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

doi:10.1016/j.vlsi.2023.102111

CORC > 自动化研究所 > 中国科学院自动化研究所 > 脑图谱与类脑智能实验室

	TA-denseNet: Efficient hardware trust and assurance model based on feature extraction and comparison of SEM images and GDSII images
	Xiao, Wei 1,3; Zhao, Fazhan 1,3; Zhao, Kun 2,3; Ma, Hongtu 2,3; Li, Qing 1,3
刊名	INTEGRATION-THE VLSI JOURNAL
	2024-03-01
卷号	95 页码:9
关键词	Scanning electron microscopy Deep learning Hardware trust and assurance Integrated circuit
ISSN号	0167-9260
DOI	10.1016/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.
资助项目	National Key Research and Development Program of China[2021YFB3100904] ; SMIC (Semiconductor Manufacturing International Corporation)
WOS关键词	NEURAL-NETWORK
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)
内容类型	期刊论文
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.