All‐Solid‐State Synaptic Transistor with Ultralow Conductance for Neuromorphic Computing

TLDR

Electronic synaptic devices are key components for neuromorphic systems that surpass von Neumann limits, but two‑terminal memristors suffer from filament‑related nonlinear switching, noise, and high conductance, whereas electrochemical three‑terminal transistors avoid filament formation. The authors present an all‑solid‑state electrochemical transistor comprising a Li‑ion solid dielectric and 2D α‑MoO₃ nanosheet channel that achieves nonvolatile conductance modulation below 75 nS through reversible Li intercalation, enabling short‑ and long‑term plasticity and near‑linear analog weight updates. Simulations on handwritten digit datasets yield 94.1 % recognition accuracy, demonstrating that 2D‑oxide synaptic transistors can support large‑scale, energy‑efficient neuromorphic computing networks.

Abstract

Abstract Electronic synaptic devices are important building blocks for neuromorphic computational systems that can go beyond the constraints of von Neumann architecture. Although two‐terminal memristive devices are demonstrated to be possible candidates, they suffer from several shortcomings related to the filament formation mechanism including nonlinear switching, write noise, and high device conductance, all of which limit the accuracy and energy efficiency. Electrochemical three‐terminal transistors, in which the channel conductance can be tuned without filament formation provide an alternative platform for synaptic electronics. Here, an all‐solid‐state electrochemical transistor made with Li ion–based solid dielectric and 2D α‐phase molybdenum oxide (α‐MoO 3 ) nanosheets as the channel is demonstrated. These devices achieve nonvolatile conductance modulation in an ultralow conductance regime (<75 nS) by reversible intercalation of Li ions into the α‐MoO 3 lattice. Based on this operating mechanism, the essential functionalities of synapses, such as short‐ and long‐term synaptic plasticity and bidirectional near‐linear analog weight update are demonstrated. Simulations using the handwritten digit data sets demonstrate high recognition accuracy (94.1%) of the synaptic transistor arrays. These results provide an insight into the application of 2D oxides for large‐scale, energy‐efficient neuromorphic computing networks.