Counterfeit integrated circuits (ICs) continue to persist in the supply chain causing early failure in electronics that unknowingly incorporate them. With counterfeiters becoming more adept at replicating ICs, the need for systems and processes to identify counterfeit ICs has been growing in recent years. In this paper, we benchmark the resonant cavity system (ResCav) by evaluating its ability to distinguish ICs with minor circuit variations. A baseline IC group along with 5 variant groups with changes made solely to their die were examined in this paper. Using a supervised machine learning algorithm, the system was able to distinguish every group of ICs amongst each other with an average weighted precision above 90% in every comparison scenario. The system’s ability to distinguish these subtle changes means that it would be suitable when used as a system for counterfeit detection, where the detection of minor deviations is pertinent. This could ultimately lead to the creation of a rapid, precise, and non-destructive system that can screen ICs for conformance.