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一种具有高维储层表达的近红外视网膜形态装置。

A Near-Infrared Retinomorphic Device with High Dimensionality Reservoir Expression.

作者信息

Leng Yan-Bing, 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

机构信息

Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, P. R. China.

College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China.

出版信息

Adv Mater. 2024 Nov;36(48):e2411225. doi: 10.1002/adma.202411225. Epub 2024 Oct 10.

Abstract

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⁻ during prediction).

摘要

基于物理储能器的智能感知储能计算(RC)系统最近受到关注,因为它们需要的计算资源较少。然而,该系统在红外(IR)机器视觉方面仍然存在局限性,包括材料和物理储能器的表达能力。受生物视觉感知系统的启发,该研究提出了一种近红外(NIR)视网膜形态器件,它能同时感知并编码窄红外光谱信息(约980纳米)。所提出的器件具有核壳型上转换纳米颗粒/聚(3-己基噻吩)(P3HT)纳米复合通道,能够将近红外光吸收并转换为高能光子,以激发P3HT中更多的光载流子。在光伏效应和光门控效应协同作用下的光子-电子耦合动力学影响了窄带近红外光照射下RC系统的非线性和高维性。该器件还具有多级数据存储能力(≥8级)、出色的稳定性(≥2000秒)和耐久性(≥100次循环)。该系统能够准确识别近红外静态和动态手写数字图像,识别准确率分别达到91.13%和90.07%。因此,该器件能够以最小的误差处理复杂的计算,如求解二阶非线性动力学方程(预测期间归一化均方误差为1.06×10⁻)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ca7/11602693/a3d9fae50f28/ADMA-36-2411225-g004.jpg

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