Machine Learning-Based Diffractive Image Analysis with Subwavelength Resolution
Machine Learning-Based Diffractive Image Analysis with Subwavelength Resolution
复制标题
基于机器学习的亚波长分辨率衍射图像分析
DOI：
10.1021/acsphotonics.1c00205
复制
发表时间：
2021
期刊：
影响因子：
7
通讯作者：
Podolskiy, Viktor A.
中科院分区：
文献类型：
--
作者：
Ghosh, Abantika;Roth, Diane J.;Nicholls, Luke H.;Wardley, William P.;Zayats, Anatoly V.;Podolskiy, Viktor A.
小目标的远场分析受到衍射极限的严重限制。现有的实现亚衍射分辨率的工具通常利用通过扫描或标记的逐点图像重建。在这里，我们提出了一种新的技术，能够快速和准确的二维结构的表征与至少λ0/25的理论分辨率，基于一个单一的远场强度测量。在实验中，我们实现了这项技术，用845 nm激光解决了180 nm尺度的特征，这是我们可用的最小特征，达到了λ0/5的分辨率。对机器学习算法进行了全面分析，以深入了解学习过程并了解系统中的亚波长信息流。图像参数化，适用于衍射配置和高度容忍的随机噪声，发展。所提出的技术可应用于具有高空间分辨率、快速数据采集和人工智能的新型光学表征工具，如高速纳米尺度计量和质量控制，并可进一步发展为高分辨率光谱学。
Far-field analysis of small objects is severely constrained by the diffraction limit. Existing tools achieving subdiffraction resolution often utilize point-by-point image reconstruction via scanning or labeling. Here, we present a new technique capable of fast and accurate characterization of two-dimensional structures with at least λ0/25 theoretical resolution, based on a single far-field intensity measurement. Experimentally, we realized this technique resolving 180 nm-scale features, the smallest available to us, with 845 nm laser light, reaching a resolution of λ0/5. A comprehensive analysis of machine learning algorithms was performed to gain insight into the learning process and to understand the flow of subwavelength information through the system. Image parametrization, suitable for diffractive configurations and highly tolerant to random noise, was developed. The proposed technique can be applied to new optical characterization tools with high spatial resolution, fast data acquisition, and artificial intelligence, such as high-speed nanoscale metrology and quality control, and can be further developed to high-resolution spectroscopy.
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DOI：
10.1007/978-981-10-5152-4
发表时间：
2019
期刊：
Deep Learning in Object Detection and Recognition
影响因子：
--
作者：
Xiaoyue Jiang;A. Hadid;Yanwei Pang;Eric Granger;Xiaoyi Feng
通讯作者：
Xiaoyue Jiang;A. Hadid;Yanwei Pang;Eric Granger;Xiaoyi Feng
影响因子：
3.8
作者：
Inampudi, Sandeep;Kuhta, Nicholas;Podolskiy, Viktor A.
通讯作者：
Podolskiy, Viktor A.
影响因子：
8.6
作者：
A. Sentenac;P. Chaumet;K. Belkebir
通讯作者：
A. Sentenac;P. Chaumet;K. Belkebir
尺度混合显微镜：超越衍射极限的远场成像
DOI：
--
发表时间：
2016
期刊：
影响因子：
--
作者：
Bo Fan;C. Roberts;N. Olivier;William P. Wardley;S. Inampudi;W. Dickson;A. Zayats;V. Podolskiy
通讯作者：
V. Podolskiy