基于机器学习校正的极紫外光刻含缺陷掩模仿真方法
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摘要
提出了一种基于机器学习参数校正的极紫外光刻三维含缺陷掩模仿真方法。本方法采用随机森林、K近邻等机器学习方法,对基于结构分解法的含缺陷掩模衍射谱快速仿真模型的参数进行动态校正,提高了模型的精度及适应性。以严格仿真为标准值,对随机设定的50组接触孔掩模进行仿真验证,结果表明,经参数校正后,快速模型的空间像仿真精度平均提升了45%,且参数校正前、后的快速模型仿真精度皆优于所对比的改进型单平面近似法(平均仿真精度分别提升4.3倍和8.7倍)。此外,在对像面周期为44nm掩模的缺陷补偿仿真应用中,在仿真结果较一致(误差0.8nm)的情况下,校正后的快速模型的单次衍射谱仿真速度比严格仿真提升了约97倍。
This study proposes a fast simulation method that employs machine learning-based parameter calibration for three-dimensional(3D)rigorous simulation of defective masks in extreme ultraviolet lithography.The parameters of the structure-decomposed fast simulation model for defective mask diffraction are calibrated using machine learning methods,such as random forest and K-nearest neighbors,to improve the simulation accuracy and adaptivity.Herein,rigorous simulation is used as a benchmark standard for the calibration of model parameters.Simulation results of 50 validation contact masks set randomly reveal that the average simulation accuracy of aerial images is increased by 45% after calibration;both calibrated and uncalibrated fast models display better simulation accuracy(improved by 4.3and 8.7times,respectively)compared with an advanced single-surface approximation model.By applying defect-compensation simulation to a mask of 44-nm period,the simulation speed of single diffraction of the corrected fast model is～97times faster than that of the rigorous simulation when the simulation results are consistent(error is 0.8nm).
This study proposes a fast simulation method that employs machine learning-based parameter calibration for three-dimensional(3D)rigorous simulation of defective masks in extreme ultraviolet lithography.The parameters of the structure-decomposed fast simulation model for defective mask diffraction are calibrated using machine learning methods,such as random forest and K-nearest neighbors,to improve the simulation accuracy and adaptivity.Herein,rigorous simulation is used as a benchmark standard for the calibration of model parameters.Simulation results of 50 validation contact masks set randomly reveal that the average simulation accuracy of aerial images is increased by 45% after calibration;both calibrated and uncalibrated fast models display better simulation accuracy(improved by 4.3and 8.7times,respectively)compared with an advanced single-surface approximation model.By applying defect-compensation simulation to a mask of 44-nm period,the simulation speed of single diffraction of the corrected fast model is～97times faster than that of the rigorous simulation when the simulation results are consistent(error is 0.8nm).
引文
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[13] Zhang H,Li S K,Wang X Z.A rapid simulation method for diffraction spectra of EUV lithography mask based on improved structural decomposition[J].Acta Optica Sinica,2018,38(1):0105001.张恒,李思坤,王向朝.基于改进型结构分解的极紫外光刻掩模衍射谱快速仿真方法[J].光学学报,2018,38(1):0105001.
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[15] Li Y,Satake M,Peng D P,et al.Advancement of fast EUV lithography modeling/simulations and applications on evaluating different repair options for EUV mask multilayer defect[J].Proceedings of SPIE,2013,8880:88802G.
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[17] Clifford C H, Neureuther A R.Fast simulation methods and modeling for extreme ultraviolet masks with buried defects[J].Journal of Micro/Nanolithography,MEMS,and MOEMS,2009,8(3):031402.
[18] Liu X L,Li S K,Wang X Z.Simplified model for defective multilayer diffraction spectrum simulation in extreme ultraviolet lithography[J]. Acta Optica Sinica,2014,34(9):0905002.刘晓雷,李思坤,王向朝.极紫外光刻含缺陷多层膜衍射谱仿真简化模型[J].光学学报,2014,34(9):0905002.
[19] Clifford C H,Chan T T,Neureuther A R,et al.Compensation methods using a new model for buried defects in extreme ultraviolet lithography masks[J].Proceedings of SPIE,2010,7823:78230V.
[20] Ito M,Ogawa T,Otaki K,et al.Simulation of multilayer defects in extreme ultraviolet masks[J].Japanese Journal of Applied Physics,2001,40(4A):2549-2553.
