首页 > 论文 > 光学学报 > 40卷 > 10期(pp:1005001--1)

极紫外光刻掩模相位型缺陷的形貌重建方法

Method for Profile Reconstruction of Phase Defects in Extreme Ultraviolet Lithography Mask

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

提出了一种极紫外光刻掩模多层膜相位型缺陷的形貌重建方法。采用表面与底部形貌参数表征相位型缺陷的三维形貌;采用原子力显微镜测量缺陷表面形貌参数;采用傅里叶叠层成像技术重建含缺陷的空白掩模空间像复振幅;采用卷积神经网络与多层感知器两种深度学习模型构建空间像振幅/相位与缺陷底部形貌参数之间的关系,建立缺陷底部形貌参数重建模型;利用训练后模型从空间像的振幅与相位信息中重建出缺陷底部形貌参数。仿真结果表明,训练后模型可准确重建相位型缺陷的底部形貌参数。凸起型与凹陷型缺陷的底部半峰全宽重建结果的均方根误差分别为0.51 nm和0.43 nm,底部高度重建结果的均方根误差分别为3.35 nm和1.73 nm。由于采用空间像作为信息载体,本方法不受沉积条件的影响。

Abstract

This paper proposed a new method for profile reconstruction of phase defects in extreme ultraviolet lithography mask multilayer films. Three-dimensional profiles of phase defects were characterized using the top and bottom profile parameters. The top profile parameters of defects were measured using an atomic force microscope. Moreover, Fourier ptychography technology was used to retrieve the complex amplitudes of aerial images of the defected mask blanks. Using deep learning models, the bottom profile parameter reconstruction model of defects was constructed by determining the relationship between the amplitudes/phases of aerial images and the bottom profile parameters of defects. The deep learning models used herein include a convolutional neural network and multilayer perceptron. The bottom profile parameters of defects can be reconstructed from the amplitudes/phases of the aerial images using the trained models. The simulation results show that the trained models can accurately reconstruct the bottom profile parameters of phase defects. The root-mean-square errors of bottom full-width-half-maximum reconstruction results of bump and pit defects are 0.51 and 0.43 nm, respectively. The root-mean-square errors of bottom height reconstruction results are 3.35 and 1.73 nm, respectively. The proposed method is immune to the deposition conditions because it captures aerial images as an information carrier.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:O436.1

DOI:10.3788/AOS202040.1005001

所属栏目:衍射与光栅

基金项目:国家科技重大专项、上海市自然科学基金;

收稿日期:2019-12-30

修改稿日期:2020-02-14

网络出版日期:2020-05-01

作者单位    点击查看

成维:中国科学院上海光学精密机械研究所信息光学与光电技术实验室, 上海 201800中国科学院大学材料与光电研究中心, 北京 100049
李思坤:中国科学院上海光学精密机械研究所信息光学与光电技术实验室, 上海 201800中国科学院大学材料与光电研究中心, 北京 100049
王向朝:中国科学院上海光学精密机械研究所信息光学与光电技术实验室, 上海 201800中国科学院大学材料与光电研究中心, 北京 100049
张子南:中国科学院上海光学精密机械研究所信息光学与光电技术实验室, 上海 201800中国科学院大学材料与光电研究中心, 北京 100049

联系人作者:李思坤(lisikun@siom.ac.cn); 王向朝(wxz26267@siom.ac.cn);

备注:国家科技重大专项、上海市自然科学基金;

【1】Ronse K, Jonckheere R, Gallagher E, et al. EUVL is being inserted in manufacturing in 2019: what are the mask related challenges remaining? [J]. Proceedings of SPIE. 2019, 11177: 111770A.

【2】Jonckheere R. Overcoming EUV mask blank defects: what we can, and what we should [J]. Proceedings of SPIE. 2017, 10454: 104540M.

【3】Jonckheere R. EUV mask defectivity: a process of increasing control toward HVM [J]. Advanced Optical Technologies. 2017, 6(3/4): 203-220.

【4】Hashimoto T, Yamanashi H, Sugawara M, et al. Lithographic characterization of EUVL mask blankdefects [J]. Proceedings of SPIE. 2004, 5374: 740-751.

【5】Bakshi V. EUV lithography [M]. 2nd ed. Washington: SPIE. 2018, 411-491.

【6】Zhang H, Li S K, Wang X Z, et al. Optimization of defect compensation for extreme ultraviolet lithography mask by covariance-matrix-adaption evolution strategy [J]. Nanolithography, MEMS, and MOEMS. 2018, 17(4): 043505.

