Fully visible light controlled neuro-synaptic ReRAM device based on metal oxide heterojunction

Photoelectric synaptic devices as a combination of electronic synapse and photodetector are considered as emerging bio-inspired device technologies. These devices have immense potential to conquer the bottleneck of von Neumann architecture based traditional computing systems. In this Letter, we propose an all-oxide based photoelectric neuro-synaptic resistive random access memory device with the structure of ITO/Ga2O3/ZnO/ITO/Glass, in which the conductance states are reversibly tuned by two different wavelengths (405/522 nm) of visible light spectrum. The strength of light pulse is altered to investigate the learning and forgetting phases of the photoelectric response of the device. A basic biomimetic function “learning-forgetting-rehearsal” behavior is imitated up to 20 cycles. Moreover, emulation of some typical synaptic functions such as associative learning and switching between short and long term plasticities indicate the wavelength awareness of the device. Based on the pure optically induced potentiation/depression characteristics, convolutional neural network simulation achieves an overall test accuracy of 82.5% for the classification of Zalando's article images. The noise tolerance capability of neural network is also examined by applying “salt and pepper” noise in high proportion (75%) to corrupt the images. This work may provide a promising step toward the development of transparent electronics in optogenetics-inspired neuromorphic computing.