论文标题
机器学习 - 基于下波长分辨率的衍射成像
Machine learning -- based diffractive imaging with subwavelength resolution
论文作者
论文摘要
小物体的远场表征受到衍射极限的严重限制。实现子分配分辨率的现有工具通常通过扫描或标签利用点映像重建。在这里，我们提出了一种基于单个远场强度测量值的新成像技术，该技术能够快速准确地表征具有至少波长/25分辨率的二维结构。在实验上，我们意识到了这项技术，可以解决我们180 nm尺寸的最小功能，具有532 nm激光光。对机器学习算法进行了全面分析，以洞悉学习过程，并了解通过系统的次波长信息的流动。图像参数化，适用于衍射构型，并且对随机噪声的高度耐受性。该提出的技术可以应用于具有高空间分辨率，快速数据获取和人工智能的新特征工具，例如高速纳米级计量和质量控制，并且可以进一步开发到高分辨率光谱
Far-field characterization of small objects is severely constrained by the diffraction limit. Existing tools achieving sub-diffraction resolution often utilize point-by-point image reconstruction via scanning or labelling. Here, we present a new imaging technique capable of fast and accurate characterization of two-dimensional structures with at least wavelength/25 resolution, based on a single far-field intensity measurement. Experimentally, we realized this technique resolving the smallest-available to us 180-nm-scale features with 532-nm laser light. 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 parameterization, suitable for diffractive configurations and highly tolerant to random noise was developed. The proposed technique can be applied to new 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
