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A Thermodynamic Framework for Reliability Kinetics
1Department of Electrical and Electronic Engineering, Ariel University, Ariel 40700, Israel.
This study develops a thermodynamic framework for reliability kinetics, unifying power-law degradation across various failure mechanisms. It introduces parameters for stress and degradation correlation, enhancing lifetime prediction models.
Area of Science:
- Materials Science
- Physics
- Engineering
Background:
- Power-law relationships are crucial for modeling degradation kinetics and predicting device lifetime in reliability physics.
- These relationships apply to diverse failure mechanisms like time-dependent dielectric breakdown (TDDB), hot-carrier injection (HCI), bias temperature instability (BTI), electromigration (EM), and fatigue.
Purpose of the Study:
- To develop a unified thermodynamic framework for reliability kinetics.
- To generalize kinetic equations by incorporating thermal activation, stress acceleration, and accumulated degradation.
- To introduce degradation correlation as a key descriptor for reliability modeling.
Main Methods:
- Derivation of a generalized kinetic equation from Gibbs free energy and entropy partitioning.
- Introduction of a stress coefficient (γ) and a correlation coefficient (χ).
- Interpretation of established reliability mechanisms within the new thermodynamic framework.
Main Results:
- The framework successfully interprets various degradation behaviors: TDDB (independent), HCI (self-limiting), BTI (strongly self-limiting), and fatigue (self-amplifying).
- Electromigration highlights the role of stress acceleration (γ).
- The correlation coefficient (χ) quantifies the influence of accumulated degradation on subsequent degradation.
Conclusions:
- The proposed thermodynamic framework offers a unified interpretation of empirical power-law degradation kinetics.
- Degradation correlation (χ) is identified as a vital complementary descriptor for accurate reliability modeling and lifetime prediction.
- This approach enhances the understanding and prediction of material degradation across multiple failure modes.
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