Optimization of Memristive Crossbar Array for Physical Unclonable Function Applications

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 $16\times 2$ to $128\times 2$ .

Keywords

• Memristor
• PUF
• memristive crossbar
• hardware security
• noise margin
• IoT security