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

Neuromorphic computing systems have shown powerful capability in tasks like recognition, learning, classification and decision-making, which are both challenging and inefficient using 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 network are more energy intensive and less sustainable than biological counterparts. The dendritic functions such as the integration of spatiotemporal signals and spike-frequency coding characteristic have not been well implemented in a single synatic device, which play an imperative role in the future practical hardware-based spiking neural networks. Moreover, most emerging synaptic transistors were fabricated by nanofabrication processes without CMOS compatibility for further wafer-scale integration. Here, we demonstrated a novel ionic-gated silicon nanowire synaptic field-effect transistor (IGNWFET) with a low power consumption (<300 fJ per switching event) based on standard CMOS process platform. For the first time, the dendritic integration and dual-synaptic dendritic computations (such as “Add” and “Subtraction”) can be realized by processing frequency coded spikes using a single device. Meanwhile, multi-functional characteristics of artificial synapses including short-term and long-term synaptic plasticity, paired pulse facilitation, spike-rate-dependent plasticity and high-pass filtering have also been 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 to understand the short-term plasticity and long-term plasticity in depth. Combined with statistical uniformity across a 4-inch wafer, the comprehensive performance of IGNWFETs allows it potential application in future biologically emulated neuromorphic system.