Reducing Aging Impacts in Digital Sensors via Run-time Calibration
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2021-12-19
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Abstract
Hazards or intentional perturbations must
be identified in safety- and security-critical applications.
Digital sensors have been shown to be an appealing approach to detect such abnormalities. However, as any
sensor technology, digital sensors are prone to mis-calibration.
In particular, even if the digital sensor initial calibration is correct, the rate of false and missed alarms might
increase when the sensor is aged. In this paper, we
thoroughly study the impact of aging-induced false and
missed alarms. Indeed aging relates to the usage time,
and a priori model (historical data for environmental
variation) for predicting the aging is unrealistic for digital sensors as tracking the usage time with related temperature and voltage variation imposes high overhead. Accordingly, we propose an alternative approach where not one but two sensors are deployed. In practice, one sensor is used to detect environmental deviations, while
the second one is used as the reference. In this respect,
the second sensor is only operated seldom, mostly to
re-calibrate the active sensor when aged.
From this dual input (unaged and aged sensor), corrective models are derived. We account for two methods, namely simple but effective offset correction, and
adjustment based on machine-learning. We conduct extensive characterizations (both pre-silicon simulations
and post-silicon measurements on FPGA) which quantitatively confirm the applicability and high sensitivity
of digital sensors.