Counterfeit FPGA Characterization and Detection with Soft Sensors and Machine Learning

With ongoing microelectronic supply chain issues, the demand for genuine field-programmable gate arrays (FPGAs) is increasing – but so is the occurrence of counterfeit devices. Frequently, devices are used, salvaged from old systems, and repackaged as new. Recycled devices represent the largest class of counterfeit devices and are becoming more rampant with ongoing supply chain challenges. Therefore, it is often necessary to test whether a device is genuine before employing it in a new system. Current methods for evaluating devices are frequently destructive allowing for only small sample testing within lots. Other methods require complex external equipment and cannot be readily deployed throughout the supply chain. Graf Research Corporation has developed a methodology for using soft sensor telemetry bitstreams to characterize an FPGA device and subsequently classify whether a device is a repackaged counterfeit via statistical and machine learning models. The new method utilizes minimal external equipment, is non-destructive, and can be employed at any point throughout the supply chain.

DISTRIBUTION A. Approved for public release; distribution unlimited. OPSEC9275.