用安全主动学习加速氧化镓二极管在极端环境下的可靠性测试
Autonomous Reliability Qualification of Ga$_2$O$_3$-based diode sensors via Safe Active Learning
- 通过安全主动学习自动规划实验并实时优化模型
- 仅用800小时数据实现长达1000小时的退化趋势预测
- 适用于可实时监测安全指标的各类半导体器件
超宽禁带半导体Ga₂O₃是高功率、高温电子器件的有力候选材料。在极端工况下对这些器件进行可靠性的定性评估至关重要，但传统可靠性测试耗时过长。自主实验提供新范式，使测量规划与模型优化可并行实时演进。本文提出安全主动学习（SAL）框架，用于在热-氢耦合应力下对Ga₂O₃基二极管传感器进行自主可靠性表征。首先在仿真中验证SAL，其能在保证安全的前提下拓展探索区域，同时学习动态整流特性表面；其次在自动化高温探针台平台上实测了Pt/Cr₂O₃:Mg/β-Ga₂O₃二极管传感器，覆盖0–800 ppm H₂和350–550 °C条件；最后利用SAL生成的数据集，结合结构化高斯过程模型，对目标电压下的二极管电流进行长期预测。其条件依赖的Kohlrausch–Williams–Watts均值与残差协方差核通过人工智能辅助设计，基于1000小时400 °C下多氢浓度的辅助验证数据集完成调参与验证，成功捕捉了长期饱和退化趋势。尽管本研究聚焦于整流型Ga₂O₃二极管，但只要存在可原位测量的安全可观测量，SAL即可推广至其他器件类别。
原文摘要 · Abstract (English)
Ultra-wide bandgap (UWBG) Ga$_2$O$_3$ 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$_2$O$_3$-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$_2$O$_3$:Mg/$β$-Ga$_2$O$_3$ 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$_2$O$_3$-based diode, SAL is applicable to other device classes whenever a suitable safety observable can be measured in situ.
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