A machine learning approach to fab-of-origin attestation

We introduce a machine learning approach for distinguishing between integrated circuits fabricated in a ratified facility and circuits originating from an unknown or undesired source based on parametric measurements. Unlike earlier approaches, which seek to achieve the same objective in a general, design-independent manner, the proposed method leverages the interaction between the idiosyncrasies of the fabrication facility and a specific design, in order to create a customized fab-of-origin membership test for the circuit in question. Effectiveness of the proposed method is demonstrated using two large industrial datasets from a 65nm Texas Instruments RF transceiver manufactured in two different fabrication facilities.