CDS&E: Thin Film Analysis by XPS: Quantitative Modeling of Sputtering and Depth Profile Data, Machine Learning Classifiers, and Novel Applications

CDS

• 批准号: 2203841
• 负责人: Lev Gelb
• 金额: $ 42万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2203841&HistoricalAwards=false
• 关键词: CDS& Thin; Film; Analysis; XPS

With support from the Chemical Measurement and Imaging program of the Chemistry Division, Lev Gelb and Amy Walker of the University of Texas at Dallas will improve methods for interpreting x-ray photoelectron spectroscopy (XPS) sputter depth profiling data sets and spectra. In XPS, x-rays eject electrons from a sample, which identify the elements present near the sample surface. In depth-profiling, the sample is simultaneously eroded away by blasting (“sputtering”) the sample with a beam of ions, so that the composition at varying depths is also determined. Unfortunately, the x-rays and sputter beam cause unwanted chemical reactions, roughening, interlayer mixing, and other effects which distort the measured composition profiles. By accounting for these effects, this Project improves the quality and reliability of such measurements. Software developed in this project will be freely distributed and promoted as a community resource. XPS is widely used in materials science, nanoscience, semiconductor research, biotechnology and other fields, so improving the performance of this technique will be of significant long-term benefit to society at large.The Project will focus on model-based data analysis. A realistic simulation of the sputter process is used to describe how the sample changes during the experiment, from which XPS spectra are calculated and compared with the collected data. The simulation parameters are then adjusted to give optimal agreement and thus the best estimates of sample properties and sputter rates. These simulations also be leveraged to develop machine-learning data analysis tools. Real XPS data are time-consuming to measure, so assembling a training set of thousands (or more) of such experiments is not practical. Instead, simulations will create training sets of millions of spectra from hypothetical samples. Deep neural network classifiers will then be trained to provide very rapid assignments of sample structure and composition. Finally, these techniques will be used in the analysis of a series of complex samples of both technological and historical interest.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在化学分部的化学测量和成像计划的支持下，达拉斯大学德克萨斯大学的Lev Gelb和Amy Walker将改善解释X射线光电光谱（XPS）溅射深度分析数据集和光谱的方法。在XPS中，来自样品的X射线喷射电子，这些电子识别出样品表面附近的元素。在深度填充的过程中，将样品简单地被带有离子束的样品爆破（“溅射”），以便还确定在不同深度处的组成。不幸的是，X射线和溅射光束会导致不需要的化学反应，粗糙，层间混合以及其他扭曲所测量曲线的影响。通过考虑这些影响，该项目提高了此类测量的质量和可靠性。该项目开发的软件将被自由分发并作为社区资源进行促进。 XPS广泛用于材料科学，纳米科学，半导体研究，生物技术和其他领域，因此改善该技术的性能将对整个社会具有重大的长期利益。该项目将重点介绍基于模型的数据分析。对溅射过程的现实模拟用于描述实验过程中样品的变化，从中计算XPS光谱并将其与收集的数据进行比较。然后调整模拟参数以提供最佳的一致性，从而对样本属性和溅射速率进行最佳估计。这些模拟还可以利用以开发机器学习数据分析工具。真正的XPS数据是耗时的，因此组装成千上万（或更多）此类实验的训练集是不切实际的。取而代之的是，模拟将从假设样本中创建数百万个光谱的训练集。然后，将对深度神经网络分类器进行培训，以提供样品结构和组成的非常快速的分配。最后，这些技术将用于分析技术和历史兴趣的一系列复杂样本。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响评估标准来评估，被认为是珍贵的支持。