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 拨款 EP/P015719/1 中引入的扫描电子显微镜技术 (SEM) 的衍射成像技术的基础上，即所谓的“电子通道对比成像”(ECCI)。我们将开发的工具也适用于电子背散射衍射（EBSD），这是另一种非破坏性技术，但更适合测量和量化材料变形。 EBSD 通常应用于冶金领域（例如在 Strathclyde 的先进成型中心检查焊缝），但在物理系内，我们将其用于测量半导体材料特性，包括最近证明也可以通过该技术获得类似 ECCI 的数据。这导致产生大量数据，这些数据编码有关形态、应力和应变的信息，以及由应变引起或产生应变的缺陷。解释这些大量数据具有挑战性，而对于 ECCI 来说，我们在寻找和识别扩展的方面取得了进展。缺陷，这些只是故事的一部分。我们需要的是对这些材料的细观尺度结构的理解，这不仅需要在更大尺度上解释结构和应变，而且还需要揭示产生这些变形和特征的影响。深度学习图像分析（深度学习图像分析的核心领域）当前的人工智能研究）可以以合理的精度快速地挑选出图像中的特征，包括定位各种形式的显微镜的感兴趣区域。在这个项目中，不仅会发现缺陷，而且还会以高度自动化的方式发现和研究由于缺陷而导致方向发生净变化的区域（所谓的亚晶粒）。在冶金学中，这些变化通常归因于“几何上必要的位错”，即由于缺陷而导致的晶体取向的净偏移。然而，这个概念忽略了相互抵消的贡献（例如具有交替伯格斯矢量的位错线）和没有解释这些结构是如何产生的（即表面以下以及在材料的生长历史过程中发生了什么以产生这种图案）。SEM 中还提供了大量其他数据（光发射、感应电）电流、X 射线发射等），它与表面微观结构相关，并揭示材料深处的信息，但只是偶尔与结构数据进行比较。表面形态，加上来自这些其他信使来源的信息，与生长到不同厚度或生长后变薄的样品的信息相结合，可以对更深层次的过程和条件（生长前沿聚结、位错湮灭、基底误切等）如何产生表面进行建模。 .)。这是可以部署其他形式的机器学习 (ML) 和人工智能来理解（而不仅仅是描述）这些材料的地方。通过将生成模型和主动学习相结合，我们可以在项目的第二阶段转向模拟物质增长，但更重要的是使用“反事实人工智能”来探索导致测量数据的影响。反事实方法通常用于生成可解释模型的方法中（即避免机器学习的“黑盒”行为），但可以部署来跟踪模型输入和过程的影响和交互。在这里，各种各样的“如果怎样？”运行模型来理清核心影响并预测材料最终状态的关键变量。