材料科学
多光谱图像
光子学
调制(音乐)
异质结
光电子学
结构着色
光学
光子晶体
人工智能
计算机科学
物理
声学
作者
Yu Han,Jun Li,Shaowen Hao,Wan‐Zhen Fo,Liwen Cao,Mengjiao Li,Yanan Liu,Jianhua Zhang
标识
DOI:10.1002/adfm.202512891
摘要
Abstract Photonic synaptic transistors, designed to emulate the multispectral light detection of retinal neurons, play a pivotal role in advancing artificial vision systems capable of full‐color image recognition. However, their limited sensitivity to weak and multispectral light significantly hampers their application in full‐color image capture and recognition. In this work, a p‐i‐n structured synaptic transistor is introduced that improves weak light sensitivity and enhances synaptic performance by addressing interface issues. By integrating P3HT/LiF/InZnGdO nanofibers as p‐i‐n structure, the spectral response is successfully broadened from visible to near‐UV region, achieving sensitivity to light intensities as low as 0.05 mW cm −2 . The device exhibits remarkable PPF characteristics across the wavelength range of 395–633 nm, with the PPF index showing a maximum increase of 84% compared to the single InZnGdO nanofiber device. Additionally, multiple neural networks are constructed with data from the synaptic transistor array driven by wavelength‐tunable synaptic plasticity to validate the device's feasibility in various application scenarios. The proposed device achieved a recognition accuracy of up to 95.26% for high‐precision full‐color images based on the public CIFAR‐10 dataset. This p‐i‐n structure offers a novel pathway to enhance the synaptic performance of metal oxide‐based synaptic transistors, advancing the development of next‐generation artificial vision.
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