Structure, Morphology and Optical Properties of TiO2 Films Formed by Anodizing in a Mixed Solution of Citric Acid and Sulfamic Acid

Choudhary, R. K.; Sarkar, Partha Sarathi; Biswas, Aniruddha; Mishra, Prerna; Abraham, Geogy J.; Sastry, P. U.; Kain, Vivekanand

doi:10.1007/s11665-017-2818-0

Cited by 5 publications

(5 citation statements)

References 63 publications

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“…With the sputtering power induced energy transfer, ionized radical mobility, and surface diffusion length also increase, leading to the increased possibility of atomic diffusion [ 31 ]. Furthermore, when Ar remains constant, with the increase of N 2 /Ar flow ratio, the proportion of N + or N 2 + increases with the increase of deposition rate [ 32 ]. Therefore, the above power and flow rate changes will be highly reflected in OES data through plasma composition and energy changes.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Assisted Classification of Aluminum Nitride Thin Film Stress via In-Situ Optical Emission Spectroscopy Data

et al. 2021

Materials

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In this study, we submit a complex set of in-situ data collected by optical emission spectroscopy (OES) during the process of aluminum nitride (AlN) thin film. Changing the sputtering power and nitrogen(N2) flow rate, AlN film was deposited on Si substrate using a superior sputtering with a pulsed direct current (DC) method. The correlation between OES data and deposited film residual stress (tensile vs. compressive) associated with crystalline status by X-ray diffraction spectroscopy (XRD), scanning electron microscope (SEM), and transmission electron microscope (TEM) measurements were investigated and established throughout the machine learning exercise. An important answer to know is whether the stress of the processing film is compressive or tensile. To answer this question, we can access as many optical spectra data as we need, record the data to generate a library, and exploit principal component analysis (PCA) to reduce complexity from complex data. After preprocessing through PCA, we demonstrated that we could apply standard artificial neural networks (ANNs), and we could obtain a machine learning classification method to distinguish the stress types of the AlN thin films obtained by analyzing XRD results and correlating with TEM microstructures. Combining PCA with ANNs, an accurate method for in-situ stress prediction and classification was created to solve the semiconductor process problems related to film property on deposited films more efficiently. Therefore, methods for machine learning-assisted classification can be further extended and applied to other semiconductors or related research of interest in the future.

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Section: Resultsmentioning

confidence: 99%

Machine Learning Assisted Classification of Aluminum Nitride Thin Film Stress via In-Situ Optical Emission Spectroscopy Data

et al. 2021

Materials

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“…The molding dependences of the samples were not investigated. The structure of TiO 2 films obtained in a mixture of citric and sulphaminic acids is studied in [15]. Valuable data on the effect of electrolysis conditions on the film structure are given, but there are no data about the formation dynamics.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Establishing the patterns in the formation of oxide films on the alloy Ti6Al4V in carbonic acid solutions

Ivashchenko

Smirnova

Kyselova

et al. 2018

EEJET

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“…Внимание к методу обусловлено тем, что применение электрохимических приемов дает возможность получать пленки различной структуры и широким спектром свойств. Для электрохимического оксидирования используют растворы органических [6; 7], неорганических кислот [8], электролиты, содержащие оба типа кислот или смеси кислот и солей [10][11][12][13][14][15][16], щелочи [17].…”

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Electrochemical Oxidation of the Ti6al4v Alloy in the Solutions of Carboxylic Acids

Smirnova¹,

Pylypenko²

2020

Scientific notes of Taurida National V.I. Vernadsky University.

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Structure, Morphology and Optical Properties of TiO2 Films Formed by Anodizing in a Mixed Solution of Citric Acid and Sulfamic Acid

Cited by 5 publications

References 63 publications

Machine Learning Assisted Classification of Aluminum Nitride Thin Film Stress via In-Situ Optical Emission Spectroscopy Data

Machine Learning Assisted Classification of Aluminum Nitride Thin Film Stress via In-Situ Optical Emission Spectroscopy Data

Establishing the patterns in the formation of oxide films on the alloy Ti6Al4V in carbonic acid solutions

Electrochemical Oxidation of the Ti6al4v Alloy in the Solutions of Carboxylic Acids

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