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Volume 30 Issue 3
May  2015
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Zhen Geng, Zheng Shi, Xiao-Lang Yan, Kai-Sheng Luo, Wei-Wei Pan. Fast Level-set-based Inverse Lithography Algorithm for Process Robustness Improvement and Its Application[J]. Journal of Computer Science and Technology, 2015, 30(3): 629-638. DOI: 10.1007/s11390-015-1549-7
Citation: Zhen Geng, Zheng Shi, Xiao-Lang Yan, Kai-Sheng Luo, Wei-Wei Pan. Fast Level-set-based Inverse Lithography Algorithm for Process Robustness Improvement and Its Application[J]. Journal of Computer Science and Technology, 2015, 30(3): 629-638. DOI: 10.1007/s11390-015-1549-7
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Fast Level-set-based Inverse Lithography Algorithm for Process Robustness Improvement and Its Application

  • 1. Institute of VLSI Design, Zhejiang University, Hangzhou 310027, China;
    2. National Information Control Laboratory, Chengdu 610036, China

Funds: This work was supported by the National Natural Science Foundation of China under Grant Nos. 61204111 and 61474098. A preliminary version of the paper was published in the Proceedings of CAD/Graphics 2013.
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  • Author Bio:

    Zhen Geng received his B.S. degree in electronic and information engineering from North China University of Water Resources and Electric Power, Zhenzhou, in 2010. Now he is a Ph.D. candidate in circuit and system in Zhejiang University, Hangzhou. His research interests include design for manufacturability and yield enhancement technology.

