期刊
IMAGE SENSING TECHNOLOGIES: MATERIALS, DEVICES, SYSTEMS, AND APPLICATIONS IX
卷 12091, 期 -, 页码 -出版社
SPIE-INT SOC OPTICAL ENGINEERING
DOI: 10.1117/12.2626871
关键词
Hardware security; THz response; integrated circuits; VLSI; failure detection; convolutional neural networks; data augmentation; transfer learning
资金
- NASA [80NSSC19M0201]
- Army Research Laboratory (ARL) Multiscale Multidisciplinary Modeling of Electronic Materials (MSME) Collaborative Research Alliance (CRA) [W911NF-12-2-0023]
THz testing using deep learning models achieves high classification accuracy of 98% for distinguishing between original and damaged ICs based on the response of a modern FET acting as a terahertz detector.
THz testing has been recently proposed to identify altered or damaged ICs. This method is based on the fact that a modern field-effect transistor (FET) with a sufficiently short channel can serve as a terahertz detector. The response can be recorded while changing the THz radiation parameters and location and compared to a trusted one for classification. We measured the THz response of original and damaged ICs for classification using different Transfer Learning models as a method of deep learning. We have achieved the highest classification accuracy of 98%.
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