The use of side-channel parametric measurements along with statistical analysis methods for detecting hardware Trojans in fabricated integrated circuits has been studied extensively in recent years, initially for digital designs but recently also for their analog/RF counterparts. Such post-fabrication trust evaluation methods, however, are unable to detect dormant hardware Trojans which are activated after a circuit is deployed in its field of operation. For the latter, an on-chip trust evaluation method is required. To this end, we present a general architecture for post-deployment trust evaluation based on on-chip classifiers. Specifically, we discuss the design of an on-chip analog neural network which can be trained to distinguish trusted from untrusted circuit functionality based on simple measurements obtained via on-chip measurement acquisition sensors. The proposed method is demonstrated using a Trojan-free and two Trojan-infested variants of a wireless cryptographic IC design, as well as a fabricated programmable neural network experimentation chip. As corroborated by the obtained experimental results, two current measurements suffice for the on-chip classifier to effectively assess trustworthiness and, thereby, detect hardware Trojans that are activated after chip deployment.
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