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.

在化学系化学测量和成像项目的支持下，德克萨斯大学达拉斯分校的 Lev Gelb 和 Amy Walker 将改进解释 X 射线光电子能谱 (XPS) 溅射深度剖析数据集和光谱的方法。在 XPS 中，X 射线从样品中发射电子，从而识别样品表面附近存在的元素。在深度剖析中，通过用离子束喷射（“溅射”）样品，同时腐蚀掉样品，从而也确定了不同深度的成分。遗憾的是，X 射线和溅射束会引起不必要的化学反应、粗糙化、层间混合和其他影响，从而扭曲测量的成分分布。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作为社区资源免费分发和推广。 XPS广泛应用于材料科学、纳米科学、半导体研究、生物技术等领域，因此提高该技术的性能将对整个社会产生重大的长期利益。该项目将重点关注基于模型的数据分析。对溅射过程的真实模拟用于描述样品在实验过程中如何变化，从中计算 XPS 光谱并与收集的数据进行比较。然后调整模拟参数以获得最佳一致性，从而获得样品特性和溅射速率的最佳估计。这些模拟还可用于开发机器学习数据分析工具。测量真正的 XPS 数据非常耗时，因此组装数千（或更多）此类实验的训练集是不切实际的。相反，模拟将从假设样本中创建数百万个光谱的训练集。然后深度神经网络分类器将被训练以提供样本结构和组成的快速分配。最后，这些技术将用于分析一系列具有技术和历史意义的复杂样本。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。