Abstract Event‐driven processing in neuromorphic vision systems, utilizing spiking neural networks, can offer improved energy efficiency compared to conventional von Neumann systems. This study proposes an artificial retinal neuron with a vertically integrated optoelectronic memristor (optomemristor) and a neuron transistor (neuristor), inspired by the visual receptive field (VRF) of the biological retina. This design performs pre‐processing in the sensor to extract essential image features, such as edges. The optomemristor on top consists of an In‐Ga‐Zn‐O thin film, which detects light, while the neuristor at the bottom is made of a Si field‐effect transistor (FET), converting the spikes into an electrical signal. The VRF hardware comprises excitatory (ON‐type) and inhibitory (OFF‐type) cells. The spiking frequency of the Si FET increases in response to light exposure for ON‐type cells, which are composed of a serially connected optomemristor and neuristor. In contrast, OFF‐type cells, composed of parallelly connected devices, decrease the spiking frequency under light exposure. The dual‐type configuration, which incorporates both ON‐ and OFF‐type cells, achieves a remarkable 99.8% accuracy in fingerprint pattern classification due to the efficient extraction of edge information. This represents a significant improvement over the 56.1% accuracy of the single‐type configuration that relies solely on ON‐type cells.
Kim et al. (Thu,) studied this question.