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

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) (PVT) variation 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 (LSM). A comparison of the PVT with competing modes of operation (e.g., BTBT versus subthreshold and above threshold) of the same transistor is a critical factor in assessing performance. In this work, we demonstrate the PVT variation impact on the BTBT regime and benchmark the operation against the subthreshold regime (SS) and ON-regime (
$\text{I}_{ \mathrm{\scriptscriptstyle ON}}$
) 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
$\sim 3\,\,{\times }$
variability reduction (
${ \boldsymbol {\sigma }_{I{_{D}}}{/} \boldsymbol {\mu }_{I{_{D}}}}$
) compared to the SS regime, considering the cumulative PVT variability. The improvement is due to the well-known weaker P, V, and T dependence of BTBT versus SS. We show that the BTBT variation is uncorrelated with mutually correlated SS and
$\text{I}_{{ \mathrm{\scriptscriptstyle ON}}}$
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 (D2D) variability neuron operation.