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

CASIA OpenIR > 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, Wei1,3 ; Zhao, Fazhan 1,3; Zhao, Kun2,3 ; Ma, Hongtu2,3 ; Li, Qing 1,3
发表期刊	INTEGRATION-THE VLSI JOURNAL
ISSN	0167-9260
	2024-03-01
卷号	95 页码:9
通讯作者	Xiao, Wei(xiaowei@ime.ac.cn) ; Li, Qing(liqing@ime.ac.cn)
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.
关键词	Scanning electron microscopy Deep learning Hardware trust and assurance Integrated circuit
DOI	10.1016/j.vlsi.2023.102111
关键词[WOS]	NEURAL-NETWORK
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2021YFB3100904] ; SMIC (Semiconductor Manufacturing International Corporation)
项目资助者	National Key Research and Development Program of China ; SMIC (Semiconductor Manufacturing International Corporation)
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic
WOS记录号	WOS:001144115400001
出版者	ELSEVIER
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/54775
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.