First Demonstration of Yttria-Stabilized Hafnia-Based Long-Retention Solid-State Electrolyte-Gated Transistor for Human-Like Neuromorphic Computing.

Abstract

Electrolyte-gated transistors have strong potential for high-performance artificial synapses in neuromorphic bio-interfaces owing to their outstanding synaptic characteristics, low power consumption, and human-like mechanisms. However, the short retention time is a hurdle to overcome owing to the natural diffusion of protons. Here, a novel modulation technique of ionic conductivity is proposed with yttria-stabilized hafnia for the first time to enhance the retention characteristic of a solid-state electrolyte-gated transistor-based artificial synapse. With the optimization of the ionic conductivity in yttria-stabilized hafnia, a high retention time of over 300s and remarkable synaptic characteristics are accomplished by regulating channel conductance with precise modulation of the strength of the proton-electron coupling intensity along the input signals. Furthermore, pattern recognition simulation is conducted based on the measured synaptic characteristics, exhibiting 94.41% of operation accuracy, which implies a promising solution for neuromorphic in-memory computing systems with a high operation accuracy and low power consumption.