Evaluating the Efficacy of the ResCav System in Distinguishing Integrated Circuits Across Manufacturers, Series, and Lots

The integrity and reliability of integrated circuits (ICs) are critical for the performance of modern electronic systems. Traditional methods for IC authentication, such as external visual inspection and X-ray imaging, have limitations in speed, accuracy, cost, and required expertise. This study evaluates the effectiveness of the resonant cavity system (ResCav System) in distinguishing ICs across different manufacturers, family series, and lots. The study specifically examines LM324 series operational amplifiers from Texas Instruments (TI) and STMicroelectronics (STM). Various machine learning techniques, including supervised learning and novelty detection, are employed to analyze electromagnetic signatures obtained from the ResCav System. Additionally, image processing techniques are applied to optical and X-ray images for visual validation of the detected differences. The results show that the ResCav System, combined with machine learning algorithms, provides high accuracy in differentiating ICs based on manufacturer and family series, while novelty detection proves effective for identifying ICs with new or unforeseen variations. However, the system’s ability to distinguish ICs from different lots remains limited, highlighting the need for further optimization.