  • Received Date: March 26, 2014
  • Revised Date: November 18, 2014
  • Published Date: May 04, 2015
    Abstract
  • Inverse lithography technology (ILT) is one of the promising resolution enhancement techniques (RETs), as the advanced integrated circuits (IC) technology nodes still use the 193nm light source. Among all the algorithms for ILT, the level-set-based ILT (LSB-ILT) is a feasible choice with good production result in practice. However, existing ILT algorithms optimize mask at nominal process condition without giving sufficient attention to the process variations, and thus the optimized masks show poor performance with focus and dose variations. In this paper, we put forward a new LSB-ILT algorithm for process robustness improvement with fast convergence. In order to account for the process variations in the optimization, we adopt a new form of the cost function by adding the objective function of process variation band (PV band) to the nominal cost. We also adopt the hybrid conjugate gradient (CG) method to reduce the runtime of the algorithm. We perform experiments on ICCAD 2013 benchmarks and the results show that our algorithm outperforms the top two winners of the ICCAD 2013 contest by 6.5%. We also adopt the attenuated Phase Shift Mask (att-PSM) in the experiment with test cases from industry. The results show that our new algorithm has fast convergence speed and reduces the process manufacturability index (PMI) by 38.77% compared with the LSB-ILT algorithm without PV band.
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    • References (30)
  • [1]
    Ma X, Arce G R. Computational Lithography. Wiley, 2011.
    [2]
    Lam E Y, Wong A K. Nebulous hotspot and algorithm variability in computation lithography. Journal of Micro/ Nanolithography, MEMS, and MOEMS, 2010, 9(3): 033002.
    [3]
    Nocedal J,Wright S. Numerical Optimization (2nd edition). New York: Springer, 2006.
    [4]
    Liu Y, Zakhor A. Binary and phase shifting mask design for optical lithography. IEEE Transactions on Semiconductor Manufacturing, 1992, 5(2): 138-152.
    [5]
    Granik Y. Fast pixel-based mask optimization for inverse lithography. Journal of Micro/Nanolithography, MEMS, and MOEMS, 2006, 5(4): 043002.
    [6]
    Yu P, Shi S X, Pan D Z. Process variation aware OPC with variational lithography modeling. In Proc. the 43rd Annual Design Automation Conference, Jul. 2006, pp.785-790.
    [7]
    Yu P, Pan D Z. TIP-OPC: A new topological invariant paradigm for pixel based optical proximity correction. In Proc. ICCAD, Nov. 2007, pp.847-853.
    [8]
    Poonawala A, Milanfar P. Mask design for optical microlithography — An inverse imaging problem. IEEE Transactions on Image Processing, 2007, 16(3): 774-788.
    [9]
    Shen S, Yu P, Pan D Z. Enhanced DCT2-based inverse mask synthesis with initial SRAF insertion. In Proc. SPIE 7122, Oct. 2008, pp.712241.
    [10]
    Zhang J Y, Xiong W, Wang Y, Yu Z P, Tsai M C. A highly efficient optimization algorithm for pixel manipulation in inverse lithography technique. In Proc. ICCAD, Nov. 2008, pp.480-487.
    [11]
    Pang L, Liu Y, Abrams D. Inverse lithography technology (ILT) for advanced semiconductor manufacturing. J. Exp. Mech., 2007, 22: 295-304.
    [12]
    Shen Y,Wong N, Lam E Y. Level-set-based inverse lithography for photomask synthesis. Optics Express, 2009, 17(26): 23690-23701.
    [13]
    Geng Z, Shi Z, Yan X, Luo K. Regularized level-set-based inverse lithography algorithm for IC mask synthesis. Journal of Zhejiang University SCIENCE C, 2013, 14(10): 799-807.
    [14]
    Jia N, Wong A K, Lam E Y. Robust mask design with defocus variation using inverse synthesis. In Proc. SPIE 7140, Dec. 2008, pp.71401W.
    [15]
    Pang L, Xiao G, Tolani V, Hu P, Cecil T, Dam T, Baik K, Gleason B. Considering MEEF in inverse lithography technology (ILT) and source mask optimization (SMO). In Proc. SPIE 7122, Oct. 2008, pp.71221W.
    [16]
    Li J, Jia N, Lam Ed Y. Hotspot-aware robust mask design with inverse lithography. ECS Transactions, 2012, 44(1): 197-202.
    [17]
    Gao J, Xu X, Yu B, Pan D. MOSAIC: Mask optimizing solution with process window aware inverse correction. In Proc. the 51st ACM/EDAC/IEEE Annual Design Automation Conference, Jun. 2014, pp.52:1-52:6.
    [18]
    Ma X, Arce G R. Pixel-based OPC optimization based on conjugate gradients. Optics Express, 2011, 19(3): 2165-2180.
    [19]
    Lv W, Liu S, Xia Q, Wu X, Shen Y, Lam E Y. Levelset-based inverse lithography for mask synthesis using the conjugate gradient and an optimal time step. Journal of Vacuum Science & Technology B, 2013, 31(4): 041605.
    [20]
    Cobb N B, Zakhor A, Miloslavsky E A. Mathematical and CAD framework for proximity correction. In Proc. SPIE 2726, Mar. 1996, pp.208-222.
    [21]
    Hopkins H. On the diffraction theory of optical images. In Proc. R. Soc. Lond. A, May 1953, pp.408-432.
    [22]
    Cobb N B, Zakhor A. Fast sparse aerial-image calculation for OPC. In Proc. SPIE 2621, Oct. 1995, pp.534-545.
    [23]
    Pan W, Ren J, Zheng Y, Shi Z, Yan X. Using NMOS transistors as switches for accuracy and area-efficiency in largescale addressable test array. In Proc. the 12th ISQED, Mar. 2011, pp.1-6.
    [24]
    Zhang B, PanW, Zheng Y, Shi Z, Yan X. A fully automated large-scale addressable test chip design with high reliability. In Proc. the 20th ECCTD, Aug. 2011, pp.61-64.
    [25]
    Fletcher R, Reeves C M. Function minimization by conjugate gradients. The Computer Journal, 1964, 7(2): 149-154.
    [26]
    Polyak B T. The conjugate gradient method in extreme problems. USSR Computational Mathematics and Mathematical Physics, 1969, 9(4): 94-112.
    [27]
    Hager W W, Zhang H. A survey of nonlinear conjugate gradient methods. Pacific Journal of Optimization, 2006, 2(1): 35-58.
    [28]
    Banerjee S, Zhuo L, Nassif S R. ICCAD-2013 CAD contest in mask optimization and benchmark suite. In Proc. ICCAD, Nov. 2013, pp.271-274.
    [29]
    Banerjee S, Agarwal K B, Orshansky M. Methods for joint optimization of mask and design targets for improving lithographic process window. Journal of Micro/ Nanolithography, MEMS, and MOEMS, 2013, 12(2): 023014.
    [30]
    Torres J A, Berglund C N. Integrated circuit DFM framework for deep sub-wavelength processes. In Proc. SPIE 5756, May 2005, pp.39-50.
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