Stochastic first-passage modeling of single-event burnout in SiC power MOSFETs

TL;DR

This paper presents a stochastic first-passage model for single-event burnout in SiC power MOSFETs, revealing how noise broadens deterministic thresholds into probabilistic transition bands and distinguishing different failure regimes.

Contribution

It introduces a novel electrothermal feedback-relaxation model incorporating stochastic effects to explain threshold broadening and failure probabilities in SiC MOSFET burnout.

Findings

Finite fluctuations broaden burnout thresholds into probabilistic bands.

Noise can induce subthreshold runaway despite recoverable conditions.

Time-resolved distributions distinguish rapid feedback failure from stochastic delays.

Abstract

Single-event burnout (SEB) in silicon carbide (SiC) power MOSFETs is often characterized by deterministic threshold quantities. Near the boundary between recovery and runaway, stochastic variability can make this threshold description probabilistic rather than sharp. This work introduces a first-passage perspective for stochastic threshold broadening in burnout. The process is described by a reduced electrothermal feedback-relaxation model with an absorbing boundary. The model combines carrier multiplication, avalanche feedback, localized heating, carrier loss, and thermal relaxation. Stochastic carrier and thermal terms represent unresolved event-level variability. The main finding is that finite fluctuations broaden the deterministic burnout threshold into a probabilistic transition band. Noise-induced subthreshold runaway also emerges, where nominally recoverable conditions can still fail through rare stochastic excursions. First-passage-time distributions resolve the time scale of burnout and survival probabilities further distinguish rapid feedback-dominated runaway from delayed stochastic failure. A feedback-relaxation phase diagram organizes recoverable, probabilistic, and rapidly unstable regimes. This framework provides a statistical-physics interpretation of threshold dispersion in single-event burnout of SiC power MOSFETs by linking coarse-grained electrothermal dynamics to probabilistic and time-resolved failure observables.