[21] Evanschitzky P,Erdmann A,Besacier M,et al.Simulation of extreme ultraviolet masks with defective multilayers[J].Proceedings of SPIE,2003,5130:1035-1045.
[22] Liu X L,Li S K,Wang X Z.Simulation model based on equivalent layer method for defective mask multilayer in extreme ultra-violet lithography[J].Acta Optica Sinica,2015,35(6):0622005.刘晓雷,李思坤,王向朝.基于等效膜层法的极紫外光刻含缺陷掩模多层膜仿真模型[J].光学学报,2015,35(6):0622005.
[23] Jang IL-Y, Teki R, Jindal V,et al.Defect printability comparing actinic printing with advanced simulation for EUV masks[J].Proceedings of SPIE,2013,8679:86790H.
[24] Verduijn E,Mangat P,Wood O,et al.Printability and actinic AIMS review of programmed mask blank defects[J].Proceedings of SPIE, 2017, 10143:101430K.
[25] Melvin L S,Kandel Y,Isoyan A,et al.Individual multilayer reflectance and near field image formation in an EUV reticle[J].Proceedings of SPIE,2017,10450:104500F.
[26] Cao Y T,Wang X Z,Qiu Z C,et al.Simplified model for mask diffraction in extreme-ultraviolet projection lithography[J].Acta Optica Sinica,2011,31(4):0405001.曹宇婷,王向朝,邱自成,等.极紫外投影光刻掩模衍射简化模型的研究[J].光学学报,2011,31(4):0405001.
[27] Hashimoto T,Yamanashi H,Sugawara M,et al.Lithographic characterization of EUVL mask blank defects[J].Proceedings of SPIE,2004,5374:740-750.
[28] Fühner T,Schnattinger T,Ardelean G,et al.Dr.LiTHO:a development and research lithography simulator[J].Proceedings of SPIE,2007,6520:65203F.
[29] van de Kerkhof M,Jasper H,Levasier L,et al.Enabling sub-10nm node lithography:presenting the NXE:3400BEUV scanner[J].Proceedings of SPIE,2017,10143:101430D.
[30] Pedregosa F,Varoquaux G,Gramfort A,et al.Scikit-learn:machine learning in Python[J].Journal of Machine Learning Research,2011,12:2825-2830.
[2] Buitrago E,Meeuwissen M,Yildirim O,et al.Stateof-the-art EUV materials and processes for the 7nm node and beyond[J].Proceedings of SPIE,2017,10143:101430T.
[3] Wood II O R.EUVL:challenges to manufacturing insertion[J].Journal of Photopolymer Science and Technology,2017,30(5):599-604.
[4] Turkot B,Carson S L,Lio A,et al.EUV progress toward HVM readiness[J].Proceedings of SPIE,2016,9776:977602.
[5] Qi Z Q J,Rankin J H,Narita E,et al.Viability of pattern shift for defect-free extreme ultraviolet lithography photomasks[J]. JournalofMicro/Nanolithography,MEMS,and MOEMS,2016,15(2):021005.
[6] Pang L Y,Satake M,Li Y,et al.EUV multilayer defect compensation(MDC)by absorber pattern modification,film deposition,and multilayer peeling techniques[J].Proceedings of SPIE,2013,8679:86790U.
[7] Erdmann A, Xu D B, Evanschitzky P,et al.Characterization and mitigation of 3D mask effects in extreme ultraviolet lithography[J].Advanced Optical Technologies,2017,6(3/4):187-201.
[8] Haque R R,Levinson Z,Smith B W.3D mask effects of absorber geometry in EUV lithography systems[J].Proceedings of SPIE, 2016:9776:97760F.
[9] Qi Z Q J,Rankin J,Narita E,et al.Viability of pattern shift for defect-free EUV photomasks at the7nm node[J].Proceedings of SPIE,2015,9635:96350N.
[10] Erdmann A, Evanschitzky P, Bret T,et al.Modeling strategies for EUV mask multilayer defect dispositioning and repair[J].Proceedings of SPIE,2013,8679:86790Y.
[11] Lam M,Clifford C,Raghunathan A,et al.Enabling full field physics based OPC via dynamic model1222002-7generation[J].Proceedings of SPIE,2017,10143:1014316.
[12] Cao Y T,Wang X Z,Bu Y.Fast simulation method for contact hole mask in extreme-ultraviolet lithography[J].Acta Optica Sinica,2012,32(7):0705001.曹宇婷,王向朝,步扬.极紫外投影光刻接触孔掩模的快速仿真计算[J].光学学报,2012,32(7):0705001.
