隨著現代積體電路的複雜度不斷增加與製程節點的演進，電路可製造性在現代微影(Lithography)製程中正遭遇許多困難。次級解析輔助特徵圖案(SRAF) 擺置與光學鄰近修正(OPC)等重要的解析度增強技術被應用在最大化製程視窗(Process Window)與加強特徵解析度。目前工業應用中，大量應用了傳統以模型為基礎(Model-Based)的次級解析輔助特徵圖案擺置與光學鄰近修正技術，但也蒙受由迭代最佳化
過程(Iterative optimization process)所引起的過久的執行時間之缺點。在本論文中，我們提出了第一篇基於深度學習(Deep Learning)架構，同時考慮次級解析輔助特徵圖案擺置與以邊為基礎(Edge-Based)的光學鄰近修正。此外，為了使最佳化的光罩(Optimized Mask)在實際工業應用更加可靠與更有公信力，我們採用了商用微影工具軟體，在晶圓影像(Wafer Image) 上驗證了許多微影度量(Lithographic Metric)。本論文所提出的架構展在實驗結果所呈現的效用與效率，展現了以機器學
習之顯影最佳化技術在現代複雜與大規模積體電路的成功。

With the dramatically increase of design complexity and the advance of semiconductor technology nodes, huge difficulties appear during design for manufacturability with existing lithography solutions. Sub-resolution assist feature (SRAF) insertion and optical proximity correction (OPC) are both inevitable resolution enhancement techniques (RET) to maximize process window and ensure feature printability. Conventional model-based SRAF insertion and OPC methods are widely applied in industrial application but suffer from the extremely long runtime due to iterative optimization process. In this thesis, we propose the first work developing a deep learning framework to simultaneously perform SRAF insertion and edge-based OPC. In addition, to make the optimized masks more reliable and convincing for industrial application, we employ a commercial lithography simulation tool to consider the quality of wafer image with various lithographic metrics. The effectiveness and efficiency of the proposed framework are demonstrated in experimental results, which also show the success of machine learning-based lithography optimization techniques for the current complex and large-scale circuit layouts.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean et al., "TensorFlow: A system for large-scale machine learning," in Proc. OSDI, pp. 265–283, 2016.

Kuan-Jung Chen, Yu-Kai Chuang, Bo-Yi Yu and Shao-Yun Fang, "Minimizing cluster number with clip shifting in hotspot pattern classification," 2017 Design Automation Conference (DAC), pp. 1-6, 2017.

N.~Cobb, ``Fast optical and process proximity correction algorithm for integrated circuit manufacturing,'' Ph.D. dissertation, University of California at Berkeley, 1998.

F. Lie, C. Y. Huang, C. S. Wu, K. T. Chen and H. F. Kuo, "Demonstration of ACO-Based Freeform Source for ArF Laser Immersion Lithography System," in IEEE Access, vol. 5, pp. 6421-6428, 2017.

A.~H.~Gabor, J.~A.~Bruce, W.~Chu, R.~A.~Ferguson, C.~A.~Fonseca, R.~L.~Gordon, K.~R.~Jantzen, M.~Khare, M.~A.~Lavin, W.~Lee, L.~W.~Liebmann, K.~P.~Muller, J.~H.~Rankin, P.~Varekamp, and F.~X.~Zach, ``Subresolution assist feature implementation for high-performance logic gate-level lithography," in Proceedings of SPIE, vol.~4691, pp.~418--425, 2002.

A.~Gu and A.~Zakhor, ``Optical proximity correction with linear regression,'' IEEE Transactions on Semiconductor Manufacturing, vol.~21, no.~2, pp.~263--271, 2008.

Haoyu Yang, Shuhe Li, Yuzhe Ma, Bei Yu and Evangeline F. Y. Young, "GAN-OPC: Mask Optimization with Lithography-guided
Generative Adversarial Nets," 2018 Design Automation Conference (DAC), Article No. 131, 2018.

N. Jia and E. Y. Lam, "Stochastic gradient descent for robust inverse photomask synthesis in optical lithography," 2010 IEEE International Conference on Image Processing, Hong Kong, pp. 4173-4176, 2010.

S.-K.~Kim, ``Model-based OPC for resist reflow process,'' Digest of Papers Microprocesses and Nanotechnology, pp.~18--19, 2005.

R.~Luo, ``Optical proximity correction using a multilayer perceptron neural network,'' Journal of Optics, vol.~15, no.~7, 2013.

X.~Ma and Y.~Li, ``Resolution enhancement optimization methods in optical lithography with improved manufacturability,'' Journal of Micro/Nanolithography, MEMS, and MOEMS, vol.~10, no.~2, 2011.

X.~Ma, B.~Wu, Z.~Song, S.~Jiang, and Y,~Li, ``Fast pixel-based optical proximity correction based on nonparametric kernel regression,''Journal of Micro/Nanolithography, MEMS, and MOEMS, vol.~13, no.~4, 2014.

T.~Matsunawa, B.~Yu, D.~Z.~Pan, ``Optical proximity correction with hierarchical bayes model,'' in Proceedings of SPIE, 2013.

O.~W.~Otto, J.~G.~Garofalo, K.~K.~Low, C.-M.~Yuan, R.~C.~Henderson, C.~Pierrat, R.~L.~Kostelak, S.~Vaidya, P.~K.~Vasudev, ``Automated optical proximity correction: a rules-based approach,'' in Proceedings of SPIE, 1994.

J.-S.~Park, C.-H.~Park, S.-U.~Rhie, Y.-H.~Kim, M.-H.~Yoo, J.-T~Kong, H.-W.~Kim, and S.-I.~Yoo, ``An efficient rule-based OPC approach using a DRC tool for 0.18 μm ASIC,'' in Proceedings IEEE International Symposium on Quality Electronic Design, 2000.

Y. Ping et al., "Process window enhancement using advanced RET techniques for 20nm contact layer," in Proceedings of SPIE, Art. no. 90521N, 2014.

J. Schmidhuber. "Deep Learning in Neural Networks: An Overview. Neural Networks," Volume 61, Pages 85-117, January 2015.

Seongbo Shim, Suhyeong Choi and Youngsoo Shin, "Machine learning (ML)-based lithography optimizations", IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), pp.~530-533, 2016.

Y.-H.~Su, Y.-C.~Huang, L.-C.~Tsai, Y.-W.~Chang, and S.~Banerjee, ``Fast lithographic mask optimization considering process variation," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol.~35, no.~8, pp.~1345--1357, 2016.

A.~K.~Wong, ``Resolution enhancement techniques in optical lithography," in Proceedings of SPIE, 2001.

L.~Yin, C.~Wang, and L.~Li, "Process window tripling by optimized SRAF placement rules: AP/DFM: Advanced patterning/design for manufacturability," in Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC), 2016.

X.~Xu, Y,~Lin, M.~Li, T.~Matsunawa, S.~Nojima, C.~Kodama, T.~Kotani, and D.~Z.~Pan, ``Sub-resolution assist feature generation with supervised data learning,'' IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2017.

S.~Banerjee, Z.~Li, and S.~R.~Nassif, ``ICCAD-2013 CAD contest in mask optimization and benchmark suite,'' in Proceedings of International Conference on Computer-Aided Design, 2013.

KLA-Tencor PROLITH user guide. https://www.kla-tencor.com/