A machine learning framework for scanning electron diffraction microscopy

扫描电子衍射显微镜的机器学习框架

• 批准号: 543431-2019
• 负责人: Blackburn, ArthurAM
• 金额: $ 8.74万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=762742
• 关键词: machine; learning; framework; scanning; electron

Electron microscopy provides atomic resolution images and information from a vast range of materials. The most common electron microscopes scan a focused electron beam over a sample and record indicators of the beam - sample interaction to construct images. Many thousands of these instruments are used globally and are essential for physical and biological sciences, and nano-technology applications. Surprisingly though, these microscopes disregard vast amounts of information on how the beam interacts with the sample on passing through or scattering from the sample. This follows from the limitations of current generation electron detectors. Recently though, these limits have been shattered by the realization of new pixelated detectors that allow formerly discarded diffraction data to be recorded at rates greater than 1000 beam positions per second. This new technique, termed scanning electron diffraction microscopy, gives a step change in the detail and speed of the microscope output from GB to TB of data per day in typical usage. This thousandfold data volume increase leads to remarkable effective resolution improvements and presents exciting possibilities for new characterization methods, which will be developed in this project. However, the increased information output necessitates that new strategies are developed to process, analyse, store and share these results to achieve the maximum impact and potential of this technique. This project will meet this need by applying the power of machine learning to the data and developing an internationally accessible computational framework. This will allow lessons learnt from the research and trained networks to be easily and efficiently shared, reused and refined. The project will apply the framework to yield new insights into materials sciences in Canada, with first applications in defect identification in electronic devices and characterizing polymer and electron beam sensitive devices such as energy storage devices, photovoltaics and nanoparticles for medical diagnostics and treatments. The project will form a Canadian hub for expertise in this rapidly expanding area of electron microscopy, and will strengthen the partner's research, development and product base in Canada.

电子显微镜提供多种材料的原子分辨率图像和信息。最常见的电子显微镜在样品上扫描聚焦电子束并记录电子束与样品相互作用的指标以构建图像。数以千计的此类仪器在全球范围内使用，对于物理和生物科学以及纳米技术应用至关重要。但令人惊讶的是，这些显微镜忽略了有关光束在穿过样品或从样品散射时如何与样品相互作用的大量信息。这是由于当前一代电子探测器的局限性所致。不过，最近，新型像素化探测器的实现打破了这些限制，这些探测器允许以每秒超过 1000 个光束位置的速率记录以前被丢弃的衍射数据。这种新技术被称为扫描电子衍射显微镜，在典型使用情况下，显微镜输出的细节和速度从每天 GB 级变为 TB 级。数据量的千倍增加带来了显着的有效分辨率提高，并为本项目将开发的新表征方法提供了令人兴奋的可能性。然而，信息输出的增加需要开发新的策略来处理、分析、存储和共享这些结果，以实现该技术的最大影响和潜力。该项目将通过将机器学习的力量应用于数据并开发国际通用的计算框架来满足这一需求。这将使从研究和训练网络中汲取的经验教训能够轻松有效地共享、重用和完善。该项目将应用该框架对加拿大材料科学产生新的见解，首先应用于电子设备中的缺陷识别以及表征聚合物和电子束敏感设备，例如用于医疗诊断和治疗的储能设备、光伏和纳米粒子。该项目将在这一快速扩展的电子显微镜领域形成加拿大的专业知识中心，并将加强合作伙伴在加拿大的研究、开发和产品基础。