Multidimensional Light Perception through Time-Dependent Plasticity of an Optoelectronic Synapse

Abstract

Light signals encode multidimensional parameters, such as wavelength, power density, and pulse duration, making visual perception critically important. The development of multidimensional light perception has proven to be a computational challenge. Conventional artificial visual systems consisting of optoelectronic sensors and von Neumann architecture suffer from separating sensors and memory units. Artificial optoelectronic synapses implementing optical memory have recently enabled neuromorphic computing for optical parameter classification. However, the classification of multiple light parameters on a synapse has not been achieved. Developing a synapse with adjustable photocurrent responses under multidimensional optical parameters and a neuromorphic computing paradigm suitable for the classification is crucial. Here, MoS₂/SnO₂ quantum dots optoelectronic synapses are demonstrated, in which the heterojunction between MoS₂ and SnO₂ achieves a pronounced optical memory effect. The time-dependent plasticity of the photocurrent responses is exhibited under wavelengths, power densities, and durations of light stimulation. The responses successfully emulate essential synaptic behaviors, including paired-pulse facilitation, short-term memory, long-term memory, and learning experience. Next, a recurrent neural network committed to processing the time-dependent responses is used to classify wavelengths, power densities, and durations of optical inputs. This realizes an accuracy of 100% under 1-parameter 3-class classification, 94% under 2-parameter 9-class classification and 96% under 3-parameter 8-class classification. Moreover, this work demonstrates effective feature recognition and extraction of a bicolor image, showcasing the advantage of multidimensional light perception for precise color-coded pattern extraction and advancing applications in multimodal image analysis. These findings highlight promising prospects in satisfying stringent performance requirements on artificial visual systems.