Organic electrochemical synaptic transistors for neuromorphic vision sensor

Traditional machine vision based on von Neumann architecture suffers from the limitation of memory and computing separation. With the explosive growth of data, meeting the application requirements has become challenging due to the architecture. Artificial neuromorphic vision systems (ANVS) inspired by the human visual system (HVS) are considered a viable solution. Organic synaptic devices have great potential in neuromorphic computing. We propose an artificial neural visual system (ANVS) based on organic electrochemical synaptic transistors (OEST) and a photodiode array strategy in this work. The basic biological synaptic behaviors can be successfully achieved by optical stimulation. This system successfully simulated the human visual system and was able to perform feature extraction on color images, producing results that aligned with simulation expectations. Color images can achieve a recognition rate of 99.4% after OEST-based hardware convolutional kernel processing, whereas this rate drops to only 33.4% after processing through software convolutional kernels. Comparison of the recognition rates shows that the OEST-based hardware convolutional arrays are more closely matched to the mechanisms of the human eye's retina and can recognize various color images. Overall, this work demonstrates that OEST has great potential in the field of neuromorphic vision.