Abstract
Memristive crossbar Physical Unclonable Function (PUF) structures are emerging as strong security primitives for resource-constrained devices demanding good retention time, negligible standby power, small size, and ultra-low power operating requirements. Memristive PUF exploits the inherent high process variations of a memristor as a source of entropy to generate device-specific signatures. These PUFs need to be strong enough to deal with active and passive attacks as well as machine learning attacks, hence requires more device-to-device variability. Memristive PUF requires dense crossbar architecture to generate unique, uniform, and reliable device signatures. Dense memristive crossbars (Xbar) face the challenges of low noise margin, proper load resistance selection, scalability, and a precise sense circuitry at the load resistance side to read the resistive state of a memristor accurately. In this work, we have simulated and optimized the load resistance of memristive scaled up crossbar arrays. We have used two of our fabricated devices for memristive crossbar PUF simulation. The proposed crossbar PUF architecture satisfies the basic PUF evaluation metrics and improves noise margin (NM). The load resistance is optimized through MATLAB simulation. The impact of optimized load resistance on Xbar architecture is observed to be noticeable and around 18% improvement in the noise margin was observed when the crossbar is scaled up from 16×2 to 128×2.
Highlights
Smart remote Internet of Thing (IoT) devices and sensors suffer from eavesdropping, side channel, man in the middle and machine learning attacks
When a challenge is applied than due to inverted challenge half of the column memristors will be in set (LRS) state while remaining half will be in reset (HRS) state
For Physical Unclonable Function (PUF) P1 and P2 implemented with memristors M1 and M2, it is observed that as the Xbar size increases, the reliability which is below 90% for 4 × 2 Xbar size, increases to above 95% values
Summary
Smart remote Internet of Thing (IoT) devices and sensors suffer from eavesdropping, side channel, man in the middle and machine learning attacks. A general framework of logical and filter based approximation attacks on arbiter PUFs is discussed in [20] To counter these Machine Learning based approximation attacks, a large crossbar size capable of generating large CRP space or some challenge obfuscation strategy [21] is needed. It was analyzed in [18] that if more number of rows are added in the memristive crossbar the voltage resolution at the load resistance will be finer, requiring precise sense circuitry.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
