Exploiting Nanoelectronic Properties of Memory Chips for Prevention of IC Counterfeiting
This addresses the issue of counterfeit memory chips for electronics manufacturers and users, representing an incremental improvement in anticounterfeiting techniques.
This study tackles the problem of integrated circuit counterfeiting by developing a methodology to detect the origin, recycled status, and used locations in non-volatile memory chips, achieving 95.1% accuracy in identifying manufacturers on a test dataset with 9 types of chips.
This study presents a methodology for anticounterfeiting of Non-Volatile Memory (NVM) chips. In particular, we experimentally demonstrate a generalized methodology for detecting (i) Integrated Circuit (IC) origin, (ii) recycled or used NVM chips, and (iii) identification of used locations (addresses) in the chip. Our proposed methodology inspects latency and variability signatures of Commercial-Off-The-Shelf (COTS) NVM chips. The proposed technique requires low-cycle (~100) pre-conditioning and utilizes Machine Learning (ML) algorithms. We observe different trends in evolution of latency (sector erase or page write) with cycling on different NVM technologies from different vendors. ML assisted approach is utilized for detecting IC manufacturers with 95.1 % accuracy obtained on prepared test dataset consisting of 3 different NVM technologies including 6 different manufacturers (9 types of chips).