Machine Learning Applied to Counterfeit Detection

The machine learning for counterfeit detection research program commences with a study evaluating the feasibility of applying machine learning to detect FPGA counterfeits. The underlying process will be broadly applicable throughout the supply chain. Using a proprietary non-destructive means of gathering data from FPGA devices, we will then make use of the data in a variety of machine learning algorithms to determine the most efficient and accurate methodology for genuine/counterfeit indication using the provided data. Lastly, an exploration into the supply chain and deployment strategies will develop a business case for full implementation of the recommended toolset. Ultimately, this research program will study the feasibility of developing a mechanism for counterfeit detection that is readily deployable and applicable at any stage of product life.