球面像差校正掃描穿透式電子顯微鏡逐漸成為材料分析中不可或缺的工具之一，其提供超高解析度；搭配著能譜分析，則可以提供材料研發人員許多寶貴的材料訊息。在本論文中，我們將以半導體材料作為案例，說明如何利用球面像差校正掃描穿透式電子顯微鏡來進行材料微結構分析。
本論文分為三個部份，分別以磊晶薄膜、二維材料之分析以及機器學習能譜分析開發進行說明。在第一部份中，說明了如何從高分辨原子影像中了解到磊晶薄膜與基板間的相互排列關係、薄膜內應力以及界面原子結構。二維材料分析方面，則會以影像處理的方式，提高影像對比進而提昇原子辨識程度。接著利用ADF影像中原子序對比之應用，進行二維材料中不同元素之定性分析。最後以影像模擬的輔助，判斷材料原子結構。最後一個章節中將介紹一種改良自機器學習演算法之混合能譜分離技術開發與應用，幫助我們自動地從能譜影像中萃取出終端材料能譜，並得到真實的分佈情形。

Spherical aberration corrected scanning transmission electron microscopy is becoming an indispensable tool for materials analysis. By providing ultra-high resolution, combined with spectroscopy, it provides valuable information to researchers. In this study, we use semiconductor materials as a case study to illustrate how spherical aberration corrected scanning electron microscopes can be used to perform qualitative microstructural analysis of materials.
In this paper, we divided into three parts. In the first part, we explain how to understand the orientation relationship, what is the internal stresses, and the interfacial atomic structure between epitaxial film and substrate from high-resolution ADF images.
In the second part, we will focus on the analysis to the 2D materials. Image processing are used to improve the image contrast and then to identify the elements in the image. Then, histogram analysis is used to qualitatively analyze the different elements in the two-dimensional material. Finally, the similar structure of the material is identified with the help of image simulation.
In the last section, we present the development and application of a hybrid energy spectral separation technique modified from a machine learning algorithm to help us to automatically extract endmembers from spectral images and obtain its distribution.

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