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Computer vision systems that lack color sensitivity may encounter challenges in object identification, particularly when the target object's pattern resembles the background. To enhance neural network color sensitivity while maintaining low power consumption and fast data processing, we present a switchable photodetection-synaptic transistor (SPST) based on a two-dimensional (2D) perovskite ferroelectric (PMA) 2 PbCl 4 film for constructing self-powered optoelectronic synapses with color perception capabilities, with applications for in-sensor memory and neuromorphic computing. The SPST can be switched to photodetection mode or optoelectronic synapse modes by controlling its gate voltage, and, compared with the neural network based on an electrical synapse, the processing time and power consumption of the neural network based on the SPST are reduced by 56.52% and 91.43%, respectively. Compared to the traditional architecture based on electrical synapses, the classification accuracies for different targets improve to 98.53% (from 10.2%) and 99.47% (from 12.98%) after 5 training epochs.
Zhang et al. (Sun,) studied this question.