【7】Kwon H J, Harris-Jones J, Teki R, et al. Printability of native blank defects and programmed defects and their stack structures [J]. Proceedings of SPIE. 2011, 8166: 81660H.

【8】Tchikoulaeva A, Miyai H, Suzuki T, et al. EUV actinic blank inspection: from prototype to production [J]. Proceedings of SPIE. 2013, 8679: 86790I.

【9】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.

【10】Stearns D G, Mirkarimi P B, Spiller E. Localized defects in multilayer coatings [J]. Thin Solid Films. 2004, 446(1): 37-49.

【11】Upadhyaya M, Jindal V, Basavalingappa A, et al. Evaluating printability of buried native EUV mask phase defects through a modeling and simulation approach [J]. Proceedings of SPIE. 2015, 9422: 94220Q.

【12】Upadhyaya M, Basavalingappa A, Herbol H, et al. Level-set multilayer growth model for predicting printability of buried native extreme ultraviolet mask defects [J]. Journal of Vacuum Science & Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phenomena. 2015, 33(2): 021602.

【13】Xu D B, Evanschitzky P, Erdmann A. Extreme ultraviolet multilayer defect analysis and geometry reconstruction [J]. Nanolithography, MEMS, and MOEMS. 2016, 15(1): 014002.

【14】Rastegar A, Jindal V. EUV mask defects and their removal [J]. Proceedings of SPIE. 2012, 8352: 83520W.

【15】Zheng G A, Horstmeyer R, Yang C. Wide-field, high-resolution Fourier ptychographic microscopy [J]. Nature Photonics. 2013, 7(9): 739-745.

【16】Wojdyla A, Benk M P, Naulleau P P, et al. EUV photolithography mask inspection using Fourier ptychography [J]. Proceedings of SPIE. 2018, 10656: 106560W.

【17】Ou X Z, Horstmeyer R, Zheng G A, et al. High numerical aperture Fourier ptychography: principle, implementation and characterization [J]. Optics Express. 2015, 23(3): 3472-3491.

【18】LeCun Y, Bengio Y, Hinton G. Deep learning [J]. Nature. 2015, 521(7553): 436-444.

【19】Gu J X, Wang Z H, Kuen J, et al. Recent advances in convolutional neural networks [J]. Pattern Recognition. 2018, 77: 354-377.

【20】Svozil D, Kvasnicka V, Pospichal J. Introduction to multi-layer feed-forward neural networks [J]. Chemometrics and Intelligent Laboratory Systems. 1997, 39(1): 43-62.

【21】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.

【22】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.

【23】Zhang H, Li S K, Wang X Z, et al. 3D rigorous simulation of defective masks used for EUV lithography via machine learning-based calibration [J]. Acta Optica Sinica. 2018, 38(12): 1222002.
张恒, 李思坤, 王向朝, 等. 基于机器学习校正的极紫外光刻含缺陷掩模仿真方法 [J]. 光学学报. 2018, 38(12): 1222002.

【24】Mochi I, Goldberg K A, Xie R, et al. Quantitative evaluation of mask phase defects from through-focus EUV aerial images [J]. Proceedings of SPIE. 2011, 7969: 79691X.

【25】Smaali R, Besacier M, Schiavone P. Three-dimensional rigorous simulation of EUV defective masks using modal method by Fourier expansion [J]. Proceedings of SPIE. 2006, 6151: 615124.

【26】Shen L N, Wang X Z, Li S K, et al. General analytical expressions for the impact of polarization aberration on lithographic imaging under linearly polarized illumination [J]. Journal of the Optical Society of America A. 2016, 33(6): 1112-1119.

【27】Abadi M, Barham P, Chen J M, et al. TensorFlow: a system for large-scale machine learning . [C]∥12th Symposium on Operating Systems Design and Implementation, November 2-4, 2016, Savannah, GA, USA. Berkeley: USENIX. 2016, 265-283.

【28】Kingma D P. -01-30)[2019-12-29] . https: ∥arxiv.xilesou. 2017, top/abs/1412: 6980.

引用该论文

Cheng Wei,Li Sikun,Wang Xiangzhao,Zhang Zinan. Method for Profile Reconstruction of Phase Defects in Extreme Ultraviolet Lithography Mask[J]. Acta Optica Sinica, 2020, 40(10): 1005001

成维,李思坤,王向朝,张子南. 极紫外光刻掩模相位型缺陷的形貌重建方法[J]. 光学学报, 2020, 40(10): 1005001

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF