  Photoresponsivity studies of wide-bandgap oxide-based devices have emerged as
a vibrant and popular research area. Researchers have explored various material
systems in their quest to develop devices capable of responding to
illumination. In this study, we engineered a mature wide bandgap oxide-based
bilayer heterostructure synaptic memristor to emulate the human brain for
applications in neuromorphic computing and photograph sensing. The device
exhibits advanced electric and electro-photonic synaptic functions, such as
long-term potentiation (LTP), long-term depression (LTD), and paired pulse
facilitation (PPF), by applying successive electric and photonic pulses.
Moreover, the device exhibits exceptional electrical SET and photonic RESET
endurance, maintaining its stability for a minimum of 1200 cycles without any
degradation. Density functional theory calculations of the band structures
provide insights into the conduction mechanism of the device. Based on this
memristor array, we developed an autoencoder and convolutional neural network
for noise reduction and image recognition tasks, which achieves a peak
signal-to-noise ratio of 562 and high accuracy of 84.23%, while consuming lower
energy by four orders of magnitude compared with the Tesla P40 GPU. This
groundbreaking research not only opens doors for the integration of our device
into image processing but also represents a significant advancement in the
realm of in-memory computing and photograph sensing features in a single cell.
