  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.
