论文标题
扫描近场光学显微镜中光学常数的深度学习辅助提取
Deep-Learning-Aided Extraction of Optical Constants in Scanning Near-Field Optical Microscopy
论文作者
论文摘要
扫描近场光学显微镜是纳米级系统光谱法最有效的技术之一。但是，由于扫描的探针和样品之间的复杂且高度非线性的相互作用，从测得的近场信号中推断出光学常数可能具有挑战性。适用于此问题的常规拟合方法通常会因缺乏融合或需要人为干预而受到影响。在这里，我们开发了一种替代方法，其中深度学习网络将光学参数提取是自动化的。与传统的对应物相比，我们的方法表明，当应用于模拟的近场光谱时，我们的方法表明了较高的精度，对噪声的稳定性以及计算速度。
Scanning near-field optical microscopy is one of the most effective techniques for spectroscopy of nanoscale systems. However, inferring optical constants from the measured near-field signal can be challenging because of a complicated and highly nonlinear interaction between the scanned probe and the sample. Conventional fitting methods applied to this problem often suffer from the lack of convergence or require human intervention. Here we develop an alternative approach where the optical parameter extraction is automated by a deep learning network. Compared to its traditional counterparts, our method demonstrates superior accuracy, stability against noise, and computational speed when applied to simulated near-field spectra.
