Multi-terminal ionic-gated low-power silicon nanowire synaptic transistors with dendritic functions for neuromorphic...

Neuromorphic computing systems have shown powerful capability in tasks, such as recognition, learning, classification and decision-making, which are both challenging and inefficient in using the traditional computation architecture. The key elements including synapses and neurons, and their feasible hardware implementation are essential for practical neuromorphic computing. However, most existing synaptic devices used to emulate functions of a single synapse and the synapse-based networks are more energy intensive and less sustainable than their biological counterparts. The dendritic functions such as integration of spatiotemporal signals and spike-frequency coding characteristics have not been well implemented in a single synaptic device and thus play an imperative role in future practical hardware-based spiking neural networks. Moreover, most emerging synaptic transistors are fabricated by nanofabrication processes without CMOS compatibility for further wafer-scale integration. Herein, we demonstrate a novel ionic-gated silicon nanowire synaptic field-effect transistor (IGNWFET) with low power consumption (<400 fJ per switching event) based on the standard CMOS process platform. For the first time, the dendritic integration and dual-synaptic dendritic computations (such as "Add" and "Subtraction") could be realized by processing frequency coded spikes using a single device. Meanwhile, multi-functional characteristics of artificial synapses including the short-term and long-term synaptic plasticity, paired pulse facilitation and high-pass filtering were also successfully demonstrated based on 40 nm wide IGNWFETs. The migration of ions in polymer electrolyte and trapping in high-k dielectric were also experimentally studied in-depth to understand the short-term plasticity and long-term plasticity. Combined with statistical uniformity across a 4-inch wafer, the comprehensive performance of IGNWFET demonstrates its potential application in future biologically emulated neuromorphic systems.