基于先验信息的二维纳米物体超分辨光学计量

中文摘要 AI 辅助

先前的研究表明，利用相似物体的先验信息训练计量估计器，可以提升一维物体（如狭缝宽度）的光学计量精度。在此，我们展示了纳米尺度椭圆颗粒的单次光学计量，通过分析其衍射图案，利用基于不同尺寸和取向的纳米椭圆先验信息训练的神经网络估计器，反演其长度、宽度和面内取向。我们使用Fisher信息流分析来优化计量装置的物理参数，以最大化测量精度。利用633纳米激光，我们测量椭圆颗粒的尺寸，精度低至λ/128（对应4.9纳米），并以5°的精度恢复其取向。我们的结果证明了光学、深度超分辨、单次、多参数测量二维亚波长物体的实用性，对微生物学和纳米技术应用具有潜在意义。

英文摘要

Previous work has shown that optical metrology of one-dimensional objects, such as slit width, can achieve improved accuracy by using prior information from similar objects to train the metrology estimator. Here, we demonstrate single-shot optical metrology of nanoscale elliptical particles by analysing their diffraction patterns to retrieve length, width and in-plane orientation using a neural-network estimator trained on prior information from nano-ellipses with varied dimensions and orientations. Fisher-information flow analysis was used to optimise the physical parameters of the metrology apparatus and maximise measurement accuracy. Using a 633 nm laser, we measure the dimensions of elliptical particles with accuracy down to $λ$/128, corresponding to 4.9 nm, and recover their orientation with 5$°$ accuracy. Our results demonstrate the practicality of optical, deep-super-resolution, single-shot, multiparameter measurements of two-dimensional subwavelength objects, with potential relevance to microbiology and nanotechnology applications.

发表机构

• Nanyang Technological University（南洋理工大学）
• University of Southampton（南安普顿大学）
• Texas A&M University（德克萨斯农工大学）

机构由 AI 辅助整理，请以论文原文为准。