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The complete hardware implementation of an optoelectronic neuromorphic computing system is considered as one of the most promising solutions to realize energy-efficient artificial intelligence. Here, a fully light-driven and scalable optoelectronic neuromorphic circuit with metal-chalcogenide/metal-oxide heterostructure phototransistor and photovoltaic divider is proposed. To achieve wavelength-selective neural operation and hardware-based pattern recognition, multispectral light modulated bidirectional synaptic circuits are utilized as an individual pixel for highly accurate and large-area neuromorphic computing system. The wavelength selective control of photo-generated charges at the heterostructure interface enables the bidirectional synaptic modulation behaviors including the excitatory and inhibitory modulations. More importantly, a 7 × 7 neuromorphic pixel circuit array is demonstrated to show the viability of implementing highly accurate hardware-based pattern training. In both the pixel training and pattern recognition simulation, the neuromorphic circuit array with the bidirectional synaptic modulation exhibits lower training errors and higher recognition rates, respectively.
Kwon et al. (Wed,) studied this question.