Mr. Xiaoyan Wu Profile

Xiaoyan Wu

at China Academy of Engineering Physics

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Proceedings Article | 27 March 2022 Paper

Laser-induced damage threshold and performance degradation of silver nanowires irradiated by a single-pulse laser

Wei Li, Xiaoyan Wu, Lingyuan Wu, Yanglong Li, Bo Fu, Weiping Wang

Proceedings Volume 12169, 121693N (2022) https://doi.org/10.1117/12.2623794

KEYWORDS: Nanowires, Laser damage threshold, Laser irradiation, Laser applications

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Silver nanowires combine excellent conductivity, low surface resistance, high transparency, and good ductility, and have been widely studied and applied to fabrication of many micro-/nanoscale optoelectronic devices, such as transparent conductive films, microelectronic devices, thin-film solar cells, microelectrodes, and biosensors. To obtain the damage and photoelectric characteristics of micro-/nanoscale optoelectronic devices under laser irradiation, and thus to improve their laser-irradiation tolerance and expand applications in strong light-irradiation environments, in this paper, through experiments and theoretical calculations, we studied the laser-induced damage thresholds and damage-induced changes in the photoelectric characteristics of silver nanowires of different diameters exposed to single-pulse laser irradiation at wavelengths of 355, 532, and 1064 nm. Results show that the larger the diameter of silver nanowires, the higher the laser-induced damage threshold. Silver nanowires have the lowest damage threshold when irradiated by a 355-nm laser. The damage thresholds of silver nanowires with diameters of 20 and 40 nm are 0.22 and 1.35 J/cm² , respectively. We have found that when the laser energies are higher than the damage thresholds, melting and fracturing occur at the wire ends and at the strong electric field distribution "node" sites of the silver nanowires, and the sample’s optical absorption rate and sheet resistance also increase. Such degradation of photoelectric performance can affect the absorptivity and conductivity of micro-/nanoscale optoelectronic devices, resulting in the degradation of device performance. The study provides new information for the development and application of micro-/nanoscale optoelectronic devices under strong laser-irradiation conditions.

Proceedings Article | 7 October 2020 Paper

RestoreNet: a deep learning framework for image restoration in optical multi-aperture imaging system

Ju Tang, Kaiqiang Wang, Xiaoyan Wu, Jianglei Di, Guodong Liu, Jianlin Zhao

Proceedings Volume 11571, 1157104 (2020) https://doi.org/10.1117/12.2576299

KEYWORDS: Imaging systems, Image restoration, Optical transfer functions, Point spread functions, Convolution, Neural networks

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Imaging blur is an inevitable problem because of the low response to medium frequency in optical multi-aperture imaging system, in which Wiener filtering is usually used in implementing image restoration to obtain clear highresolution images. Recent notable developments in the field of deep learning have opened up exciting avenues inspiring us to use data-driven approach for image deblurring in optical multi-aperture imaging system. In this paper, a deep learning framework named RestoreNet, which is based on a U-shaped convolution neural network, is proposed to replace the general Wiener filtering for image restoration. Numerical simulation and experiment results show that RestoreNet could recover the imaging map from system successfully, just like Wiener filtering does. However, RestoreNet only needs one dataset containing a few images for training, and shows strong image restoration ability without the point spread function or optical transfer function of system in testing, as well as the priori information of object and noise. As a result, RestoreNet is an effective alternative in image restoration of the optical multi-aperture imaging system.

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