Fast mask near-field calculation using fully convolution network

Jiaxin Lin, Lisong Dong, Taian Fan, Xu Ma*, Rui Chen, Yayi Wei

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)

Abstract

Near-field calculation for thick mask is a fundamental task in lithography simulations. This paper proposes a fully convolution network (FCN) method to improve the efficiency of three-dimensional (3D) mask near-field calculation compared to the rigorous electromagnetic field (EMF) simulation methods. Taking into account the 3D mask effects, the network is trained based on a set of mask samples and the corresponding near-field data obtained by the EMF simulator. During the testing stage, the trained FCN is used to rapidly predict the diffraction near-field of the testing mask patterns. The performance of the proposed FCN approach is evaluated by simulations based on EUV lithography.

Original languageEnglish
Title of host publication2020 4th International Workshop on Advanced Patterning Solutions, IWAPS 2020
EditorsYayi Wei, Tianchun Ye
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728175775
DOIs
Publication statusPublished - 5 Nov 2020
Event4th International Workshop on Advanced Patterning Solutions, IWAPS 2020 - Chengdu, China
Duration: 5 Nov 20206 Nov 2020

Publication series

Name2020 4th International Workshop on Advanced Patterning Solutions, IWAPS 2020

Conference

Conference4th International Workshop on Advanced Patterning Solutions, IWAPS 2020
Country/TerritoryChina
CityChengdu
Period5/11/206/11/20

Keywords

  • 3D mask effect
  • deep learning
  • diffraction near-field
  • fully convolution network
  • machine learning

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