Supervised Feature Extraction and Synthesis of Integrated Circuits Micrographs for Physical Assurance

Md. Mahfuz Al Hasan, Md. Tahsin Mostafiz, N. Asadizanjani
{"title":"Supervised Feature Extraction and Synthesis of Integrated Circuits Micrographs for Physical Assurance","authors":"Md. Mahfuz Al Hasan, Md. Tahsin Mostafiz, N. Asadizanjani","doi":"10.31399/asm.edfa.2022-3.p012","DOIUrl":null,"url":null,"abstract":"\n This article proposes a design for a real-time Trojan detection system and explores possible solutions to the challenge of large-scale SEM image acquisition. One such solution, a deep-learning approach that generates synthetic micrographs from layout images, shows significant promise. Learning-based approaches are also used to both synthesize and classify cells. The classification outcome is matched with the design exchange format file entry to ensure the purity of the underlying IC.","PeriodicalId":431761,"journal":{"name":"EDFA Technical Articles","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EDFA Technical Articles","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31399/asm.edfa.2022-3.p012","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

This article proposes a design for a real-time Trojan detection system and explores possible solutions to the challenge of large-scale SEM image acquisition. One such solution, a deep-learning approach that generates synthetic micrographs from layout images, shows significant promise. Learning-based approaches are also used to both synthesize and classify cells. The classification outcome is matched with the design exchange format file entry to ensure the purity of the underlying IC.
用于物理保证的集成电路显微照片的监督特征提取和合成
本文提出了一种实时木马检测系统的设计,并探讨了大规模扫描电镜图像采集挑战的可能解决方案。其中一个解决方案是一种深度学习方法,可以从布局图像中生成合成显微照片,这显示出了很大的前景。基于学习的方法也用于细胞的合成和分类。分类结果与设计交换格式文件条目相匹配,以确保底层IC的纯度。
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:481959085
Book学术官方微信