Process Voltage Temperature Variability Estimation of Tunneling Current for Band-to-Band-Tunneling based Neuron

Abstract: Compact and energy-efficient Synapse and Neurons are essential to realize the full potential of neuromorphic computing. In addition, a low variability is indeed needed for neurons in Deep neural networks for higher accuracy. Further, process (P), voltage (V), and temperature (T) variation (PVT) are essential considerations for low-power circuits as performance impact and compensation complexities are added costs. Recently, band-to-band tunneling (BTBT) neuron has been demonstrated to operate successfully in a network to enable a Liquid State Machine. A comparison of the PVT with competing modes of operation (e.g., BTBT vs. sub-threshold and above threshold) of the same transistor is a critical factor in assessing performance. In this work, we demonstrate the PVT variation impact in the BTBT regime and benchmark the operation against the subthreshold slope (SS) and ON-regime (ION) of partially depleted-Silicon on Insulator MOSFET. It is shown that the On-state regime offers the lowest variability but dissipates higher power. Hence, not usable for low-power sources. Among the BTBT and SS regimes, which can enable the low-power neuron, the BTBT regime has shown ~3x variability reduction ({\sigma}_I_D/{\mu}_I_D) than the SS regime, considering the cumulative PVT variability. The improvement is due to the well-known weaker P, V, and T dependence of BTBT vs. SS. We show that the BTBT variation is uncorrelated with mutually correlated SS & ION operation - indicating its different origin from the mechanism and location perspectives. Hence, the BTBT regime is promising for low-current, low-power, and low device-to-device variability neuron operation.