Large scale data analysis and machine learning assisted prediction of a–Si:H to nc–Si:H transition based on Classifiers of OES，PCA and SVM
| Autor: | Song-Ho Wang, 王淞禾 |
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| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 Plasma Enhanced Chemical Vapor Deposition (PECVD) has been used to improve the efficiency and stability of tantalum film optoelectronic components, including the nc–Si:H deposition film. In this paper, the nanocrystalline silicon thin films were deposited on Si substrate by PECVD from source gas of trichlorosilane (TCS, SiHCl3) at temperatures 350°C. The in–situ plasma monitoring and the resultant deposited film properties of Hydrogen chloride silicon thin film were characterized by Optical emission spectroscopy (OES), Fourier transfer infrared spectroscopy (FTIR), Raman spectroscopy (RS), X-ray Diffraction(XRD), Transmission electron microscope(TEM) and Alpha–Step profiler. In addition, principal component analysis (PCA) based on large scale OES dataset was performed and through the proposed PC1–DEV algorithm, the high–dimensional OES data of complexity should be selected and reduced to radicals of interest (SiCl2*, SiCl3*, Hα and Hβ). The value of crystalline phase (VCP) was established to differentially characterize the nanocrystalline phase as mean VCP of 0.11 and the control limits of 0.06, which can be used as the in–situ monitoring tool for crystalline phase characterization. And demonstrates use the large plasma data for PCA analysis connect with SVM algorithm method for the screening and grouping of nanocrystalline and amorphous OES spectra data, and make a decision with strong classifier performance. The support vector machine(SVM) method can classification of Hydrogen chloride silicon thin film, and using three different kernel function methods, include linear kernel, the polynomial kernel and the radial basis (Gaussian) function kernel. In this study the radial basis function kernel algorithm is the best training function used to be judgment and radial basis function kernel is selected to learn a high-smart judgment of the structure model of hydrogen chloride crystal film with an accuracy of 98%. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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