Real-Time IC Aging Prediction via On-Chip Sensors
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2021
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Abstract
Real-time aging prediction for nanoscale integrated
circuits (ICs) is a crucial step for developing prevention and
mitigation actions to avoid unexpected circuit failures in the
field of operation. Current practices for predicting aging-related
performance degradation in ICs consist of recording the operating conditions (e.g. workload, temperature, etc.) throughout ICs’
usage time and building a learning model that maps historical
operating conditions to actual performance degradation. While
some operating conditions such as IC workload can be readily
recorded using existing on-chip structures (e.g. registers), other
operating conditions such as historical temperature values may
not be available for real-time aging degradation prediction. In
this paper, we develop a novel real-time IC aging prediction
scheme using a set of on-chip sensors that can accurately
record historical operating condition parameter values, which
will in turn be used for aging-related performance degradation
prediction. Experimental results show that by using a machine
learning based prediction model and the notion of equivalent
aging time, we can achieve accurate aging degradation prediction
with the proposed on-chip sensor structure.