隨著半導體科技的發展，各式各樣的新穎材料逐漸地被開發以及元件尺寸的縮小，而如何去有效的分析及運用這些新穎半導體材料的電性特性。有許多種有效的分析量測方法，各個都有不同的優勢及特性。在眾多的測量模式中，掃描電容顯微鏡（SCM）是量測半導體元件重要的測量模式。通過SCM測量技術，我們可以獲得元件內部載子相關的訊息，然而SCM技術要求樣品表面有一層薄絕緣層，所以開發了新的量測技術，在基於靜電力顯微鏡(EFM)技術上的應用，將振幅調製的微波輸入到探針上，藉此量測靜態靜電力獲得電容相關訊息。
結合了商用模擬軟體COMSOL Multiphysics 6.1的AC/DC低頻電磁靜電模組和半導體模組的非接觸電容模擬半導體檢測技術，探討探針與樣品間的電荷交互作用，解釋了一些實驗上電容量測中特殊現象的物理機制。
機器學習不僅有顯著的去雜訊效果，還能透過聚類分群法在空間位置上將不同載子濃度的區域劃分出來，並分別對每個濃度區域的電壓譜進行主成分分析，從而分離出較乾淨的電容特徵曲線。我們也可以利用聚類分群法選取不同濃度進行分析，或者手動選取局部範圍進行分析，以彌補掃描電容顯微鏡在摻雜濃度靈敏度方面的不足。在標準樣品中，不同結構對應不同的摻雜濃度及種類，每個濃度對應不同的特徵曲線，我們可以整理出濃度與曲線之間的相對應關係。
有限元素法的模擬幫助我們更了解不同參數是如何影響電容特徵曲線的行為，例如探針針尖的半徑大小和樣品表面氧化層厚度對電容訊號的強度和位置有影響。
掃描電容顯微鏡對樣品的製備要求較高，需要在表面形成一層薄的絕緣層。因此，我們引入了振幅調製靜電力顯微鏡，這套量測方法不需要特殊樣品製備，並且可以量測到被掩埋在基板內部的摻雜結構，這對於半導體元件的量測具有優勢。振幅調製靜電力顯微鏡使用微波訊號產生器來對探針進行振幅調製，以獲得電容訊號。嘗試以量測不同直流偏壓下的高度譜來獲得電容電壓的特徵曲線，但關於摻雜濃度的定量分析還需要更進一步改善量測方法。

With the rapid advancements in semiconductor technology and the development of various novel materials, as well as the miniaturization of device sizes, effective analysis and utilization of the electrical properties of these novel semiconductor materials have become crucial. There are numerous effective analysis and measurement methods, each with its advantages and characteristics. Among them, Scanning Probe Microscopy (SPM) is an efficient and non-invasive measurement technique. The application of Atomic Force Microscopy (AFM) within SPM is particularly widespread as it allows for effective nanoscale measurements and analysis without damaging the sample itself. When combined with the increasingly mature technology of Machine Learning, the analyzed data can reveal hidden information, and with the help of appropriate models and simulations, a clear understanding of the phenomena occurring during the measurements can be achieved, leading to better interpretation of experimental results.
In measurements related to semiconductor materials, obtaining information about carrier properties inside the sample is often required. Scanning Capacitance Microscopy (SCM) is widely used for this purpose, enabling the observation of tiny variations in sample capacitance to analyze carrier concentration and types. However, factors influencing changes in sample capacitance go beyond just carrier type and concentration, including probe geometry, surface oxide thickness, trapped charges, defects, and sample size, among others.
In this study, we combined the measurements of Scanning Capacitance Microscopy with simulations using the Finite Element Method to explain the behavior of atypical capacitance characteristic curves. Additionally, we employed Machine Learning to analyze experimental data from Scanning Capacitance Microscopy to extract more hidden carrier information.
Due to the limitations of Scanning Capacitance Microscopy concerning sample structure, we established a new electrical characterization technique called Modulated Electrostatic Force Microscopy. This technique is based on the principles of electrostatic force microscopy and enables the measurement of dopant structures buried within silicon substrates. By coupling this technique with Finite Element Method simulations of capacitance-voltage curves, capacitance-height curves, capacitance-frequency curves, and other concentration-dependent parameters, quantitative measurements can be achieved. Furthermore, it can serve as a defect detector for analyzing flaws in semiconductor devices.

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