A Data-Driven Approach for Predicting Remaining Useful Life of Semiconductor Devices Based on Machine Learning and Synthetic Data Generation: A Review and Case Study on SiC MOSFETs

A Data-Driven Approach for Predicting Remaining Useful Life of Semiconductor Devices Based on Machine Learning and Synthetic Data Generation: A Review and Case Study on SiC MOSFETs

Predicting the remaining useful life of electronic components is a crucial aspect for predictive maintenance and system reliability across multiple fields and applications. Data-driven approaches, particularly those methods based on machine learning, are currently being used due to their ability to...

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Bibliographic Details
Main Authors: Yarens J. Yarenscruz, Fernando Castano, Alberto Villalonga, Madhav Mishra, Rodolfo E. Haber
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Machine learning
mixture density network
neural network
remaining useful life
silicon carbide metal-oxide-semiconductor field-effect transistor
synthetic data
Online Access:https://ieeexplore.ieee.org/document/11114952/
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