Artificial Synapse Based on a 2D-SnO₂ Memtransistor with Dynamically Tunable Analog Switching for Neuromorphic Computing

TLDR

Polycrystalline 2D‑SnO₂ memristors produced by a low‑temperature, vacuum‑free liquid‑metal process exhibit excellent digital/analog resistive switching, multistate storage, and gate‑tunable resistance states. The gate‑tunable memtransistor enables heterosynaptic analog switching by adjusting gate bias, a capability absent in conventional two‑terminal memristors, thereby facilitating complex neuromorphic learning. We demonstrated that the gate‑tunable 2D‑SnO₂ memtransistor dynamically modulates analog switching with good linearity and an improved conductance‑change ratio, achieving high‑accuracy learning and advancing neuromorphic device technology with an extra degree of freedom.

Abstract

A new type of two-dimensional (2D) SnO2 semiconductor-based gate-tunable memristor, that is, a memtransistor, an integrated device of a memristor and a transistor, was demonstrated to advance next-generation neuromorphic computing technology. The polycrystalline 2D-SnO2 memristors derived from a low-temperature and vacuum-free liquid metal process offer several interesting resistive switching properties such as excellent digital/analog resistive switching, multistate storage, and gate-tunability function of resistance switching states. Significantly, the gate tunability function that is not achievable in conventional two-terminal memristors provides the capability to implement heterosynaptic analog switching by regulating gate bias for enabling complex neuromorphic learning. We successfully demonstrated that the gate-tunable synaptic device dynamically modulated the analog switching behavior with good linearity and an improved conductance change ratio for high recognition accuracy learning. The presented gate-tunable 2D-oxide memtransistor will advance neuromorphic device technology and open up new opportunities to design learning schemes with an extra degree of freedom.