近年來,球面像差校正穿透式電子顯微鏡影像搭配能譜分析技術,已逐漸成為材料分析中不可或缺的工具。從能譜影像中,藉由適當的數據處理,我們除了原子特徵之外,更可以得到化學元素及電子結構等材料訊息。在本文中,我們介紹一種新穎的演算法-kMLLS 聚類,其結合了 k 平均聚類與複線性最小平方擬合的優點,使我們可以正確地萃取出終端材料能譜,並得到其分佈情形。利用 kMLLS 聚類,未來可應用在線上檢測,使研究人員即時得到更深入的材料訊息。
In recent years, combining a Cs-corrected scanning transmission electron microscope with an EDS and/or EELS detector has become an indispensable tool for material characterization. With a proper data processing, atomic structures, chemical compositions, and electronic configurations of materials can be resolved in a spectrum image. In this article, we introduce a novel algorithm - kMLLS clustering, which combines the advantages of k-means clustering and multiple linear least squares fitting, to accurately extract the spectra of the endmembers and the corresponding distribution from a spectrum image. kMLLS clustering has the great potential to the in-line application and provides significant insights into materials.