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Artificial synapse devices are dedicated to overcoming the von Neumann bottleneck. Adopting light signals in visual information processing and computing is vital for developing next‐generation artificial neuromorphic systems. A strategy to construct all‐optically controlled artificial synaptic devices based on full oxides with amorphous ZnAlSnO/SnO heterojunction in a two‐terminal planar configuration is proposed. All synaptic behaviors are operated in the visible optical pathway, with excitatory synapse under red (635 nm) light and inhibitory synapse under green (532 nm) and blue (405 nm) lights. Based on the different inhibitory effects, two modes of long‐term depression (LTD) and RESET processes can be implemented through green and blue lights, respectively. The energy consumption of an event can be as low as 0.75 pJ. A three‐layer perceptron model is designed to classify 28 × 28‐pixel handwritten digital images and performed supervised learning using a backpropagation algorithm, demonstrating the bio‐visually inspired neuromorphic computing with a training accuracy of 92.74%. The all‐optically controlled artificial synapses with write/erasure behaviors in visible RGB region and rational microelectronic process, as presented in this work, are essential in developing future artificial neuromorphic systems and highlight the huge potential of next‐generation computer systems. [ABSTRACT FROM AUTHOR]

Neuromorphic visual systems based on optoelectronic synaptic devices have been recently studied to simulate the retina and visual cortex of a human being. Now it is shown that an array of optoelectronic synaptic devices based on the two‐terminal structure of Si/perovskite/Au may mimic the functionalities of lateral geniculate nucleus (LGN) cells. Benefiting from the photovoltaic effect, the devices can work under a self‐powered mode. Diverse synaptic functionalities such as postsynaptic current, paired‐pulse facilitation/depression, spike duration‐dependent plasticity, spike number‐dependent plasticity, and spike rate‐dependent plasticity have been simulated. By modulating the electric bias of the devices in the array the simulation of the positional and orientational recognition of the LGN cells is demonstrated. [ABSTRACT FROM AUTHOR]

Optoelectronic synaptic devices that mimic biological synapses are critical building blocks of artificial neural networks (ANN) based on optoelectronic integration. Here it is shown that an optoelectronic synaptic device based on the hybrid structure of silicon nanocrystals (Si NCs) and poly(3‐hexylthiophene) (P3HT) can work with dual modes, exhibiting versatile synaptic plasticity. In the three‐terminal mode, the device is a synaptic transistor, which has wavelength‐selective synaptic plasticity due to potential wells enabled by the Si NCs/P3HT hybrid structure. In the two‐terminal mode, it is a synaptic metal‐oxide‐semiconductor (MOS) device, which is capable of mimicking spike‐rate‐dependent plasticity (SRDP) and metaplasticity with optical stimulation. Based on the wavelength‐selective synaptic plasticity a light‐stimulated ANN is proposed to recognize handwritten digits with an accuracy of around 90.4%. In addition, the SRDP and metaplasticity may be well used for the simulation of edge detection of images, facilitating real‐time image processing. [ABSTRACT FROM AUTHOR]