Infrared analysis for counterfeit electronic parts detection and supply chain validation

Within the electronics industry, counterfeit electronic components entering the supply chain have steadily become an increasing threat accounting for more than 8 % of global merchandise trade and an annual $600 billion enterprise. Currently, there are not many cost-effective and nonintrusive solutions for counterfeit detection of electronic parts. In this paper, the authors present a statistical approach for detecting counterfeit components based on infrared (IR) analysis by the use of independent component analysis (ICA). As a prominent higher-order statistical analysis technique, ICA is capable of extracting relevant features from IR data. The latest applications and the extended algorithms of ICA have been elucidated for the purposes of classification and identification of counterfeit electronic parts. The theoretical framework of ICA is presented along with extensive experimental results to illustrate its feature extraction function in counterfeit electronic parts detection.