博碩士論文 107323605 詳細資訊




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姓名 楊毓璞(Yu-Pu Yang)  查詢紙本館藏   畢業系所 機械工程學系
論文名稱 基於電漿發射光譜數據之人工神經網路輔助氮化鋁薄膜的應力分析與預測
(Artificial neural network assisted stress analysis and prediction of aluminum nitride thin films based on optical emission spectroscopy data)
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摘要(中) 在本研究中,我們提交了由光學發射光譜(optical emission spectroscopy, OES)收集的復雜的及時電漿數據。在不考慮複雜因素的情況下,從一組復雜的物理參數,如氮化鋁(Aluminum Nitride, AlN)薄膜的殘餘應力獲取了一系列解決方案。AlN具有較高的應力穩定性、熱穩定性和化學穩定性,我們採用脈沖直流電濺射法在矽基板上沉積AlN。我們想要知道的一個重要答案是,沈積的薄膜的應力是壓縮的還是拉伸的。為了回答這個問題,我們可以訪問任意多的光譜數據,記錄數據生成一個庫,並利用主成分分析(Principal Component Analysis, PCA)來降低復雜數據的復雜性。經過PCA預處理後,我們試圖證明我們是否可以採用標準的人工神經網路(Artificial Neural Network, ANN),以獲得一個足夠解析度的機器思維分類方法來區分AlN薄膜的應力類型。因此,通過這些機器學習練習,這些輔助分類可以擴展到未來其他感興趣的半導體研究。
摘要(英) In this study, we present complex real-time plasma data collected by optical emission spectroscopy (OES). A series of solutions were obtained from a set of complex physical parameters, such as the residual stress of Aluminum Nitride (AlN) films, without taking into account complex factors. AlN has high stress stability, thermal stability and chemical stability. AlN was deposited on silicon substrate by pulsed direct current sputtering. One of the key answers we want to know is whether the stresses on the deposited film are compressed or stretched. To answer this question, we can access as much spectral data as we want, record the data to generate a library, and use Principal Component Analysis (PCA) to reduce the complexity of complex data. After PCA pretreatment, we tried to prove whether we could use standard Artificial Neural networks (ANN) to obtain a machine-mind classification method with sufficient resolution to distinguish stress types in AlN films. Therefore, through these machine learning exercises, these auxiliary classifications can be extended to other interesting semiconductor research in the future.
關鍵字(中) ★ 機器學習
★ 主成份分析
★ 氮化鋁
★ 薄膜應力
★ 光放射光譜
關鍵字(英) ★ Machine Learning
★ Principal components analysis
★ Aluminum Nitride
★ Thin Film Stress
★ Optical Emission Spectroscopy
論文目次 中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
圖目錄 v
表目錄 vi
一、緒論 1
1-1 前言 1
1-2 研究背景 2
1-3 研究目的 3
二、研究理論 4
2-1 薄膜沈積原理 4
2-2 薄膜材料介紹 7
2-3 薄膜濺鍍方法 9
2-4 薄膜應力量測 12
2-5 機器學習 15
2-6 多層感知器 16
2-7 主成分分析法 18
三、研究內容與方法 21
3-1 實驗流程 21
3-2 薄膜成長 22
3-2-1 試片準備 22
3-2-2 製程準備 23
3-2-3 製程監測與數據收集 23
3-3 薄膜品質偵測與量測 25
3-3-1 電漿發射光譜 25
3-3-2 X射線繞射儀 25
3-3-3 掃描電子顯微鏡 27
3-3-4 穿透式電子顯微鏡 28
3-4 數據處理 30
3-4-1 數據類型與結構 30
3-4-2 數據預處理 30
3-4-3 利用主成份分析法進行數據處理 31
3-5 機器學習模型建立與預測 35
3-5-1 神經網路搭建 35
3-5-2 模型訓練 39
3-5-3 利用模型預測 45
四、實驗結果與討論 47
4-1 薄膜結晶與應力 47
4-2 薄膜微觀結構分析 50
4-3 神經網路測試與選擇 52
4-4 神經網路最佳化預測與驗證 58
五、結論與未來展望 61
參考文獻 62
參考文獻 (1) Mitchell, T. M. (1997). Machine learning.
(2) Ueno, M., Onodera, A., Shimomura, O., & Takemura, K. (1992). X-ray observation of the structural phase transition of aluminum nitride under high pressure. Physical Review B, 45(17), 10123.
