Impacts of Machine Learning on Counterfeit IC Detection and Avoidance Techniques

Abstract

Globalization of integrated circuit (IC) supply chain has made counterfeiting a major source of concern in the semiconductor industry. To address this concern, extensive efforts have been put into developing effective counterfeit detection and avoidance techniques. In the recent years, machine learning (ML) algorithms have played an important role in development and evaluation of many emerging countermeasures against counterfeiting. In this paper, we aim to investigate impacts of such algorithms on the landscape of anti-counterfeiting schemes. We provide a comprehensive review of prior arts that deploy machine learning to develop or attack counterfeit detection and avoidance techniques. We also discuss future directions for application of machine learning in anti-counterfeit schemes.