paper_authors: Yifeng Shao, Sven Weerdenburg, Jacob Seifert, H. Paul Urbach, Allard P. Mosk, Wim Coene
for: This paper aims to demonstrate the potential of ptychographic extreme ultraviolet (EUV) diffractive imaging as an efficient and accurate metrology tool for the semiconductor industry.
methods: The paper introduces a novel algorithm that enables wavelength-multiplexed reconstruction, which enhances the measurement throughput and introduces data diversity, allowing for accurate characterization of sample structures. The algorithm uses a modal approach to represent the cross-density function of the illumination by a series of mutually incoherent and independent spatial modes.
results: The proposed algorithm was tested on a mainstream machine learning platform, and the results demonstrate the algorithm’s capacity to accommodate experimental uncertainties and achieve a resolution approaching the diffraction limit in reflection geometry. The reconstruction of wafer samples with 20-nm high patterned gold structures on a silicon substrate highlights the ability to handle complex physical interrelations involving a multitude of parameters.Abstract
Ptychographic extreme ultraviolet (EUV) diffractive imaging has emerged as a promising candidate for the next-generation metrology solutions in the semiconductor industry, as it can image wafer samples in reflection geometry at the nanoscale. This technique has surged attention recently, owing to the significant progress in high-harmonic generation (HHG) EUV sources and advancements in both hardware and software for computation. In this study, a novel algorithm is introduced and tested, which enables wavelength-multiplexed reconstruction that enhances the measurement throughput and introduces data diversity, allowing the accurate characterisation of sample structures. To tackle the inherent instabilities of the HHG source, a modal approach was adopted, which represents the cross-density function of the illumination by a series of mutually incoherent and independent spatial modes. The proposed algorithm was implemented on a mainstream machine learning platform, which leverages automatic differentiation to manage the drastic growth in model complexity and expedites the computation using GPU acceleration. By optimising over 200 million parameters, we demonstrate the algorithm's capacity to accommodate experimental uncertainties and achieve a resolution approaching the diffraction limit in reflection geometry. The reconstruction of wafer samples with 20-nm heigh patterned gold structures on a silicon substrate highlights our ability to handle complex physical interrelations involving a multitude of parameters. These results establish ptychography as an efficient and accurate metrology tool.
摘要
弹性极紫外（EUV）探测技术在半导体工业中 emerged as a promising candidate for the next-generation metrology solutions, as it can image wafer samples in reflection geometry at the nanoscale. This technique has attracted significant attention recently, due to the significant progress in high-harmonic generation (HHG) EUV sources and advancements in both hardware and software for computation. In this study, a novel algorithm is introduced and tested, which enables wavelength-multiplexed reconstruction that enhances the measurement throughput and introduces data diversity, allowing the accurate characterization of sample structures. To tackle the inherent instabilities of the HHG source, a modal approach was adopted, which represents the cross-density function of the illumination by a series of mutually incoherent and independent spatial modes. The proposed algorithm was implemented on a mainstream machine learning platform, which leverages automatic differentiation to manage the drastic growth in model complexity and expedites the computation using GPU acceleration. By optimizing over 200 million parameters, we demonstrate the algorithm's capacity to accommodate experimental uncertainties and achieve a resolution approaching the diffraction limit in reflection geometry. The reconstruction of wafer samples with 20-nm height patterned gold structures on a silicon substrate highlights our ability to handle complex physical interrelations involving a multitude of parameters. These results establish ptychography as an efficient and accurate metrology tool.
Lightweight Framework for Automated Kidney Stone Detection using coronal CT images
results: 实验结果表明，该方案可以在8%的原始训练数据上达到竞争力的结果，其中F1分数为96%，false negative率为4%。同时，每个CT图像的检测时间平均为0.62秒。Abstract
Kidney stone disease results in millions of annual visits to emergency departments in the United States. Computed tomography (CT) scans serve as the standard imaging modality for efficient detection of kidney stones. Various approaches utilizing convolutional neural networks (CNNs) have been proposed to implement automatic diagnosis of kidney stones. However, there is a growing interest in employing fast and efficient CNNs on edge devices in clinical practice. In this paper, we propose a lightweight fusion framework for kidney detection and kidney stone diagnosis on coronal CT images. In our design, we aim to minimize the computational costs of training and inference while implementing an automated approach. The experimental results indicate that our framework can achieve competitive outcomes using only 8\% of the original training data. These results include an F1 score of 96\% and a False Negative (FN) error rate of 4\%. Additionally, the average detection time per CT image on a CPU is 0.62 seconds. Reproducibility: Framework implementation and models available on GitHub.
摘要
每年美国医院访问量因肾石病已经达到数百万次。 computed tomography（CT）扫描成为了肾石的efficient检测标准影像技术。各种利用卷积神经网络（CNN）的方法已经被提议用于自动诊断肾石。然而，随着临床实践中的edge设备应用得到越来越多的关注，快速和高效的CNN在edge设备上进行训练和推理已成为一个热点。在这篇论文中，我们提出了一种轻量级融合框架，用于肾部检测和肾石诊断在横截 CT 图像上。我们的设计目标是尽量降低训练和推理的计算成本，实现自动化的方法。实验结果表明，我们的框架可以在只使用8%的原始训练数据时达到竞争力的结果，其中F1分数为96%，False Negative（FN）错误率为4%。此外，在CPU上处理每个 CT 图像的检测时间平均为0.62秒。可重现：框架实现和模型可以在 GitHub 上找到。