Implementation of brain-like neuromorphic computing systems with artificial synapses may broaden its advantages in the application field by providing devices with the characteristics of the optoelectronic response. However, all-inorganic optoelectronic synaptic devices based on solution-processed still remain largely unexplored. Therefore, ZrLiO/InLiO thin-film transistors (TFTs) based on Li-ion doping strategy are fabricated by a facile, carbon-free, and environmentally friendly aqueous solution route in this work. Li-ion is expected to play a dual modulation role in synaptic TFTs by promoting the formation of electric-double-layers and increasing the number of oxygen vacancies that can be excited by optical signals. The ZrLiO/InLiO TFT shows excellent multi-functional synaptic characteristics such as excitatory and inhibitory postsynaptic currents, paired-pulse facilitation, the transition from short-term memory to long-term memory. The optoelectronic device is sensitive to the optical signal and exhibits brain-like learning and memory abilities after repeated presynaptic stimulation. Besides, the classic biological Pavlov's dog experiments are successfully demonstrated to verify the associative learning ability of the artificial synapse. This result may provide a feasible and effective strategy for future optoelectronic neuromorphic computing devices.

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.

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