Memristor based on carbon nanotube gelatin composite film as artificial optoelectronic synapse for image processing

Abstract

Photoelectric artificial synapses based on memristors is an effective method to realize neuromorphic computation. This study presents an optoelectronic responsive artificial synapse made of a composite material consisting of gelatin and carbon nanotubes. The memristor demonstrates characteristics of analog resistive switching, the ability to store multiple memory states, and impressive retention properties. It has the capability to induce an excitatory post-synaptic current by means of electrical pulses or pulsed light exposure. The excitatory post-synaptic current can be modulated by the number, amplitude and interval of electrical pulses, as well as the action time, interval and light intensity of optical pulses. The artificial synapse showcases the emulation of fundamental Hebbian learning protocols, including spike timing dependent plasticity and spike amplitude dependent plasticity. In addition, the charge transfer in the carbon nanotube gelatin composite optoelectronic memristor is investigated through first-principles calculations, shedding light on its operational mechanism. Experimental results show that these devices have the potential to be utilized for processing image information, resulting in a significant reduction of input data and training expenses when recognizing handwritten numbers. Overall, the optoelectronic synapse exhibits promising image processing prospects in the field of neuromorphic computing.