Protonic solid-state electrochemical synapse for physical neural networks

Abstract

Physical neural networks made of analog resistive switching processors are promising platforms for analog computing. State-of-the-art resistive switches rely on either conductive filament formation or phase change. These processes suffer from poor reproducibility or high energy consumption, respectively. Herein, we demonstrate the behavior of an alternative synapse design that relies on a deterministic charge-controlled mechanism, modulated electrochemically in solid-state. The device operates by shuffling the smallest cation, the proton, in a three-terminal configuration. It has a channel of active material, WO₃. A solid proton reservoir layer, PdH_x, also serves as the gate terminal. A proton conducting solid electrolyte separates the channel and the reservoir. By protonation/deprotonation, we modulate the electronic conductivity of the channel over seven orders of magnitude, obtaining a continuum of resistance states. Proton intercalation increases the electronic conductivity of WO₃ by increasing both the carrier density and mobility. This switching mechanism offers low energy dissipation, good reversibility, and high symmetry in programming.

Designing energy efficient neural networks based on synaptic memristor devices remains a challenge. Here, the authors propose the development of a 3-terminal WO₃ synaptic device based on proton intercalation in inorganic materials by leveraging a solid proton reservoir layer PdH_x as the gate terminal.