Advanced image analysis of semiconductor structures

半导体结构的高级图像分析

• 批准号: 2905133
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2905133
• 关键词: Advanced; image; analysis; semiconductor; structures

This project builds on diffraction imaging technology we introduced in EPSRC grant EP/P015719/1 for a scanning electron microscopy technique (SEM), so called "electron channelling contrast imaging" (ECCI). The tools we will develop are also applicable for electron backscatter diffraction (EBSD), another non-destructive technique, but one which is better at measuring and quantifying material deformations. EBSD is commonly applied in metallurgy (for example at Strathclyde's advanced forming centre to examine welds), but within the Physics department, we deploy it for measuring semiconductor material properties, including recently demonstrating that ECCI-like data can also be obtained by this technique. This leads to large quantities of data which encodes information on the morphology, stress and strains, plus the defects that either arise from or produce strain.Interpreting this flood of data is challenging, and while for ECCI we have made progress in finding and identifying extended defects, these are only part of the story. What is needed is an understanding of the meso-scale structure of these materials, which not only requires interpreting structure and strain on a larger scale, but also unpicking the influences which produce these deformations and features.Deep learning image analysis (a core area of current AI research) can rapidly, with reasonable accuracy, pick out features in images, including locating areas of interest for various forms of microscopy. In this project, not only defects, but also regions with net changes in orientation due to the defects (so called sub-grains) will be found and studied in a largely automated way. In metallurgy, these changes are usually ascribed to 'geometrically necessary dislocations', i.e., net shift in crystal orientation because of defects.However, this concept misses contributions which cancel each other out (for example lines of dislocation with alternating Burgers' vectors) and does not explain how these structures arise (i.e. what is happening below the surface and during the growth history of the material to produce this pattern).There is also a wealth of other data available in the SEM (light emission, induced electrical current, x-ray emission, etc.) which correlates with this surface microstructure as well as revealing information from deeper in the material, but which is only occasionally compared against the structural data. The surface morphology, plus information from these other messenger sources, combined with information from samples grown to different thicknesses or thinned after growth enables modelling of how the surface resulted from deeper processes and conditions (growth front coalescence, dislocation annihilation, substrate miscut, ...). This is where other forms of machine learning (ML) and AI can be deployed to gain understanding (not just description) of these materials. By combining generative models and active learning, we can in the second stage of the project move to both simulating the material growth, but more importantly use 'counterfactual AI' to explore the influences that led to the measured data. The counterfactual approaches are normally used in methods to produce explainable models (i.e. avoiding 'black-box' behaviour of ML), but can be deployed to trace the impact and interaction of model inputs and processes. Here, various 'what if?' models are run to disentangle the core influences and predict key variables for the resulting state of the material.

该项目建立在我们在EPSRC Grant EP/P015719/1中引入的衍射成像技术，用于扫描电子显微镜技术（SEM），所谓的“电子通道对比度成像”（ECCI）。我们将开发的工具也适用于电子反向散射衍射（EBSD），这是另一种非破坏性技术，但一种旨在更好地测量和量化材料变形。 EBSD通常用于冶金中（例如，在Strathclyde的高级成型中心检查焊缝），但是在物理部门内，我们将其部署用于测量半导体材料属性，包括最近证明了ECCI样数据也可以通过这项技术获得。这导致大量数据编码有关形态，压力和菌株的信息，以及由于或产生因素而产生的缺陷。解释这一数据泛滥是具有挑战性的，而对于ECCI来说，我们在查找和识别扩展的缺陷方面取得了进展，这些只是故事的一部分。需要的是了解这些材料的中尺度结构，这不仅需要在更大范围内解释结构和压力，而且还需要取消产生这些变形和功能的影响。深入学习图像分析（当前AI研究的核心区域）可以快速，具有合理的精确性，具有出色的精度，在图像中挑选图像中的功能，包括各种形式的图像，包括各种显微镜。在这个项目中，不仅缺陷，而且还会以很大的自动化方式找到和研究由于缺陷（所谓的子元素）而导致的方向变化的区域。在冶金中，这些变化通常归因于“几何必要的脱位”，即由于缺陷而晶体取向的净移位。在SEM中可用（光发射，诱导电流，X射线发射等），与该表面微观结构相关，并揭示了材料中更深层的信息，但仅与结构数据进行比较。表面形态以及来自其他信使来源的信息，再加上从生长到不同厚度的样品或生长后变细的信息，可以建模表面如何由更深的过程和条件（生长前聚结，位错灭绝，底物误导，底物错误，...）。在这里，可以部署其他形式的机器学习（ML）和AI来获得对这些材料的理解（不仅仅是描述）。通过结合生成模型和主动学习，我们可以在项目的第二阶段进行模拟材料增长，但更重要的是使用“反事实AI”来探索导致测量数据的影响。反事实方法通常用于产生可解释模型的方法（即避免ML的“黑盒”行为），但可以部署以追踪模型输入和过程的影响和相互作用。在这里，各种各样的“如果？”模型被运行以消除核心影响，并预测材料所得状态的关键变量。