A robust neuromorphic vision sensor with optical control of ferroelectric switching

The rapid development of the artificial intelligence field has increased the demand for retina-inspired neuromorphic vision sensors with integrated sensing, memory, and processing functions. Here, we present a neuromorphic vision sensor with an optoelectronic transistor structure consisting of monolayer molybdenum disulfide and barium titanate ferroelectric film. Beyond conventional electrical tuning of ferroelectric polarization, the optoelectronic transistor can exhibit a light-dosage tunable synaptic behavior with a high switching ratio and good non-volatility, enabled by photo-induced ferroelectric polarization reversal. The wavelength-dependent optical sensing and multi-level optical memory properties are utilized to achieve the in-sensor neuromorphic visual pre-processing. A simulated artificial neural network built from the proposed vision sensors with neuromorphic pre-processing function demonstrated that the image recognition rate for the Modified National Institute of Standards and Technology (MNIST) handwritten dataset could be significantly improved by reducing redundant data. The obtained results suggest that 2D semiconductor/ferroelectric optoelectronic transistors can provide a promising hardware implementation towards constructing high-performance neuromorphic visual systems A neuromorphic vision sensor based on an optoelectronic transistor using a monolayer MoS2 channel atop a ferroelectric BaTiO3 film is presented. The ferroelectric polarization switching process strongly depends on the light-dosage. The wavelength-dependent optical sensing and multi-level optical memory properties are utilized to realize the in-sensor neuromorphic pre-processing. [Display omitted] •A neuromorphic vision sensor was fabricated with a monolayer MoS2 channel atop a ferroelectric BaTiO3 film.•The transistor exhibits light-dosage tunable synaptic behavior with a high switching ratio and good non-volatility.•The image recognition rate can be improved from 15% to 91% by reducing redundant data via neuromorphic preprocessing.