Abstract: The development of high-performance neuromorphic phototransistors is of paramount importance for image perception and depth memory learning. Here, based on metal-oxide heterojunction architecture, artificial synaptic phototransistors with synaptic plasticity have been achieved, demonstrating an artificial synapse that integrates central and optic nerve functions. Thanks to the sensitive light-detection properties, the optical power consumption of such photonic artificial synapses can be as low as 22 pico-joules, which is extremely competitive compared with other pure metal oxide photoelectric synapses ever reported. What is more, owing to its good short-term (STP) and tunable amplitude-frequency characteristics, the as-constructed device can function as a biomimetic high-pass filter for picture edge detection. Dual-mode synaptic modulation has been performed, combining photonic pulse with gate voltage stimulus. After photoelectric-synergistic modulation, the high synaptic weights enable the device to simulate complex neural learning rules for neuromorphic applications, including gesture recognition, image perception in the visual system, and classically conditioned reflexes. These results suggest that the current oxide-based heterojunction architecture displays potential application in future multifunction neuromorphic devices and systems.

Key words: Metal oxide, Artificial synaptic devices, Phototransistor, Associative-memory-learning, Neuromorphic applications