(3) Zhao, Y., Zhu, C., Wang, S., Tian, J. Z., Yang, D. J., Chen, C. K., ... & Hing, P. (2004). Pulsed photothermal reflectance measurement of the thermal conductivity of sputtered aluminum nitride thin films. Journal of applied physics, 96(8), 4563-4568.
(4) Dubois, M. A., & Muralt, P. (1999). Properties of aluminum nitride thin films for piezoelectric transducers and microwave filter applications. Applied Physics Letters, 74(20), 3032-3034.
(5) Muggleton, S. (2014). Alan Turing and the development of Artificial Intelligence. AI communications, 27(1), 3-10.
(6) Copeland, B. J. (Ed.). (2004). The essential turing. Clarendon Press.
(7) Wei, J., Chu, X., Sun, X. Y., Xu, K., Deng, H. X., Chen, J., ... & Lei, M. (2019). Machine learning in materials science. InfoMat, 1(3), 338-358.
(8) Zhai, X., Oliver, A., Kolesnikov, A., & Beyer, L. (2019). S4l: Self-supervised semi-supervised learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 1476-1485).
(9) Ryman-Tubb, N. F., Krause, P., & Garn, W. (2018). How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark. Engineering Applications of Artificial Intelligence, 76, 130-157.
(10) Ren, T., Modest, M. F., Fateev, A., Sutton, G., Zhao, W., & Rusu, F. (2019). Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements. Applied Energy, 252, 113448.
(11) Lai, C. Y., Santos, S., & Chiesa, M. (2019). Machine learning assisted quantification of graphitic surfaces exposure to defined environments. Applied Physics Letters, 114(24), 241601.
(12) Kutsukake, K., Nagai, Y., Horikawa, T., & Banba, H. (2020). Real-time prediction of interstitial oxygen concentration in Czochralski silicon using machine learning. Applied Physics Express, 13(12), 125502.
(13) Martinez, A. M., & Kak, A. C. (2001). Pca versus lda. IEEE transactions on pattern analysis and machine intelligence, 23(2), 228-233.
(14) Roweis, S. (1998). EM algorithms for PCA and SPCA. Advances in neural information processing systems, 626-632.
(15) Pinkus, A. (1999). Approximation theory of the MLP model. Acta Numerica 1999: Volume 8, 8, 143-195.
(16) Vishwanathan, S. V. M., & Murty, M. N. (2002, May). SSVM: a simple SVM algorithm. In Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN′02 (Cat. No. 02CH37290) (Vol. 3, pp. 2393-2398). IEEE.
(17) Dhanalakshmi, P., Palanivel, S., & Ramalingam, V. (2009). Classification of audio signals using SVM and RBFNN. Expert systems with applications, 36(3), 6069-6075.
(18) Vossen, J. L., Kern, W., & Kern, W. (Eds.). (1991). Thin film processes II (Vol. 2). Gulf Professional Publishing.
(19) Bahuguna, G., & Mishra, N. K. (2016). Thin Film Coating. Research Journal of Chemical, 6(7), 65-72.
(20) Arndt, D. P., Azzam, R. M. A., Bennett, J. M., Borgogno, J. P., Carniglia, C. K., Case, W. E., ... & Thonn, T. F. (1984). Multiple determination of the optical constants of thin-film coating materials. Applied Optics, 23(20), 3571-3596.
(21) Hsieh, H. H., Kamiya, T., Nomura, K., Hosono, H., & Wu, C. C. (2008, May). P‐29: Modeling of Amorphous Oxide Semiconductor Thin Film Transistors and Subgap Density of States. In SID Symposium Digest of Technical Papers (Vol. 39, No. 1, pp. 1277-1280). Oxford, UK: Blackwell Publishing Ltd.
(22) Urban, G., Jachimowicz, A., Kohl, F., Kuttner, H., Olcaytug, F., Kamper, H., ... & Schönauer, M. (1990). High-resolution thin-film temperature sensor arrays for medical applications. Sensors and Actuators A: Physical, 22(1-3), 650-654.
(23) Macleod, H. A., & Macleod, H. A. (2010). Thin-film optical filters. CRC press.
(24) Fortunato, E., Barquinha, P., & Martins, R. (2012). Oxide semiconductor thin‐film transistors: a review of recent advances. Advanced materials, 24(22), 2945-2986.
