Machine Learning Assisted Classification of Aluminum Nitride Thin Film Stress via In-Situ Optical Emission Spectroscopy Data
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(N<sub>2</sub>) flow rate, AlN film was deposited on Si substrate using a superior spu...
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2021-08-01
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| author | Yu-Pu Yang Te-Yun Lu Hsiao-Han Lo Wei-Lun Chen Peter J. Wang Walter Lai Yiin-Kuen Fuh Tomi T. Li |
| author_facet | Yu-Pu Yang Te-Yun Lu Hsiao-Han Lo Wei-Lun Chen Peter J. Wang Walter Lai Yiin-Kuen Fuh Tomi T. Li |
| author_sort | Yu-Pu Yang |
| collection | DOAJ |
| description | 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(N<sub>2</sub>) 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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| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-10T08:39:21Z |
| publishDate | 2021-08-01 |
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| series | Materials |
| spelling | doaj.art-ac6d8eb1e8734b7dac2829ff782cbd512023-11-22T08:27:38ZengMDPI AGMaterials1996-19442021-08-011416444510.3390/ma14164445Machine Learning Assisted Classification of Aluminum Nitride Thin Film Stress via In-Situ Optical Emission Spectroscopy DataYu-Pu Yang0Te-Yun Lu1Hsiao-Han Lo2Wei-Lun Chen3Peter J. Wang4Walter Lai5Yiin-Kuen Fuh6Tomi T. Li7Department of Mechanical Engineering, National Central University, Taoyuan 32001, TaiwanDepartment of Mechanical Engineering, National Central University, Taoyuan 32001, TaiwanDepartment of Mechanical Engineering, National Central University, Taoyuan 32001, TaiwanDepartment of Mechanical Engineering, National Central University, Taoyuan 32001, TaiwanDelta Electronics Inc., Taoyuan 32063, TaiwanDelta Electronics Inc., Taoyuan 32063, TaiwanDepartment of Mechanical Engineering, National Central University, Taoyuan 32001, TaiwanDepartment of Mechanical Engineering, National Central University, Taoyuan 32001, TaiwanIn 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(N<sub>2</sub>) 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.https://www.mdpi.com/1996-1944/14/16/4445machine learningaluminum nitride (AlN)principal component analysis (PCA)artificial neural networks (ANNs)in-situthin film stress |
| spellingShingle | Yu-Pu Yang Te-Yun Lu Hsiao-Han Lo Wei-Lun Chen Peter J. Wang Walter Lai Yiin-Kuen Fuh Tomi T. Li Machine Learning Assisted Classification of Aluminum Nitride Thin Film Stress via In-Situ Optical Emission Spectroscopy Data Materials machine learning aluminum nitride (AlN) principal component analysis (PCA) artificial neural networks (ANNs) in-situ thin film stress |
| title | Machine Learning Assisted Classification of Aluminum Nitride Thin Film Stress via In-Situ Optical Emission Spectroscopy Data |
| title_full | Machine Learning Assisted Classification of Aluminum Nitride Thin Film Stress via In-Situ Optical Emission Spectroscopy Data |
| title_fullStr | Machine Learning Assisted Classification of Aluminum Nitride Thin Film Stress via In-Situ Optical Emission Spectroscopy Data |
| title_full_unstemmed | Machine Learning Assisted Classification of Aluminum Nitride Thin Film Stress via In-Situ Optical Emission Spectroscopy Data |
| title_short | Machine Learning Assisted Classification of Aluminum Nitride Thin Film Stress via In-Situ Optical Emission Spectroscopy Data |
| title_sort | machine learning assisted classification of aluminum nitride thin film stress via in situ optical emission spectroscopy data |
| topic | machine learning aluminum nitride (AlN) principal component analysis (PCA) artificial neural networks (ANNs) in-situ thin film stress |
| url | https://www.mdpi.com/1996-1944/14/16/4445 |
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