Ran Wenhao, Wang Lili, Zhao Shufang, Wang Depeng, Yin Ruiyang, Lou Zheng, Shen Guozhen
State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences and Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing, 100083, China.
State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, 130012, P. R. China.
Adv Mater. 2020 Apr;32(16):e1908419. doi: 10.1002/adma.201908419. Epub 2020 Feb 27.
Infrared (IR) photodetectors are a key optoelectronic device and have thus attracted considerable research attention in recent years. Photosensitivity is an increasingly important device performance parameter for nanoscale photodetectors and image sensors, as it determines the ultimate imaging quality and contrast. However, photosensitivities of state-of-the-art low-dimensional nanostructure-based IR detectors are considerably low, limiting their practical applications. Herein, a biomimetic IR detection amplification (IRDA) system that boosts photosensitivity by several orders of magnitude by introducting nanowire field effect transistors (FETs), resulting in a peak photosensitivity of 7.6 × 10 under an illumination of 1342 nm, is presented. Consequently, high-contrast imaging of IR light is obtained on the flexible IRDA arrays. The image information can be then trained and recognized by an artificial neural network for higher image-recognition efficiency. This work provides a new perspective for developing high-performance IR imaging systems, and is expected to undoubtedly enlighten future work on artificial intelligence and biorobotic systems.
红外（IR）光电探测器是一种关键的光电器件，近年来因此吸引了大量的研究关注。对于纳米级光电探测器和图像传感器而言，光敏性是一个越来越重要的器件性能参数，因为它决定了最终的成像质量和对比度。然而，基于最先进的低维纳米结构的红外探测器的光敏性相当低，限制了它们的实际应用。在此，提出了一种仿生红外检测放大（IRDA）系统，通过引入纳米线场效应晶体管（FET）将光敏性提高几个数量级，在1342 nm光照下实现了7.6×10的峰值光敏性。因此，在柔性IRDA阵列上获得了红外光的高对比度成像。然后，图像信息可以通过人工神经网络进行训练和识别，以提高图像识别效率。这项工作为开发高性能红外成像系统提供了新的视角，无疑有望启发未来在人工智能和生物机器人系统方面的工作。
