Demonstrating Spike-Timing-Dependent Plasticity Learning with Electrochemical FETs

This study explores the properties of spike timing dependent plasticity (STDP) in spiking neural networks (SNNs) with electrochemical field-effect transistor (ECFET) synaptic devices. STDP is a fundamental unsupervised learning mechanism in neuromorphic computing systems. Electrochemical FETs demonstrate tunable conductance through ionic drift-diffusion processes, offering biologically inspired computation. STDP behavior is demonstrated in ECFETs by analyzing the relationship between input spike timing and frequency, and subsequent modulation of the conductance between ECFET source and drain. Using advanced models that capture the dynamics of real devices, synaptic weight adjustments similar to biological neural plasticity are observed. Impacts of various device parameters on conductance tuning, weight stability are investigated. The findings reveal that ECFETs are an outstanding candidate for synapses in bioloigcally-inspired neuromorphic SNNs.