材料科学
光子上转换
维数之咒
非线性系统
油藏计算
近红外光谱
红外线的
光电子学
计算机科学
生物系统
人工智能
光学
人工神经网络
物理
循环神经网络
量子力学
发光
生物
作者
Yan‐Bing Leng,Ziyu Lv,Shengming Huang,Peng Xie,Huaxin Li,Zhu Shi-rui,Tao Sun,Y. Zhou,Yongbiao Zhai,Qingxiu Li,Guanglong Ding,Ye Zhou,Su‐Ting Han
标识
DOI:10.1002/adma.202411225
摘要
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⁻ 3 during prediction).
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