Full Text:   <3364>

Summary:  <2136>

CLC number: TN47

On-line Access: 2014-05-06

Received: 2013-12-09

Revision Accepted: 2014-03-11

Crosschecked: 2014-04-11

Cited: 5

Clicked: 7830

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE C 2014 Vol.15 No.5 P.390-400


SVM based layout retargeting for fast and regularized inverse lithography

Author(s):  Kai-sheng Luo, Zheng Shi, Xiao-lang Yan, Zhen Geng

Affiliation(s):  Institute of VLSI Design, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   luoks@vlsi.zju.edu.cn

Key Words:  Inverse lithography technology, Optical proximity correction, Layout retargeting, Support vector machine

Share this article to: More <<< Previous Article|

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",
publisher="Zhejiang University Press & Springer",

%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

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

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Banerjee, S., Agarwal, K.B., 2011. Integrated model-based retargeting and optical proximity correction. SPIE, 7974:79740F.

[2]Byun, H., Lee, S.W., 2002. Pattern Recognition with Support Vector Machine. Springer, Berlin, Heidelberg, p.571-591.

[3]Chen, Y., Wu, K., Shi, Z., et al., 2007. A feasible model-based OPC algorithm using Jacobian matrix of intensity distribution functions. SPIE, 6520:65204C.

[4]Chiang, C., Kawa, J., 2007. Design for manufacturability and yield for nano-scale CMOS. Series on Integrated Circuits and Systems. Springer, Dordrecht, The Netherlands, p.58-72.

[5]Cobb, N.B., Zakhor, A., 1995. Fast sparse aerial image calculation for OPC. SPIE, 2621:534-545.

[6]Cobb, N.B., Zakhor, A., Miloslavsky, E.A., 1996. Mathematical and CAD framework for proximity correction. SPIE, 2726:208-222.

[7]Corinna, C., Vladimir, V., 1995. Support-vector networks. Mach. Learn., 20(3):273-297.

[8]Erdmann, A., Farkas, R., Fuhner, T., et al., 2004. Towards automatic mask and source optimization for optical lithography. SPIE, 5377:646-657.

[9]Garofalo, J., Low, K., Otto, O., et al., 1994. Automatic proximity correction for 0.35 μm I-line photolithography. Proc. IEEE Int. Workshop on Numerical Modeling of Processes Devices for Integrated Circuits, p.92-94.

[10]Geng, Z., Shi, Z., Yan, X.L., et al., 2013. Regularized level-set-based inverse lithography algorithm for IC mask synthesis. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 14(10):799-807.

[11]Granik, Y., 2005. Solving inverse problems of optical microlithography. SPIE, 5754:506-526.

[12]Granik, Y., 2006. Fast pixel-based mask optimization for inverse lithography. J. Micro/Nanolith. MEMS MOEMS, 5(4):043002.

[13]Gu, A., Zakhor, A., 2008. Optical proximity correction with linear regression. IEEE Trans. Semicond. Manuf., 21(2):263-271.

[14]Hopkins, H.H., 1953. On the diffraction theory of optical images. Proc. R. Soc. Lond. A, 217(1130):408-432.

[15]Huang, W.C., Lai, C.M., Luo, B., et al., 2006. Intelligent model-based OPC. SPIE, 6154:615436.

[16]Hung, M., Balasingam, P., 2002. Hybrid optical proximity correction: concepts and results. SPIE, 4889:1173-1180.

[17]ITRS, 2012. International Technology Roadmap for Semiconductors 2012 Update Overview. ITRS. Available from http://www.itrs.net/Links/2012ITRS/2012Chapters/2012Overview.pdf [Accessed on Sept. 1, 2013].

[18]Kotani, T., Kobayashi, S., Ichikawa, H., et al., 2002. Advanced hybrid optical proximity correction system with OPC segment library and model-based correction module. SPIE, 4691:188-195.

[19]Lin, B., Yan, X.L., Shi, Z., et al., 2011. A sparse matrix model-based OPC algorithm with model-based mapping between segments and control sites. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 12(5):436-442.

[20]Lv, W., Xia, Q., Liu, S., 2013. Pixel-based inverse lithography using a mask filtering technique. SPIE, 8683:868325.

[21]Ma, X., Arce, G., 2007. Generalized inverse lithography methods for phase-shifting mask design. Opt. Expr., 15(23):15066-15079.

[22]Ma, X., Arce, G., 2008. Binary mask optimization for inverse lithography with partially coherent illumination. J. Opt. Soc. Am. A, 25(12):2960-2970.

[23]Ma, X., Li, Y.Q., 2011. Resolution enhancement optimization methods in optical lithography with improved manufacturability. J. Micro/Nanolith. MEMS MOEMS, 10(2):023009.

[24]Ma, X., Li, Y.Q., Dong, L.S., 2012a. Mask optimization approaches in optical lithography based on a vector imaging model. J. Opt. Soc. Am. A, 29(7):1300-1312.

[25]Ma, X., Li, Y.Q., Guo, X.J., et al., 2012b. Vectorial mask optimization methods for robust optical lithography. J. Micro/Nanolith. MEMS MOEMS, 11(4):043008.

[26]Oh, Y., Lee, J.C., Lim, S., 1999. Resolution enhancement through optical proximity correction and stepper parameter optimization for 0.12-μm mask pattern. SPIE, 3679:607-613.

[27]Osher, S., Sethian, J.A., 1988. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys., 79(1):12-49.

[28]Pang, L.Y., Dai, G., Cecil, T., et al., 2008. Validation of inverse lithography technology (ILT) and its adaptive SRAF at advanced technology nodes. SPIE, 6924:69240T.

[29]Park, J., Park, C., Phie, S., et al., 2000. An efficient rule-based OPC approach using DRC tool for 0.18 μm ASIC. Proc. IEEE 1st Int. Symp. on Quality Electronic Design, p.81-85.

[30]Platt, J.C., 1998. Sequential Minimal Optimization: a Fast Algorithm for Training Support Vector Machines. Technical Report MsR-TR-98-14, Microsoft Research, Microsoft Inc., Redmond, WA.

[31]Poonawala, A., Milanfar, P., 2007a. Mask design for optical microlithography—an inverse imaging problem. IEEE Trans. Image Process., 16(3):774-778.

[32]Poonawala, A., Milanfar, P., 2007b. A pixel-based regularization approach to inverse lithography. Microelectro. Eng., 84(12):2837-2852.

[33]Shen, S.H., Peng, Y., Pan, D.Z., 2008. Enhanced DCT2-based inverse mask synthesis with initial SRAF insertion. SPIE, 7122:712241.

[34]Shen, Y.J., Wong, N., Lam, E.Y., 2009. Level-set-based inverse lithography for photomask synthesis. Opt. Expr., 17(26):23690-23701.

[35]Wong, A.K.K., 2001. Resolution Enhancement Techniques in Optical Lithography. SPIE Press, Bellingham, Washington, USA, p.28.

[36]Yang, E., Li, C.H., Kang, X.H., et al., 2009. Model-based retarget for 45nm node and beyond. SPIE, 7274:727428.

[37]Yang, Y.W., Shi, Z., Shen, S.H., 2009. Seamless-merging-oriented parallel inverse lithography technology. J. Semicond., 30(10):106002-106006.

[38]Yu, P., Pan, D.Z., 2007. TIP-OPC: a new topological invariant paradigm for pixel based optical proximity correction. Proc. IEEE/ACM Int. Conf. on Computer-Aided Design, p.847-853.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE