based layout retargeting for fast and regularized inverse lithography",
author="Kai-sheng Luo, Zheng Shi, Xiao-lang Yan, Zhen Geng",
journal="Journal of Zhejiang University Science C",
volume="15",
number="5",
pages="390-400",
year="2014",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1300357"
}
%0 Journal Article
%T SVM based layout retargeting for fast and regularized inverse lithography
%A Kai-sheng Luo
%A Zheng Shi
%A Xiao-lang Yan
%A Zhen Geng
%J Journal of Zhejiang University SCIENCE C
%V 15
%N 5
%P 390-400
%@ 1869-1951
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300357
TY - JOUR
T1 - SVM based layout retargeting for fast and regularized inverse lithography
A1 - Kai-sheng Luo
A1 - Zheng Shi
A1 - Xiao-lang Yan
A1 - Zhen Geng
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 5
SP - 390
EP - 400
%@ 1869-1951
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300357
Abstract: inverse lithography technology (ILT), also known as pixel-based optical proximity correction (PB-OPC), has shown promising capability in pushing the current 193 nm lithography to its limit. By treating the mask optimization process as an inverse problem in lithography, ILT provides a more complete exploration of the solution space and better pattern fidelity than the traditional edge-based OPC. However, the existing methods of ILT are extremely time-consuming due to the slow convergence of the optimization process. To address this issue, in this paper we propose a support vector machine (SVM) based layout retargeting method for ILT, which is designed to generate a good initial input mask for the optimization process and promote the convergence speed. Supervised by optimized masks of training layouts generated by conventional ILT, SVM models are learned and used to predict the initial pixel values in the ‘undefined areas’ of the new layout. By this process, an initial input mask close to the final optimized mask of the new layout is generated, which reduces iterations needed in the following optimization process. Manufacturability is another critical issue in ILT; however, the mask generated by our layout retargeting method is quite irregular due to the prediction inaccuracy of the SVM models. To compensate for this drawback, a spatial filter is employed to regularize the retargeted mask for complexity reduction. We implemented our layout retargeting method with a regularized level-set based ILT (LSB-ILT) algorithm under partially coherent illumination conditions. Experimental results show that with an initial input mask generated by our layout retargeting method, the number of iterations needed in the optimization process and runtime of the whole process in ILT are reduced by 70.8% and 69.0%, respectively.
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