[13] Zhang H,Li S K,Wang X Z.A rapid simulation method for diffraction spectra of EUV lithography mask based on improved structural decomposition[J].Acta Optica Sinica,2018,38(1):0105001.张恒,李思坤,王向朝.基于改进型结构分解的极紫外光刻掩模衍射谱快速仿真方法[J].光学学报,2018,38(1):0105001.
[14] Lam M C,Neureuther A R.Simplified model for absorber feature transmissions on EUV masks[J].Proceedings of SPIE,2006,6349:63492H.
[15] Li Y,Satake M,Peng D P,et al.Advancement of fast EUV lithography modeling/simulations and applications on evaluating different repair options for EUV mask multilayer defect[J].Proceedings of SPIE,2013,8880:88802G.
[16] Gullikson E M,Cerjan C,Stearns D G,et al.Practical approach for modeling extreme ultraviolet lithography mask defects[J].Journal of Vacuum Science&Technology B:Microelectronics and Nanometer Structures,2002,20(1):81-86.
[17] Clifford C H, Neureuther A R.Fast simulation methods and modeling for extreme ultraviolet masks with buried defects[J].Journal of Micro/Nanolithography,MEMS,and MOEMS,2009,8(3):031402.
[18] Liu X L,Li S K,Wang X Z.Simplified model for defective multilayer diffraction spectrum simulation in extreme ultraviolet lithography[J]. Acta Optica Sinica,2014,34(9):0905002.刘晓雷,李思坤,王向朝.极紫外光刻含缺陷多层膜衍射谱仿真简化模型[J].光学学报,2014,34(9):0905002.
[19] Clifford C H,Chan T T,Neureuther A R,et al.Compensation methods using a new model for buried defects in extreme ultraviolet lithography masks[J].Proceedings of SPIE,2010,7823:78230V.
[20] Ito M,Ogawa T,Otaki K,et al.Simulation of multilayer defects in extreme ultraviolet masks[J].Japanese Journal of Applied Physics,2001,40(4A):2549-2553.
[21] Evanschitzky P,Erdmann A,Besacier M,et al.Simulation of extreme ultraviolet masks with defective multilayers[J].Proceedings of SPIE,2003,5130:1035-1045.
[22] Liu X L,Li S K,Wang X Z.Simulation model based on equivalent layer method for defective mask multilayer in extreme ultra-violet lithography[J].Acta Optica Sinica,2015,35(6):0622005.刘晓雷,李思坤,王向朝.基于等效膜层法的极紫外光刻含缺陷掩模多层膜仿真模型[J].光学学报,2015,35(6):0622005.
[23] Jang IL-Y, Teki R, Jindal V,et al.Defect printability comparing actinic printing with advanced simulation for EUV masks[J].Proceedings of SPIE,2013,8679:86790H.
[24] Verduijn E,Mangat P,Wood O,et al.Printability and actinic AIMS review of programmed mask blank defects[J].Proceedings of SPIE, 2017, 10143:101430K.
[25] Melvin L S,Kandel Y,Isoyan A,et al.Individual multilayer reflectance and near field image formation in an EUV reticle[J].Proceedings of SPIE,2017,10450:104500F.
[26] Cao Y T,Wang X Z,Qiu Z C,et al.Simplified model for mask diffraction in extreme-ultraviolet projection lithography[J].Acta Optica Sinica,2011,31(4):0405001.曹宇婷,王向朝,邱自成,等.极紫外投影光刻掩模衍射简化模型的研究[J].光学学报,2011,31(4):0405001.
[27] Hashimoto T,Yamanashi H,Sugawara M,et al.Lithographic characterization of EUVL mask blank defects[J].Proceedings of SPIE,2004,5374:740-750.
[28] Fühner T,Schnattinger T,Ardelean G,et al.Dr.LiTHO:a development and research lithography simulator[J].Proceedings of SPIE,2007,6520:65203F.
[29] van de Kerkhof M,Jasper H,Levasier L,et al.Enabling sub-10nm node lithography:presenting the NXE:3400BEUV scanner[J].Proceedings of SPIE,2017,10143:101430D.
[30] Pedregosa F,Varoquaux G,Gramfort A,et al.Scikit-learn:machine learning in Python[J].Journal of Machine Learning Research,2011,12:2825-2830.
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