论文标题
使用连贯的傅立叶散射测量法，用于在表面上检测纳米颗粒的机器学习技术
Machine learning techniques applied for detection of nanoparticles on surfaces using Coherent Fourier Scatterometry
论文作者
论文摘要
我们提出了一个有效的机器学习框架，用于检测和分类纳米颗粒在远场在具有相干傅立叶散射测量学（CFS）中检测到的表面上。我们研究被球形聚苯乙烯（PSL）纳米颗粒污染的硅晶片（直径降低至$λ/8 $）。从原始数据开始，提出的框架进行了预处理和粒子搜索。此外，定制了无监督的聚类算法（例如K-均值和DBSCAN），用于定义归因于单个散射器的信号组。最后，生成粒子计数与粒径直方图。 处理了数据集中高密度，噪声和漂移的挑战性案例。我们利用先前有关散射器大小的信息，以最大程度地减少错误检测，从而提供更高的歧视能力和更准确的粒子计数。进行了数值和真实的实验，以证明所提出的搜索和集群评估技术的性能。我们的结果表明，所提出的算法可以正确有效地检测表面污染物。
We present an efficient machine learning framework for detection and classification of nanoparticles on surfaces that are detected in the far-field with Coherent Fourier Scatterometry (CFS). We study silicon wafers contaminated with spherical polystyrene (PSL) nanoparticles (with diameters down to $λ/8$). Starting from the raw data, the proposed framework does the pre-processing and particle search. Further, the unsupervised clustering algorithms, such as K-means and DBSCAN, are customized to be used to define the groups of signals that are attributed to a single scatterer. Finally, the particle count versus particle size histogram is generated. The challenging cases of the high density of scatterers, noise and drift in the dataset are treated. We take advantage of the prior information on the size of the scatterers to minimize the false-detections and as a consequence, provide higher discrimination ability and more accurate particle counting. Numerical and real experiments are conducted to demonstrate the performance of the proposed search and cluster-assessment techniques. Our results illustrate that the proposed algorithm can detect surface contaminants correctly and effectively.
