# A Physics-Consistent Framework for Semiconductor Device Reliability Including Multiple Degradation Mechanisms

## TL;DR

This paper introduces a new framework for assessing semiconductor device reliability by considering multiple degradation mechanisms together, improving accuracy in predicting device lifetimes.

## Contribution

A physics-consistent framework is introduced that integrates multiple degradation mechanisms and improves lifetime prediction accuracy.

## Key findings

- The framework separates physical degradation processes from analytical models, enabling clearer interpretation of degradation data.
- Common data interpretation practices can lead to systematic errors when sublinear kinetics are present.
- A reformulated analytical model improves lifetime extraction while aligning with standard reliability theory.

## Abstract

Reliability assessment of semiconductor devices increasingly requires the consideration of multiple degradation mechanisms acting simultaneously over long stress durations. Conventional lifetime qualification and prediction approaches rely on simplified assumptions that can obscure the interpretation of measured degradation data and lead to large uncertainty when extrapolated over many orders of magnitude in time. A consistent analytical framework is therefore required to relate measured degradation behavior to meaningful reliability metrics. This work presents a general framework for semiconductor device reliability that is consistent with established reliability theory and explicitly accommodates multiple competing degradation mechanisms, consistent with modern JEDEC reliability standards. The framework presented here separates physical degradation processes from analytical representations used to interpret experimental data, allowing the effect of independent mechanisms to be combined without imposing an implied physical model. Degradation behaviors exhibiting sublinear time dependence, which are commonly observed across device technologies, are discussed within this context. We show that common data interpretation practices can introduce systematic errors when ssublinearkinetics are present, particularly regarding lifetime extrapolation. A reformulated analytical representation is introduced that improves clarity and robustness in lifetime extraction while remaining fully compatible with standard reliability theory. This framework supports more consistent reliability assessment and more credible lifetime prediction across materials, devices, and operating conditions.