Emulating the biological visual perception system typically requires a complex architecture including the integration of an artificial retina and optic nerves with various synaptic behaviors. However, self‐adaptive synaptic behaviors, which are frequently translated into visual nerves to adjust environmental light intensities, have been one of the serious challenges for the artificial visual perception system. Here, an artificial optoelectronic neuromorphic device array to emulate the light‐adaptable synaptic functions (photopic and scotopic adaptation) of the biological visual perception system is presented. By employing an artificial visual perception circuit including a metal chalcogenide photoreceptor transistor and a metal oxide synaptic transistor, the optoelectronic neuromorphic device successfully demonstrates diverse visual synaptic functions such as phototriggered short‐term plasticity, long‐term potentiation, and neural facilitation. More importantly, the environment‐adaptable perception behaviors at various levels of the light illumination are well reproduced by adjusting load transistor in the circuit, exhibiting the acts of variable dynamic ranges of biological system. This development paves a new way to fabricate an environmental‐adaptable artificial visual perception system with profound implications for the field of future neuromorphic electronics.

Neuromorphic electronics draw attention as innovative approaches that facilitate hardware implementation of next‐generation artificial intelligent system including neuromorphic in‐memory computing, artificial sensory perception, and humanoid robotics. Among the various neuromorphic devices, optoelectronic synapses are promising neuromorphic devices that use optical means to mimic synaptic plasticity and related functions. Compared with classical neuromorphic chip based on electronic synapses using electrical means, photonic neuromorphic chip using light as input spike signal can be attractive alternative approach for next‐generation artificial intelligent system capable of high density, low power consumption, and low crosstalk. Thus, various optoelectronic synaptic electronics have been developed to overcome the drawback of conventional artificial intelligent system based on electrical synapses. Herein, the recent progresses in transistor‐based optoelectronic synapses for artificial intelligent system and review their device architecture, neuromorphic operational mechanisms, manufacturing methodologies, and advanced applications for artificial intelligent computing and visual perception systems are focused. Finally, the future challenges and research direction in the optoelectronic synaptic research are discussed.

We suggest the use of a thin-film transistor (TFT) composed of amorphous InGaZnO (a-IGZO) as a channel and a sensing layer for low-concentration NO gas detection. Although amorphous oxide layers have a restricted surface area when reacting with NO gas, such TFT sensors have incomparable advantages in the aspects of electrical stability, large-scale uniformity, and the possibility of miniaturization. The a-IGZO thin films do not possess typical reactive sites and grain boundaries, so that the variation in drain current of the TFTs strictly originates from oxidation reaction between channel surface and NO gas. Especially, the sensing data obtained from the variation rate of drain current makes it possible to monitor efficiently and quickly the variation of the NO concentration. Interestingly, we found that enhancement-mode TFT (EM-TFT) allows discrimination of the drain current variation rate at NO concentrations ≤10 ppm, whereas a depletion-mode TFT is adequate for discriminating NO concentrations ≥10 ppm. This discrepancy is attributed to the ratio of charge carriers contributing to gas capture with respect to total carriers. This capacity for the excellent detection of low-concentration NO gas can be realized through (i) three-terminal TFT gas sensors using amorphous oxide, (ii) measurement of the drain current variation rate for high selectivity, and (iii) an EM mode driven by tuning the electrical conductivity of channel layers.

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