Autonomous Reliability Qualification of Ga$_2$O$_3$-based diode sensors via Safe Active Learning

Ultra-wide bandgap (UWBG) Ga_2O_3 is a promising semiconductor for high-power and high-temperature electronics. Reliable qualification of these devices under extreme operating conditions is essential, yet conventional reliability testing is inherently time-consuming. Autonomous experimentation offers a new paradigm by enabling measurement planning and model refinement to evolve in parallel in real time. We present a Safe Active Learning (SAL) framework for autonomous reliability characterization of Ga_2O_3-based diode sensors under coupled thermal and hydrogen stress. We first evaluate SAL in simulation, where it safely expands the explored region while learning the evolving rectification surface. Second, we demonstrate SAL experimentally on an automated high-temperature probe-station platform using a Pt/Cr_2O_3:Mg/β-Ga_2O_3 diode sensor of H_2 and temperature, spanning 0-800 ppm H_2 and 350-550 °C. Finally, we use the SAL-generated dataset for offline long-horizon forecasting of the diode current at a target voltage with a structured Gaussian-process model. Its condition-dependent Kohlrausch--Williams--Watts mean and residual covariance kernel were engineered with artificial-intelligence assistance using the SAL data and an auxiliary validation dataset spanning 1,000 hours at 400 °C across multiple H_2 concentrations. This dataset guided kernel design and validation, and the resulting model captures its long-time, saturating degradation trends. Although demonstrated here for a rectifying Ga_2O_3-based diode, SAL is applicable to other device classes whenever a suitable safety observable can be measured in situ.