On-line prediction of NBTI-induced aging rates

Nanoscale technologies are increasingly susceptible to aging processes such as Negative-Bias Temperature Instability (NBTI) which undermine the reliability of VLSI systems. Existing monitoring techniques can detect the violation of safety margins and hence make the prediction of an imminent failure possible. However, since such techniques can only detect measurable degradation effects which appear after a relatively long period of system operation, they are not well suited to early aging prediction and proactive aging alleviation. This work presents a novel method for the monitoring of NBTI-induced degradation rate in digital circuits. It enables the timely adoption of proper mitigation techniques that reduce the impact of aging. The developed method employs machine learning techniques to find a small set of so called Representative Critical Gates (RCG), the workload of which is correlated with the degradation of the entire circuit. The workload of RCGs is observed in hardware using so called workload monitors. The output of the workload monitors is evaluated on-line to predict system degradation experienced within a configurable (short) period of time, e.g. a fraction of a second. Experimental results show that the developed monitors predict the degradation rate with an average error of only 1.6% at 4.2% area overhead.