Tunable plasticity in functionalized honeycomb synaptic memristor for neurocomputing

Abstract

Tunable plasticity is one of the important features in synapses and plays a key role in neuromorphic computing (neurocomputing, NC), maintaining a stable pulse neural network environment, and realizing accurate image recognition. Herein, a functionalized honeycomb-like synaptic memristor (HLSM) composed of porous silicon oxide incorporated with MoS2 quantum dots (QDs) is fabricated and the synaptic properties such as paired-pulse facilitation (PPF), post-tetanic potentiation (PTP), learning-forgetting behavior, and spike-timing-dependent plasticity (STDP) are simulated. A critical characteristic of short/long-term plasticity (SLTP) is observed from the HLSM device for the sliding frequency threshold between 200 μs and 10 ms and tunable synaptic plasticity is realized without gate voltage regulation. An artificial neural network (ANN) based on the potentiation/depression characteristics is designed theoretically and the recognition rate is observed to increase from 54.2% to 91.8% by simply adjusting the input frequency. The underlying mechanism of the tunable synaptic plasticity is proposed and discussed by taking advantage of the honeycomb structure on fluid buffering and acceleration. The results and theoretical understanding are expected to accelerate the application in NC to image recognition.