Prognosis of NBTI aging using a machine learning scheme

Circuit aging is an important failure mechanism in nanoscale designs and is a growing concern for the reliability of future systems. Aging results in circuit performance degradation over time and the ultimate circuit failure. Among aging mechanisms, Negative-Bias Temperature Instability (NBTI) is the main limiting factor of circuits lifetime. Estimating the effect of aging-related degradation, before it actually occurs, is crucial for developing aging prevention/mitigations actions to avoid circuit failures. In this paper, we propose a general-purpose IC aging prognosis approach by considering a comprehensive set of IC operating conditions including workload, usage time and operating temperature. In addition, our model considers process variation by using a calibration technique applied at the time of manufacturing. Experimental results confirms that our model is able to accurately predict the NBTI-related path delay degradation under various operating conditions. The proposed model is robust to process variations.