Machine Learning Assisted Counterfeit IC Detection through Non-destructive Infrared (IR) Spectroscopy Material Characterization

Nowadays, counterfeit integrated circuits (IC) are increasingly common due to the continuous growth of supply chain globalization. This supply chain vulnerability results in unreliable and insecure counterfeit ICs integrated into the end-user devices in many applications, including consumer, corporate, and military domains. Various methods such as aging detection sensors, Physical Unclonable Functions (PUFs), and hardware metering have been developed to detect such counterfeits before they become integrated into critical systems. However, several complicated aspects of detection and prevention limit their use as a stopgap to the counterfeit problem. Hence, there is a critical need for novel inspection and assurance techniques that require minimal or no additional changes/modifications to the device circuit or material while remaining low-cost per sample. In this paper, the possibility of using IC packaging material characterization for counterfeit detection is proved by a preliminary material survey between the counterfeit and authentic ICs. Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFT) is used as the material characterization in this research, and the material spectrums will be utilized for training machine learning classification models. Several machine learning classification methods will be tested, such as Linear discriminant analysis (LDA), Support Vector Machine (SVM), random forest(RF), and multi-layer perceptron (MLP). With the help of the Standard Normal Variate (SNV) data preprocessing and MPL model, over 92 percent accuracy of counterfeit versus genuine sample discrimination has been achieved. This proves the existence of packaging material differences between counterfeit and authentic IC samples.