Multiwavelength Optoelectronic Synapse with 2D Materials for Mixed-Color Pattern Recognition

TLDR

Neuromorphic visual systems that emulate retinal functions hold promise for in‑sensor computing, potentially making AI ubiquitous. The study presents an optoelectronic synapse that integrates sensing, storage, and processing for real‑time object identification. The device uses a UV–visible MoS₂ FET channel coupled to an infrared PtTe₂/Si gate to sense, store, and process light across 300 nm–2 µm with low dark current, and its weight‑update parameters train an ANN to recognize single and mixed‑wavelength patterns. The device shows optically controlled short‑ and long‑term potentiation, electrically driven long‑term depression, and wavelength‑dependent weight updates from 300 nm to 2 µm, demonstrating feasibility for multi‑wavelength neuromorphic pattern recognition and object identification.

Abstract

Neuromorphic visual systems emulating biological retina functionalities have enormous potential for in-sensor computing, with prospects of making artificial intelligence ubiquitous. Conventionally, visual information is captured by an image sensor, stored by memory units, and eventually processed by the machine learning algorithm. Here, we present an optoelectronic synapse device with multifunctional integration of all the processes required for real time object identification. Ultraviolet–visible wavelength-sensitive MoS2 FET channel with infrared sensitive PtTe2/Si gate electrode enables the device to sense, store, and process optical data for a wide range of the electromagnetic spectrum, while maintaining a low dark current. The device exhibits optical stimulation-controlled short-term and long-term potentiation, electrically driven long-term depression, synaptic weight update for multiple wavelengths of light ranging from 300 nm in ultraviolet to 2 μm in infrared. An artificial neural network developed using the extracted weight update parameters of the device can be trained to identify both single wavelength and mixed wavelength patterns. This work demonstrates a device that could potentially be used for realizing a multiwavelength neuromorphic visual system for pattern recognition and object identification.