Monolithic 2D Perovskites Enabled Artificial Photonic Synapses for Neuromorphic Vision Sensors
© 2024 The Authors. Advanced Materials published by Wiley‐VCH GmbH.
Veröffentlicht in: | Advanced materials (Deerfield Beach, Fla.). - 1998. - 36(2024), 18 vom: 14. Mai, Seite e2311524 |
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1. Verfasser: | |
Weitere Verfasser: | , , , , , , , , , |
Format: | Online-Aufsatz |
Sprache: | English |
Veröffentlicht: |
2024
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Zugriff auf das übergeordnete Werk: | Advanced materials (Deerfield Beach, Fla.) |
Schlagworte: | Journal Article 2D organic–inorganic hybrid perovskite ion‐exciton coupling neuromorphic visual system photonic synapse perovskite 12194-71-7 Oxides Titanium D1JT611TNE |
Zusammenfassung: | © 2024 The Authors. Advanced Materials published by Wiley‐VCH GmbH. Neuromorphic visual sensors (NVS) based on photonic synapses hold a significant promise to emulate the human visual system. However, current photonic synapses rely on exquisite engineering of the complex heterogeneous interface to realize learning and memory functions, resulting in high fabrication cost, reduced reliability, high energy consumption and uncompact architecture, severely limiting the up-scaled manufacture, and on-chip integration. Here a photo-memory fundamental based on ion-exciton coupling is innovated to simplify synaptic structure and minimize energy consumption. Due to the intrinsic organic/inorganic interface within the crystal, the photodetector based on monolithic 2D perovskite exhibits a persistent photocurrent lasting about 90 s, enabling versatile synaptic functions. The electrical power consumption per synaptic event is estimated to be≈1.45 × 10-16 J, one order of magnitude lower than that in a natural biological system. Proof-of-concept image preprocessing using the neuromorphic vision sensors enabled by photonic synapse demonstrates 4 times enhancement of classification accuracy. Furthermore, getting rid of the artificial neural network, an expectation-based thresholding model is put forward to mimic the human visual system for facial recognition. This conceptual device unveils a new mechanism to simplify synaptic structure, promising the transformation of the NVS and fostering the emergence of next generation neural networks |
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Beschreibung: | Date Completed 02.05.2024 Date Revised 02.05.2024 published: Print-Electronic Citation Status MEDLINE |
ISSN: | 1521-4095 |
DOI: | 10.1002/adma.202311524 |