Artificial optic-neural synapse for colored and color-mixed pattern recognition

By Seunghwan Seo, Seo-Hyeon Jo, Sungho Kim, Jaewoo Shim, Seyong Oh, Jeong-Hoon Kim, K. Heo, Jae-Woong Choi, C. Choi, Saeroonter Oh and 3 more. Nature Communications, 2018

616 citations recorded by the source corpus.

The priority of synaptic device researches has been given to prove the device potential for the emulation of synaptic dynamics and not to functionalize further synaptic devices for more complex learning. Here, we demonstrate an optic-neural synaptic device by implementing synaptic and optical-sensing functions together on h-BN/WSe2 heterostructure. This device mimics the colored and color-mixed pattern recognition capabilities of the human vision system when arranged in an optic-neural network. Our synaptic device demonstrates a close to linear weight update trajectory while providing a large number of stable conduction states with less than 1% variation per state. The device operates with low voltage spikes of 0.3 V and consumes only 66 fJ per spike. This consequently facilitates the demonstration of accurate and energy efficient colored and color-mixed pattern recognition. The work will be an important step toward neural networks that comprise neural sensing and training functions for more complex pattern recognition. Artificial neural networks can emulate the human vision because of their spike-based operation by employing memristors as synapses. Here, Seo et al. integrate synaptic and optical sensing functions in a single heterostructure, which enables accurate and energy-efficient recognition of colored patterns.

Subfields: Artificial Intelligence, Cellular and Molecular Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/be3bc8062860ee79d5e534be675f49b888dbf522

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Feng Miao
2. Wenhao Song
3. Zhongrui Wang

In Electrical and Electronic Engineering

1. Yang Chai
2. Feng Miao
3. Takashi Taniguchi
4. Wenhao Song
5. Zhongrui Wang

Citations in the corpus

• Hardware implementation of memristor-based artificial neural networks (2024, neutral): “However, most of those studies in fact only measured the fi gures-of-merit of one/few devices and simulated the accuracy of an ANN via software 62 – 67 in such type of studies the connection between the memristors fabricated and the ANN is relatively weak.”
• Near-sensor and in-sensor computing (2020, methodology): “The optical synapses with a wavelength-dependent response also showed colour-filtering behaviours 30 , which has potential for colour segmentation applications of low-level image processing.”
• Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses (2023, background): “…for artificial visual sensory systems [194] [182] Copyright (2018) The Authors. b An artificial optic-neural synapse system for pattern recognition [193] Copyright (2018) The Authors. c An artificial optoelectronic neuromorphic circuit array for mimicking environment-adaptable artificial visual…”
• Gate-tunable van der Waals heterostructure for reconfigurable neural network vision sensor (2020, positive): “The vdW vertical heterostructures formed by stacking different 2D materials accommodate an abundance of electronic and optoelectronic properties (21-32), which may be exploited to mimic hierarchical architecture and functions of retinal neurons (33-43) in a natural manner to implement a neural netwo”