SEM Image Transformation Between Litho Domain and Etch Domain

Yan Yan, X. Shi, Chen Li, Bowen Xu, Yifei Lu, Ying Gao, Wenzhan Zhou, Kan Zhou
{"title":"SEM Image Transformation Between Litho Domain and Etch Domain","authors":"Yan Yan, X. Shi, Chen Li, Bowen Xu, Yifei Lu, Ying Gao, Wenzhan Zhou, Kan Zhou","doi":"10.1109/CSTIC52283.2021.9461458","DOIUrl":null,"url":null,"abstract":"In semiconductor manufacturing, a forward etching process model that can accurately predict the pattern transformation from post-lithography (ADI) to post-etch (AEI) is greatly desired. However, current etching model is etch bias based, it is unable to offer rich information as the SEM image does for engineers to do wafer disposition. In practice, ambiguous circumstances often arise when a disposition decision cannot be made easily by examining the post-lithography pattern image alone. The probability of making the disposition decision correctly can be greatly enhanced if the post etch image can be predicted accurately. Likewise, an inverse etching model that can predict post lithography pattern SEM image from post-etch pattern SEM image is also desired for OPC lithography target layer definition. Current OPC lithography target layer is derived from etch bias estimation from a forward etching model. The rigorous solution to OPC lithography target layer generation should come from an inverse etching model instead of the forward etching model. These practical needs have motivated us to develop models that can transform SEM images between post lithography domain and post etch domain freely and accurately. In this paper, we will report results from our proposed image based forward etching model and the image based inverse etching model. The deep convolutional neural network (DCNN) models have achieved SEM image transformation between post lithography and post etch accurately enough for practical applications.","PeriodicalId":186529,"journal":{"name":"2021 China Semiconductor Technology International Conference (CSTIC)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 China Semiconductor Technology International Conference (CSTIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSTIC52283.2021.9461458","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In semiconductor manufacturing, a forward etching process model that can accurately predict the pattern transformation from post-lithography (ADI) to post-etch (AEI) is greatly desired. However, current etching model is etch bias based, it is unable to offer rich information as the SEM image does for engineers to do wafer disposition. In practice, ambiguous circumstances often arise when a disposition decision cannot be made easily by examining the post-lithography pattern image alone. The probability of making the disposition decision correctly can be greatly enhanced if the post etch image can be predicted accurately. Likewise, an inverse etching model that can predict post lithography pattern SEM image from post-etch pattern SEM image is also desired for OPC lithography target layer definition. Current OPC lithography target layer is derived from etch bias estimation from a forward etching model. The rigorous solution to OPC lithography target layer generation should come from an inverse etching model instead of the forward etching model. These practical needs have motivated us to develop models that can transform SEM images between post lithography domain and post etch domain freely and accurately. In this paper, we will report results from our proposed image based forward etching model and the image based inverse etching model. The deep convolutional neural network (DCNN) models have achieved SEM image transformation between post lithography and post etch accurately enough for practical applications.
光刻域与蚀刻域之间的SEM图像变换
在半导体制造中,迫切需要一种能准确预测从后光刻(ADI)到后蚀刻(AEI)模式转换的前向蚀刻过程模型。然而,目前的蚀刻模型是基于蚀刻偏差的,它不能像扫描电镜图像那样为工程师提供丰富的信息来进行晶圆配置。在实践中,当不能通过单独检查光刻后图案图像轻松地作出处置决定时,往往会出现模棱两可的情况。如果能准确地预测刻蚀后的图像,可以大大提高正确配置决策的概率。同样,OPC光刻目标层定义也需要一种逆蚀刻模型,该模型可以从蚀刻后模式SEM图像预测光刻后模式SEM图像。当前OPC光刻目标层是由正向蚀刻模型的蚀刻偏差估计得到的。OPC光刻目标层生成的严格解决方案应该是逆刻蚀模型,而不是正刻蚀模型。这些实际需求促使我们开发能够在光刻后域和蚀刻后域之间自由准确地转换SEM图像的模型。在本文中,我们将报告我们提出的基于图像的正演刻蚀模型和基于图像的逆刻蚀模型的结果。深度卷积神经网络(DCNN)模型能够较准确地实现后光刻和后蚀刻之间的扫描电镜图像转换。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信