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TA-denseNet: Efficient hardware trust and assurance model based on feature extraction and comparison of SEM images and GDSII images

文献类型：期刊论文

作者Xiao, Wei1,3; Zhao, Fazhan1,3; Zhao, Kun2,3; Ma, Hongtu2,3; Li, Qing1,3
刊名INTEGRATION-THE VLSI JOURNAL
出版日期2024-03-01
卷号95页码:9
ISSN号0167-9260
关键词Scanning electron microscopy Deep learning Hardware trust and assurance Integrated circuit
DOI10.1016/j.vlsi.2023.102111
通讯作者Xiao, Wei(xiaowei@ime.ac.cn) ; Li, Qing(liqing@ime.ac.cn)
英文摘要Hardware trust and assurance, which relies on extracting and analyzing information from GDSII images and SEM images of integrated circuits, plays a critical role in ensuring the integrity, privacy, security, and functionality of integrated circuits. However, with the continuous improvement of integrated circuits integration, traditional approaches for hardware trust and assurance face great challenges owing to the low efficiency, low precision, and high cost. To solve the issue mentioned above, we propose a novel, automatic, and efficient deep learning-based method, called TA-denseNet, which directly learns the mapping from GDSII images and SEM images to the compact Euclidean space, where the distances between GDSII images and SEM images directly correspond to measures of similarity. Also, a new hardware trust and assurance dataset for model training and evaluation, named TA-dataset, is proposed. Finally, we evaluate the accuracy of the proposed TA-denseNet model in simi-larity measures between GDSII images and SEM images on the test sets of the proposed dataset and obtain an accuracy of over 99.83%, which shows that the TA-denseNet model proposed in this paper has good performance on direct feature extraction and comparison between GDSII images and SEM images.
WOS关键词NEURAL-NETWORK
资助项目National Key Research and Development Program of China[2021YFB3100904] ; SMIC (Semiconductor Manufacturing International Corporation)
WOS研究方向Computer Science ; Engineering
语种英语
出版者ELSEVIER
WOS记录号WOS:001144115400001
资助机构National Key Research and Development Program of China ; SMIC (Semiconductor Manufacturing International Corporation)
源URL[http://ir.ia.ac.cn/handle/173211/54774]  
专题脑图谱与类脑智能实验室
通讯作者Xiao, Wei; Li, Qing
作者单位1.Chinese Acad Sci, Inst Microelect, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Xiao, Wei,Zhao, Fazhan,Zhao, Kun,et al. TA-denseNet: Efficient hardware trust and assurance model based on feature extraction and comparison of SEM images and GDSII images[J]. INTEGRATION-THE VLSI JOURNAL,2024,95:9.
APA Xiao, Wei,Zhao, Fazhan,Zhao, Kun,Ma, Hongtu,&Li, Qing.(2024).TA-denseNet: Efficient hardware trust and assurance model based on feature extraction and comparison of SEM images and GDSII images.INTEGRATION-THE VLSI JOURNAL,95,9.
MLA Xiao, Wei,et al."TA-denseNet: Efficient hardware trust and assurance model based on feature extraction and comparison of SEM images and GDSII images".INTEGRATION-THE VLSI JOURNAL 95(2024):9.

入库方式： OAI收割

来源：自动化研究所

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