Operando thermal behaviour of transistor-integrated memristors and its implications on online and offline learning

The influence of in situ temperature on the electrical characteristics of memristors is a critical consideration for the reliable deployment of neuromorphic computing systems. This study investigates the operando thermal behaviour of a 1T-1R (1 Transistor-1 Resistor) Ta₂O₅-based memristive device operated at temperatures up to 150 °C, focusing on real-time read-margin changes rather than long-term retention degradation. The demonstrated device exhibited stable potentiation and depression (P/D) behaviour over 2M consecutive pulses without significant degradation, confirming its robustness for thermal stress analysis. Pulse-based measurements revealed distinct temperature-dependent behaviours of the transistor and memristor components. While the transistor current consistently decreased with increasing temperature due to enhanced carrier scattering, the integrated 1T-1R configuration exhibited a unique response: the high-conductance state (HCS) current decreased from metallic-like filamentary conduction, whereas the low-conductance state (LCS) current increased due to thermally activated hopping conduction. Consequently, the dynamic range of conductance compressed from ∼17× at 25 °C to ∼5× at 150 °C, highlighting the severity of thermal-induced read margin shrinkage. Neural network simulations demonstrated that this compression results in a degradation in accuracy of more than 6% in simple classification tasks, such as MNIST, and up to 2.7% and 4% degradation in offline training for the MNIST and Fashion-MNIST datasets, respectively, compared to the ideal memristor. A scaling-based compensation model was proposed to restore the effective conductance range, thereby recovering the inference accuracy at elevated temperatures. These findings highlight a universal thermal interaction challenge in 1T-1R RRAM architectures and establish a quantitative framework for evaluating and mitigating its impact on neuromorphic system reliability.