Noninvasive Methodology for the Age Estimation of ICs Using Gaussian Process Regression

Age prediction for integrated circuits (ICs) is essential in establishing prevention and mitigation steps to avoid unexpected circuit failures in the field. Any electronic system would get benefit from an accurate age calculation. Additionally, it would assist in reducing the amount of electronic waste and the effort toward green computing. In this article, we propose a methodology to estimate the age of ICs using the Gaussian process regression (GPR). The output frequency of the ring oscillator (RO) is influenced by various factors, including the trackable path, voltage, temperature, and ageing. These dependencies are leveraged in the GPR model training. We demonstrate the RO’s frequency degradation by employing the Synopsys HSPICE tool with 32 nm predictive technology model (PTM) and the Synopsys technology library. We used temperature variation from 0 °C to 100 °C and voltage variation from 0.80 to 1.05 V for the data acquisition. Our methodology predicts age precisely; the minimum prediction accuracy with a month deviation on linear sampling rate is 85.36% for 13-Stage RO and 87.09% for 21-Stage RO, with a range of improvement in prediction accuracy compared to state-of-the-art (SOTA) is 9.74% to 16.99%. Similarly, on the logarithmic sampling rate, the prediction accuracy for 13-Stage RO and 21-Stage RO are 98.62% and 98.56%, respectively. The proposed methodology performs more accurately in terms of prediction accuracy and age prediction deviation from the SOTA methodology.