EMFORCED: EM-Based Fingerprinting Framework for Remarked and Cloned Counterfeit IC Detection Using Machine Learning Classification
Andrew Stern, Ulbert J. Botero, Fahim Rahman, Domenic J. Forte, Mark Mohammad Tehranipoor · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2019
Electronics supply chain vulnerabilities have broadened in scope over the past two decades. With nearly all integrated circuit (IC) design companies relinquishing their fabrication, packaging, and test facilities, they are forced to rely upon companies from around the world to produce their ICs. This dependence leaves the electronics supply chain open to counterfeiting activities. In this article, we propose an electromagnetic (EM)-based fingerprinting framework, called EMFORCED, to detect remarked and cloned counterfeit ICs. Here, we demonstrate the benefits of using naturally occurring EM side channels to identify the IC design layout without decapsulating the chip under test. Enabling only the clock, Vdd, and ground pins allows us to generate a design-specific fingerprint that is dependent upon the physical parameters of the chip under test. EMFORCED leverages the EM emissions from the clock distribution network to create a holistic, design-level, fingerprint, including both temporal information and spatial information. We utilize the fingerprint information of functionally similar 8051-series microprocessors from three vendors and perform unsupervised (principal component analysis) and supervised (linear discriminant analysis) machine learning methods on all ICs to determine their intravendor and intervendor similarities. We acquired ICs from multiple dates and lot codes along with variants acquired from the gray market and analyzed them for authenticity using physical inspection and X-ray tomography. Statistical analysis and machine learning techniques are used to demonstrate the reference-free and reference-inclusive classification methods based on EMFORCED measurements. We demonstrate the classification accuracies of 99.46% and 100% for unsupervised and supervised approaches, respectively.