We present a Safe Active Learning (SAL) framework for autonomous reliability characterization of rectifying Ga$_2$O$_3$-based devices under coupled thermal and hydrogen stress. SAL treats rectification as a device-physics-motivated safety observable and models its evolution over elapsed time, temperature, and H$_2$ concentration using a Gaussian-process surrogate. To handle condition-dependent and uncertain experiment durations, the method combines an adaptive completion-time window, time-window lower-confidence-bound safety checks, a trust region anchored to previously verified safe conditions, and a two-phase strategy that transitions from conservative safe exploration to progressively relaxed rectification targets as the device degrades. We first evaluate SAL in simulation, where it safely expands the explored region while learning the evolving rectification surface. We then demonstrate SAL experimentally on an automated high-temperature probe-station platform using a Pt/Cr$_2$O$_3$:Mg/$β$-Ga$_2$O$_3$ device. In the reported campaign, phase 1 incurred only one unsafe measurement associated with spurious current-voltage sweeps, while phase 2 intentionally probed lower-rectification regimes. Finally, we use the curated SAL dataset for offline long-horizon forecasting of device response at a target voltage using a structured Gaussian-process model with a condition-dependent Kohlrausch--Williams--Watts mean and a residual covariance kernel. The model captures long-time, saturating degradation trends in an auxiliary validation dataset, illustrating how safety-aware autonomous experimentation enables both conservative characterization and subsequent degradation modeling. Although demonstrated here for a rectifying Ga$_2$O$_3$ device, SAL is applicable to other systems where a measurable in situ safety observable can be defined.

翻译：我们提出一种安全主动学习（SAL）框架，用于在热与氢共同应力作用下，对整流型Ga$_2$O$_3$基器件进行自主可靠性表征。SAL将整流特性视为受器件物理学启发的安全性可观测变量，并利用高斯过程代理模型对其随时间、温度及H$_2$浓度的演变过程进行建模。为处理条件依赖且时长不确定的实验进程，该方法结合了自适应完成时间窗口、基于时间窗口下置信区间的安全校验、锚定于先前已验证安全条件的置信域，以及一个两阶段策略——该策略从保守的安全探索过渡到随器件退化而逐步放宽的整流目标。我们首先在仿真中评估SAL，结果表明该方法在安全地拓展探索区域的同时，能够学习不断演变的整流曲面。随后，我们利用Pt/Cr$_2$O$_3$:Mg/$β$-Ga$_2$O$_3$器件，在自动化高温探针台平台上进行了SAL实验验证。在所报告的实验中，第一阶段仅发生一次与异常电流-电压扫描相关的不安全测量，而第二阶段则有目的地探测了低整流区。最后，我们利用整理后的SAL数据集，采用结构化高斯过程模型（该模型包含条件依赖的Kohlrausch–Williams–Watts均值函数及残差协方差核），在目标电压下对器件响应进行离线长时程预测。该模型在辅助验证数据集中成功捕捉到长时间尺度的饱和退化趋势，从而展示了安全感知的自主实验如何实现保守表征与后续退化建模的双重目标。尽管本文以整流型Ga$_2$O$_3$器件为例进行验证，但SAL方法适用于任何能够定义可测量的原位安全性可观测变量的系统。