Nonlinear Dynamics in HfO2/SiO2-Based Interface Dipole Modulation Field-Effect Transistors for Synaptic Applications

Abstract

In the pursuit of energy-efficient spiking neural network (SNN) hardware, synaptic devices leveraging emerging memory technologies hold significant promise. This study investigates the application of the recently proposed HfO2/SiO2-based interface dipole modulation (IDM) memory for synaptic spike timing-dependent plasticity (STDP) learning. Firstly, through pulse measurements of IDM metal–oxide–semiconductor (MOS) capacitors, we demonstrate that IDM exhibits an inherently nonlinear and near-symmetric response. Secondly, we discuss the drain current response of a field-effect transistor (FET) incorporating a multi-stack IDM structure, revealing its nonlinear and asymmetric pulse response, and suggest that the degree of the asymmetry depends on the modulation current ratio. Thirdly, to emulate synaptic STDP behavior, we implement double-pulse-controlled drain current modulation of IDMFET using a simple bipolar rectangular pulse. Additionally, we propose a double-pulse-controlled synaptic depression that is valuable for optimizing STDP-based unsupervised learning. Integrating the pulse response characteristics of IDMFETs into a two-layer SNN system for synaptic weight updates, we assess training and classification performance on handwritten digits. Our results demonstrate that IDMFET-based synaptic devices can achieve classification accuracy comparable to previously reported simulation-based results.