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).

翻译：这项研究介绍了一种非挥发性内存芯片的防伪方法,特别是,我们实验性地展示了一种用于检测(一) 集成电路(IC)来源、(二) 再循环或使用的NVM芯片和(三) 查明芯片中的用过地点(地址)的通用方法。我们提议的方法检查商用脱落-雪尔夫(COTS)NVM芯片的延缓性和变异性特征。拟议的技术要求采用低周期(~100) 预制和使用机器学习算法。我们观察到不同销售商对不同NVM技术进行循环的延缓(部门擦除或书写)的演变趋势不同趋势。ML协助方法用于检测IC制造商,其精确度为95.1%的预制测试数据集由3种不同的NVM技术组成,包括6种不同的制造商(9种芯片)。