Real-Time Prediction for IC Aging Based on Machine Learning

• Published in: IEEE transactions on instrumentation and measurement Vol. 68; no. 12; pp. 4756 - 4764
• Main Authors: Huang, Ke, Zhang, Xinqiao, Karimi, Naghmeh
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aging; Artificial intelligence; Degradation; Equivalent aging time; hot carrier injection (HCI); Integrated circuit modeling; Integrated circuits; Machine learning; negative/positive bias temperature instability (NBTI/PBTI); Prediction models; Predictive models; Real time; real-time IC aging prediction; Stress; Transistors; Workload
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2019.2899477

Estimating the aging-related degradation and failure of nanoscale integrated circuits (ICs), before they actually occur, is crucial for developing aging prevention/mitigation actions and in turn avoiding unexpected in-field circuit failures. Real-time monitoring of IC operating conditions can be efficiently used for predicting aging degradation and in turn timing failures caused by device aging. The existing approaches only take some specific operating conditions (e.g., workload or temperature) into account. In this paper, we propose a novel method for real-time IC aging prediction by extending the prediction schemes to a comprehensive model which takes into account any time-variant dynamic operating conditions relevant to aging prediction. Using a machine learning prediction model and the notion of equivalent aging time, we show that our approach outperforms the existing methods in terms of aging-prediction accuracy under different scenarios of time-variant operating conditions.