Oxygen-Vacancy-Induced Synaptic Plasticity in an Electrospun InGdO Nanofiber Transistor for a Gas Sensory System with a Learning Function

The perceptual learning function of a simulating human body is very important for constructing a neural computing system and a brainlike computer in the future. The sense of smell is an important part of the human sensory nervous system. However, current gas sensors simply convert gas concentrations into electrical signals and do not have the same learning and memory function as synapses. To solve this problem, we propose a new sensing idea to induce and activate the synaptic properties of transistors by adjusting the oxygen vacancy in the active layer. This sensor combines gas detection with synaptic memory and learning and overcomes the disadvantage of the separation of synaptic transistors and sensors, thus greatly reducing the cost of production. This work combines the detection of N,N-dimethylformamide (DMF) gas with the synaptic mechanism of human olfactory nerves. We successfully fabricated an InGdO nanofiber field-effect transistor by electrostatic spinning and simulated the response of human olfactory synapses to target gas by regulating the oxygen vacancy of the InGdO nanofiber. The synaptic transistor response under different concentrations of unmodulated pulses is tested, and the pavlovian conditioned reflex experiment is simulated successfully. This work provides a new idea of a gas sensor device, which is very important for the development of high-performance gas sensors and bionic electronic devices in the future.

Automated summary

This study proposes a new sensing idea by inducing and activating the synaptic properties of transistors through adjusting the oxygen vacancy. It combines gas detection with synaptic memory and learning, overcoming the separation between synaptic transistors and sensors and reducing production cost. By simulating the response of human olfactory synapses to target gas, a novel nanofiber field-effect transistor is successfully fabricated.