# Failure-Mechanism Transferability of Cumulative-Damage Features for Health State Estimation of SiC Power Modules

arXiv: https://arxiv.org/abs/2608.08365 · HTML: https://mlsanity.com/paper/2608.08365/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 8 Aug 2026.

## Builds on (most cited first, 4 of 4)

- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Comparative analysis and evaluation of ageing forecasting methods for semiconductor devices in online health monitoring](https://mlsanity.com/paper/2503.20403/) (2025)
- [Data-driven Power Loss Identification through Physics-Based Thermal Model Backpropagation](https://mlsanity.com/paper/2504.00133/) (2025)

## Abstract (arXiv)

Data-driven health-state estimators for SiC (Silica-Carbide) power modules typically report their performance on a single accelerated-aging campaign, and how that performance transfers to a different failure mechanism is rarely tested. We benchmark five reference methods from the prognostics and condition-monitoring literature against a physics-informed NODE (Neural Ordinary Differential Equation) on two SiC power-cycling campaigns driven by structurally different failure mechanisms, solder-layer fatigue and wire-bond lift-off, under a per-module k-fold protocol. The NODE is evaluated under two input regimes that share the rest of the pipeline: the baseline electrical precursors and a set of cumulative thermoelectric features. Every reference method degrades on the wire-bond campaign, with average errors growing and precision decreasing with respect to their performance on the soldered campaign. The NODE fed with the cumulative features keeps its soldered-campaign metrics on both mechanisms, with differences inside the fold-to-fold variance, while the same architecture fed with the baseline precursors falls back to the reference-method cluster. The input representation contributes at least as much as the architecture to failure-mechanism transferability of a health-state estimator.