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This research aims to explore AI and machine learning techniques for predicting semiconductor degradation and estimating remaining useful life.

July 22, 2026Open Access

The Nano Paradox: AI and Machine Learning in Predicting Semiconductor Degradation

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This research aims to explore AI and machine learning techniques for predicting semiconductor degradation and estimating remaining useful life.
Conceptual framework incorporates degradation-sensitive parameters such as temperature and voltage variations.
Multiple machine learning approaches, including random forest and LSTM, are investigated.
Simulation-assisted methodology is adopted using publicly available resources due to limited access to experimental data.
AI models effectively capture degradation trajectories under stress conditions.
Proposed framework shows potential for integrating physics-of-failure with machine learning for reliability prediction.

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Randomized trial investigates AI methods for predicting degradation in semiconductors, suggesting potential for enhanced reliability assessments.

Reshma Barik (2026) studied this question.

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Randomized trial investigates AI methods for predicting degradation in semiconductors, suggesting potential for enhanced reliability assessments.