A Robust Area-Efficient Physically Unclonable Function with High Machine Learning Attack Resilience in 28-nm CMOS

Lai Y.-CYao C.-YYang S.-HWu Y.-WTSUNG-TE LIU2022-04-252022-04-25202215498328https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112604366&doi=10.1109%2fTCSI.2021.3098018&partnerID=40&md5=19e258f7f339aa84202036a616be17b3https://scholars.lib.ntu.edu.tw/handle/123456789/607328Strong physically unclonable function (PUF) offers a promising solution to low-cost hardware identification and authentication for Internet of Things (IoT) applications. The continuous advancement of machine learning (ML) technology makes the PUF resilience to ML attacks a major design priority. This paper presents a robust and area-efficient strong PUF design with high ML attack resilience. The proposed PUF architecture based on inverter amplifiers operating in the subthreshold region achieves both low energy consumption and high supply and temperature scalability. The proposed nonlinearity topology effectively enhances PUF resilience to various ML attacks with low implementation area and cost. The proposed strong PUF design was designed and implemented using a 28-nm CMOS process. The measurement results show that the proposed PUF design achieves a nearly ideal ML attack resilience of 49.96 % with a small area of 239,857 F2, and demonstrates a stable operation across a wide range of supply voltage from 0.5-1.4 V and temperature from -40-100 °C. This represents $3\times $ improvement in area efficiency, $2.25\times $ and $1.08\times $ improvement in operating voltage and temperature range, respectively, compared to the state-of-the-art results. ? 2004-2012 IEEE.Authenticationhardware securityidentificationInternet of things (IoT)machine learning (ML)ML resiliencephysically unclonable function (PUF)process variationstrong PUFCMOS integrated circuitsEnergy utilizationInternet of thingsMachine learningArchitecture-basedAttack resiliencesInternet of Things (IOT)Inverter amplifiersLow energy consumptionPhysically unclonable functionsSub-threshold regionsTemperature rangeIntegrated circuit design[SDGs]SDG7A Robust Area-Efficient Physically Unclonable Function with High Machine Learning Attack Resilience in 28-nm CMOSjournal article10.1109/TCSI.2021.30980182-s2.0-85112604366