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Compact and energy-efficient Synapse and Neurons are essential to realize thefull potential of neuromorphic computing. In addition, a low variability isindeed needed for neurons in Deep neural networks for higher accuracy. Further,process (P), voltage (V), and temperature (T) variation (PVT) are essentialconsiderations for low-power circuits as performance impact and compensationcomplexities are added costs. Recently, band-to-band tunneling (BTBT) neuronhas been demonstrated to operate successfully in a network to enable a LiquidState 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 acritical factor in assessing performance. In this work, we demonstrate the PVTvariation impact in the BTBT regime and benchmark the operation against thesubthreshold slope (SS) and ON-regime (ION) of partially depleted-Silicon onInsulator MOSFET. It is shown that the On-state regime offers the lowestvariability but dissipates higher power. Hence, not usable for low-powersources. 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 improvementis due to the well-known weaker P, V, and T dependence of BTBT vs. SS. We showthat the BTBT variation is uncorrelated with mutually correlated SS & IONoperation - indicating its different origin from the mechanism and locationperspectives. Hence, the BTBT regime is promising for low-current, low-power,and low device-to-device variability neuron operation.

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