A Near-Infrared Retinomorphic Device with High Dimensionality Reservoir Expression

A Near-Infrared Retinomorphic Device with High Dimensionality Reservoir Expression

© 2024 The Author(s). Advanced Materials published by Wiley‐VCH GmbH.

Bibliographische Detailangaben
Veröffentlicht in:Advanced materials (Deerfield Beach, Fla.). - 1998. - 36(2024), 48 vom: 01. Nov., Seite e2411225
1. Verfasser: Leng, Yan-Bing (VerfasserIn)
Weitere Verfasser: Lv, Ziyu, Huang, Shengming, Xie, Peng, Li, Hua-Xin, Zhu, Shirui, Sun, Tao, Zhou, You, Zhai, Yongbiao, Li, Qingxiu, Ding, Guanglong, Zhou, Ye, Han, Su-Ting
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2024
Zugriff auf das übergeordnete Werk:Advanced materials (Deerfield Beach, Fla.)
Schlagworte:Journal Article flash memory high dimensionality reservoir in‐sensor reservoir computing retinomorphic device upconversion materials
Beschreibung
Zusammenfassung:© 2024 The Author(s). Advanced Materials published by Wiley‐VCH GmbH.
Physical reservoir-based reservoir computing (RC) systems for intelligent perception have recently gained attention because they require fewer computing resources. However, the system remains limited in infrared (IR) machine vision, including materials and physical reservoir expression power. Inspired by biological visual perception systems, the study proposes a near-infrared (NIR) retinomorphic device that simultaneously perceives and encodes narrow IR spectral information (at ≈980 nm). The proposed device, featuring core-shell upconversion nanoparticle/poly (3-hexylthiophene) (P3HT) nanocomposite channels, enables the absorption and conversion of NIR into high-energy photons to excite more photo carriers in P3HT. The photon-electron-coupled dynamics under the synergy of photovoltaic and photogating effects influence the nonlinearity and high dimensionality of the RC system under narrow-band NIR irradiation. The device also exhibits multilevel data storage capability (≥8 levels), excellent stability (≥2000 s), and durability (≥100 cycles). The system accurately identifies NIR static and dynamic handwritten digit images, achieving recognition accuracies of 91.13% and 90.07%, respectively. Thus, the device tackles intricate computations like solving second-order nonlinear dynamic equations with minimal errors (normalized mean squared error of 1.06 × 10⁻3 during prediction)
Beschreibung:Date Revised 30.11.2024
published: Print-Electronic
Citation Status PubMed-not-MEDLINE
ISSN:1521-4095
DOI:10.1002/adma.202411225