NIL Solutions using Computational Lithography for Semiconductor Device Manufacturing

被引:0
|
作者
Aihara, Sentaro [1 ]
Yamamoto, Kenji [1 ]
Nakano, Yukio [1 ]
Kijima, Hiromu [1 ]
Jimbo, Satoru [2 ]
Evans, Humberto [3 ]
Ishida, Shingo [1 ]
Fujimoto, Masayoshi [1 ]
Takami, Shota [2 ]
Oguchi, Yuichiro [2 ]
Seki, Junichi [2 ]
Asano, Toshiya [1 ]
Morimoto, Osamu [1 ]
机构
[1] Canon Inc, 20-2 Kiyohara Kogyodanchi, Utsunomiya, Tochigi 3213292, Japan
[2] Canon Inc, 30-2 Shimomaruko 3 Chome,Ohta Ku, Tokyo 1468501, Japan
[3] Canon Nanotechnol Inc, 1807 West Braker Lane,C-300, Austin, TX USA
来源
关键词
nanoimprint lithography; NIL; simulation; fluid structure interaction; computation fluid dynamics; CFD; FSI; STEP;
D O I
10.1117/12.3009839
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Computational technologies are still in the course of development for nanoimprint lithography (NIL). Only a few simulators are applicable to the nanoimprint process, and these simulators are desired by device manufacturers as part of their daily toolbox. The most challenging issue in NIL process simulation is the scale difference of each component of the system. The template pattern depth and the residual resist film thickness are generally of the order of a few tens of nanometers, while the process needs to work over the entire shot size, which is typically of the order of 10 mm square. This amounts to a scale difference of the order of 106. Therefore, in order to calculate the nanoimprint process with conventional fluid structure interaction (FSI) simulators, an enormous number of meshes is required, which results in computation times that are unacceptable. To support all lithographic systems, Canon has introduced "Lithography Plus", a software solution capable of anomaly detection, automatic recovery, trouble flow prediction and remote support. The software is now under development specifically for NIL. Because NIL is a rheological process, to software must address a completely new work flow. In this paper, we introduce the methods used to create drop patterns and refinements to the NIL process simulator which can be applied to predict resist filling and, in the future, be used to make corrections to the drop pattern virtually, thereby eliminating time consuming on-tool verification. Finally, we discuss the development of virtual metrology software that incorporates artificial intelligence to provide fast feedback on key tool outputs such as overlay.
引用
收藏
页数:9
相关论文
共 50 条
  • [31] Patterning, Mask Life, Throughput and Overlay Improvements for High Volume Semiconductor Manufacturing using Nanoimprint Lithography
    Morimoto, Osamu
    Iwanaga, Takehiko
    Takabayashi, Yukio
    Sakai, Keita
    Zhang, Wei
    Cherala, Anshuman
    Im, Se-Hyuk
    Meissl, Mario
    Choi, Jin
    PHOTOMASK TECHNOLOGY 2019, 2019, 11148
  • [32] Improved Particle Control for High Volume Semiconductor Manufacturing for Nanoimprint Lithography
    Yonekawa, Masami
    Nakayama, Takahiro
    Nakagawa, Kazuki
    Maeda, Toshihiro
    Matsuoka, Yoichi
    Emoto, Keiji
    Azuma, Hisanobu
    Takabayashi, Yukio
    Aghili, Ali
    Mizuno, Makoto
    Choi, Jin
    Jones, Chris E.
    PHOTOMASK JAPAN 2017: XXIV SYMPOSIUM ON PHOTOMASK AND NEXT-GENERATION LITHOGRAPHY MASK TECHNOLOGY, 2017, 10454
  • [33] Technology review and assessment of nanoimprint lithography for semiconductor and patterned media manufacturing
    Malloy, Matt
    Litt, Lloyd C.
    JOURNAL OF MICRO-NANOLITHOGRAPHY MEMS AND MOEMS, 2011, 10 (03):
  • [34] New open platform software for monitoring lithography process of semiconductor manufacturing
    Igarashi, Yutaka
    Kikuchi, Satoru
    Jordan, Levi
    Abe, Kunihiko
    Minegishi, Yuji
    OPTICAL MICROLITHOGRAPHY XXXI, 2018, 10587
  • [35] Improved Particle Control for High Volume Semiconductor Manufacturing for Nanoimprint Lithography
    Arai, Tsuyoshi
    Matsuoka, Yoichi
    Azuma, Hisanobu
    PHOTOMASK JAPAN 2018: XXV SYMPOSIUM ON PHOTOMASK AND NEXT-GENERATION LITHOGRAPHY MASK TECHNOLOGY, 2018, 10807
  • [36] Advance process control solutions for semiconductor manufacturing
    Sarfaty, M
    Shanmugasundram, A
    Schwarm, A
    Paik, J
    Zhang, JM
    Pan, R
    Seamons, MJ
    Li, H
    Hung, R
    Parikh, S
    2002 IEEE/SEMI ADVANCED SEMICONDUCTOR MANUFACTURING CONFERENCE AND WORKSHOP: ADVANCING THE SCIENCE OF SEMICONDUCTOR MANUFACTURING EXCELLENCE, 2002, : 101 - 106
  • [37] APPLICATIONS OF THE RAMAN MICROPROBE IN SEMICONDUCTOR-DEVICE MANUFACTURING
    NEEDHAM, CD
    PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 1983, 411 : 13 - 17
  • [38] Scatterometry for advanced process control in semiconductor device manufacturing
    den Boef, Arie
    Cramer, Hugo
    Petra, Stefan
    Auer, Bastiaan Onne Fagginger
    Schmetz-Schagen, Jolanda
    Koolen, Armand
    van Loon, Olaf
    de Gersem, Gudrun
    Klandermans, Pieter
    Bakker, Eric
    FIFTH INTERNATIONAL CONFERENCE ON OPTICAL AND PHOTONICS ENGINEERING, 2017, 10449
  • [40] Initial transients of solutions of the semiconductor device equations
    Szmolyan, P.
    Computers & mathematics with applications, 1990, 19 (8-9): : 43 - 58