A high reliability PUF using hot carrier injection based response reinforcement

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Abstract

Achieving high reliability across environmental variations and over aging in physical unclonable functions (PUFs) remains a challenge for PUF designers. The conventional method to improve PUF reliability is to use powerful error correction codes (ECC) to correct the errors in the raw response from the PUF core. Unfortunately, these ECC blocks generally have high VLSI overheads, which scale up quickly with the error correction capability. Alternately, researchers have proposed techniques to increase the reliability of the PUF core, and thus significantly reduce the required strength (and complexity) of the ECC. One method of increasing the reliability of the PUF core is to use normally detrimental IC aging effects to reinforce the desired (or "golden") response of the PUF by altering the PUF circuit characteristics permanently and hence making the PUF more reliable. In this work, we present a PUF response reinforcement technique based on hot carrier injection (HCI) which can reinforce the PUF golden response in short stress times (i.e., tens of seconds), without impacting the surrounding circuits, and that has high permanence (i.e., does not degrade significantly over aging). We present a self-contained HCI-reinforcement-enabled PUF circuit based on sense amplifiers (SA) which autonomously self-reinforces with minimal external intervention. We have fabricated a custom ASIC testchip in 65nm bulk CMOS with the proposed PUF design. Measured results show high reliability across environmental variations and accelerated aging, as well as good uniqueness and randomness. For example, 1600 SA elements, after being HCI stressed for 125s, show 100% reliability (zero errors) across ±20% voltage variations a temperature range of -20°C to 85°C. © 2013 Springer-Verlag Berlin Heidelberg.

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APA

Bhargava, M., & Mai, K. (2013). A high reliability PUF using hot carrier injection based response reinforcement. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8086 LNCS, pp. 90–106). Springer Verlag. https://doi.org/10.1007/978-3-642-40349-1_6

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