The advancement of artificial intelligent vision systems
heavily
relies on the development of fast and accurate optical imaging detection,
identification, and tracking. Framed by restricted response speeds
and low computational efficiency, traditional optoelectronic information
devices are facing challenges in real-time optical imaging tasks and
their ability to efficiently process complex visual data. To address
the limitations of current optoelectronic information devices, this
study introduces a novel photomemristor utilizing halide perovskite
thin films. The fabrication process involves adjusting the iodide
proportion to enhance the quality of the halide perovskite films and
minimize the dark current. The photomemristor exhibits a high external
quantum efficiency of over 85%, which leads to a low energy consumption
of 0.6 nJ. The spike timing-dependent plasticity characteristics of
the device are leveraged to construct a spiking neural network and
achieve a 99.1% accuracy rate of directional perception for moving
objects. The notable results offer a promising hardware solution for
efficient optoneuromorphic and edge computing applications.