Monolithic security primitive integration in self-rectifying memristors for extreme-temperature internet-of-things

Reliable hardware security primitives that withstand harsh temperatures are essential for the next-generation Internet of Things (IoT) deployed in industrial, automotive, and remote environments, where thermal stress undermines data integrity and device authentication. Conventional silicon-based true random number generators (TRNGs) and physical unclonable functions (PUFs) suffer from catastrophic performance degradation above 85 ℃. Here, we address this critical gap with a unified, temperature-agnostic security primitive based on a Pt/HfO_2-y/WO_3-x/TiN self-rectifying memristor (SRM). The device exhibits exceptional performance across an unprecedented thermal range (from -50 to 150 ℃), featuring ultrahigh rectification (>10⁶), nonlinearity (>10⁵), and stable non-volatility with >10-year data retention. By harnessing intrinsic random telegraph noise (RTN) and stochastic conductance decay dynamics, we monolithically integrate TRNG and PUF functionalities on a single 32 × 32 crossbar array. The TRNG demonstrates resilience against machine learning attacks, maintaining near-ideal prediction accuracy at both 150 ℃ and -50 ℃. Simultaneously, the PUF achieves a record-low bit error rate of 1.859 × 10^-2% across this 200 ℃ temperature swing, outperforming all existing silicon-based solutions by a margin greater than 85 ℃.Validation against generative adversarial network attacks and tripartite authentication protocols confirms this selector-free, memristor-based approach as a pivotal advancement for secure, scalable hardware security in extreme-temperature IoT systems.