All-Optically Modulated In-Sensor Computing Device Based on Ionic-Conducting CuInP2Se6
© 2025 Wiley‐VCH GmbH.
| Publié dans: | Advanced materials (Deerfield Beach, Fla.). - 1998. - 37(2025), 29 vom: 14. Juli, Seite e2502254 |
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| Auteur principal: | |
| Autres auteurs: | , , , , , , , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2025
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| Accès à la collection: | Advanced materials (Deerfield Beach, Fla.) |
| Sujets: | Journal Article All‐optical modulation In‐sensor computing Ion‐conducting Optoelectronics Synaptic devices |
| Résumé: | © 2025 Wiley‐VCH GmbH. Inspired by the human visual system, in-sensor computing has emerged as a promising approach to address growing demands for real-time image processing while overcoming constraints in computational resources. However, existing in-sensor computing optoelectronic devices still face challenges such as complex heterostructures or limited optical modulation for operational efficiency, restricting their practical use. Here, a simple two-terminal optoelectronic device has been fabricated using the 2D material CuInP2Se6, achieving neuromorphic functionalities through all-optical modulation. The device exhibits a tunable photoresponse across the visible spectrum (400 to 700 nm) and enables bidirectional conductance modulation in response to light stimuli, driven by the interaction between Cu⁺ ions and photogenerated electrons. It shows high linearity with 300 discrete conductance states under red, green, and blue light, enabling color-specific image feature extraction, processing, and recognition across three channels. This approach significantly enhances color image recognition accuracy by 4.6% when integrated with a three-channel convolutional neural network. Additionally, the bidirectional photoresponse allows for efficient noise suppression during color image preprocessing, leading to a 490% improvement in signal-to-noise ratio. These findings highlight the potential of CuInP2Se6-based architecture for robust performance, paving the way for in-sensor neuromorphic vision systems in artificial intelligence and biomimetic computing |
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| Description: | Date Revised 24.07.2025 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1521-4095 |
| DOI: | 10.1002/adma.202502254 |