Design space and variability analysis of SOI MOSFET for ultra-low power band-to-band tunneling neurons

Abstract

Large spiking neural networks (SNNs) require ultra-low power and low variability hardware for neuromorphic computing applications. Recently, a band-to-band tunneling-based (BTBT) integrator, enabling sub-kHz operation of neurons with area and energy efficiency, was proposed. For an ultra-low power implementation of such neurons, a very low BTBT current is needed, so minimizing current without degrading neuronal properties is essential. Low variability is needed in the ultra-low current integrator to avoid network performance degradation in a large BTBT neuron-based SNN. To address this, we conducted design space and variability analysis in TCAD, utilizing a well-calibrated TCAD deck with experimental data from GlobalFoundries 32nm PD-SOI MOSFET. First, we discuss the physics-based explanation of the tunneling mechanism. Second, we explore the impact of device design parameters on SOI MOSFET performance, highlighting parameter sensitivities to tunneling current. With device parameters' optimization, we demonstrate a ~20x reduction in BTBT current compared to the experimental data. Finally, a variability analysis that includes the effects of random dopant fluctuations (RDF), oxide thickness variability (OTV), and channel-oxide interface traps DIT in the BTBT, SS, and ON regimes of operation is shown. The BTBT regime shows high sensitivity to the RDF and OTV as any variation in them directly modulates the tunnel length or the electric field at the drain-channel junction, whereas minimal sensitivity to DIT is observed.