EMFORCED: EM-based Fingerprinting Framework for Counterfeit Detection with Demonstration on Remarked and Cloned ICs
Andrew Stern, Ulbert J. Botero, Bicky Shakya, Haoting Shen, Domenic J. Forte, Mark Mohammad Tehranipoor · 2018
Today’s globalized electronics supply chain is prone to counterfeit chip proliferation. Existing techniques to detect counterfeit integrated circuits (ICs) are limited by relatively high cost, lengthy inspection time, destructive nature, and restriction to a pre-packaging environment. We propose a novel method of counterfeit IC detection which takes advantage of design-specific electromagnetic (EM) fingerprints generated by simulating on-chip clock distribution networks. Through exploitation of the chip’s physical characteristics, our technique can help detect foundry of origin. We validate our approach on 8051 microcontrollers from three different vendors and utilize principal component analysis to distinguish the acquisitions by vendor. Our results show that near-field EM measurements combined with unsupervised machine learning provide ≈ 99% accuracy in counterfeit detection through design-specific fingerprint classification.