4.8 Article

Artificial Tactile Sensing System with Photoelectric Output for High Accuracy Haptic Texture Recognition and Parallel Information Processing

Journal

NANO LETTERS
Volume 22, Issue 17, Pages 7275-7283

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.nanolett.2c02995

Keywords

photoelectric signals parallel output; triboelectric nanogenerator; artificial synapse; artificial sensory system; light-emitting synaptic devices

Funding

  1. National Natural Science Foundation of China [U21A20497, 61974029]
  2. Natural Science Foundation for Distinguished Young Scholars of Fujian Province [2020J06012]
  3. Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China [2021ZZ129]

Ask authors/readers for more resources

Developing multifunctional artificial sensory systems is crucial for constructing future artificial neural networks. This study proposed a novel sensory system capable of implementing a photoelectric hybrid neural network, showcasing high accuracy in fabric recognition.
Developing multifunctional artificial sensory systems is an important task for constructing future artificial neural networks. A system with multisignal output capability is highly required by the rising demand for high-throughput data processing in the Internet of Things (IoT) society. Here, a novel dual-output artificial tactile sensing (DOATS) system with parallel output of photoelectric signals was proposed. Because of the ionic-electronic coupling mechanism in light-emitting synaptic (LES) devices in the DOATS system, modulating electric current and light emission can coexist through ion accumulation and electron-hole recombination. As a result, the DOATS system can realize the simulation of human tactile information, and the recognition of 16 kinds of fabrics was demonstrated with an accuracy rate of 94.1%. A photoelectric hybrid artificial neural network was proposed, which achieved efficient and accurate multitask operation. The DOATS system proposed in this work is promising for implementing photoelectric hybrid neural network and promoting the development of interactive artificial intelligence.

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