(25) Marinca, V., Herişanu, N., & Nemeş, I. (2008). Optimal homotopy asymptotic method with application to thin film flow. Open Physics, 6(3), 648-653.
(26) Seshan, K. (Ed.). (2001). Handbook of thin film deposition processes and techniques. William Andrew.
(27) Abegunde, O. O., Akinlabi, E. T., Oladijo, O. P., Akinlabi, S., & Ude, A. U. (2019). Overview of thin film deposition techniques. AIMS Materials Science, 6(2), 174-199.
(28) Mattox, D. M. (2010). Handbook of physical vapor deposition (PVD) processing. William Andrew.
(29) Jimenez-Cadena, G., Comini, E., Ferroni, M., Vomiero, A., & Sberveglieri, G. (2010). Synthesis of different ZnO nanostructures by modified PVD process and potential use for dye-sensitized solar cells. Materials Chemistry and Physics, 124(1), 694-698.
(30) Nekarda, J., Reinwand, D., Grohe, A., Hartmann, P., Preu, R., Trassl, R., & Wieder, S. (2009, June). Industrial PVD metallization for high efficiency crystalline silicon solar cells. In 2009 34th IEEE Photovoltaic Specialists Conference (PVSC) (pp. 000892-000896). IEEE.
(31) Akkuş, Y., & Dursunkaya, Z. (2016). A new approach to thin film evaporation modeling. International Journal of Heat and Mass Transfer, 101, 742-748.
(32) Ziberi, B., Cornejo, M., Frost, F., & Rauschenbach, B. (2009). Highly ordered nanopatterns on Ge and Si surfaces by ion beam sputtering. Journal of Physics: Condensed Matter, 21(22), 224003.
(33) Wasa, K., Kitabatake, M., & Adachi, H. (2004). Thin film materials technology: sputtering of control compound materials. Springer Science & Business Media.
(34) Alfonso, E., Olaya, J., & Cubillos, G. (2012). Thin film growth through sputtering technique and its applications. Crystallization-Science and technology, 23, 11-12.
(35) Tong, Y., Xu, Z., Liu, C., Wang, J., & Wu, Z. G. (2014). Magnetic sputtered amorphous Si/C multilayer thin films as anode materials for lithium ion batteries. Journal of Power Sources, 247, 78-83.
(36) Delcorte, A., Bertrand, P., & Garrison, B. J. (2001). Collision cascade and sputtering process in a polymer. The Journal of Physical Chemistry B, 105(39), 9474-9486.
(37) Sandoval, L., & Urbassek, H. M. (2015). Collision-spike sputtering of au nanoparticles. Nanoscale research letters, 10(1), 1-8.
(38) Feix, M., Hartmann, A. K., Kree, R., Muñoz-García, J., & Cuerno, R. (2005). Influence of collision cascade statistics on pattern formation of ion-sputtered surfaces. Physical Review B, 71(12), 125407.
(39) Hamon, Y., Douard, A., Sabary, F., Marcel, C., Vinatier, P., Pecquenard, B., & Levasseur, A. (2006). Influence of sputtering conditions on ionic conductivity of LiPON thin films. Solid State Ionics, 177(3-4), 257-261.
(40) Nose, M., Deguchi, Y., Mae, T., Honbo, E., Nagae, T., & Nogi, K. (2003). Influence of sputtering conditions on the structure and properties of Ti–Si–N thin films prepared by rf-reactive sputtering. Surface and Coatings Technology, 174, 261-265.
(41) Tvarozek, V., Novotny, I., Sutta, P., Flickyngerova, S., Schtereva, K., & Vavrinsky, E. (2007). Influence of sputtering parameters on crystalline structure of ZnO thin films. Thin Solid Films, 515(24), 8756-8760.
(42) Matsuda, A. (2004). Thin-film silicon–growth process and solar cell application–. Japanese journal of applied physics, 43(12R), 7909.
(43) Jin, Z., Zhou, H. J., Jin, Z. L., Savinell, R. F., & Liu, C. C. (1998). Application of nano-crystalline porous tin oxide thin film for CO sensing. Sensors and Actuators B: Chemical, 52(1-2), 188-194.
(44) Trolier-McKinstry, S., & Muralt, P. (2004). Thin film piezoelectrics for MEMS. Journal of Electroceramics, 12(1), 7-17.
(45) Dubois, M. A., & Muralt, P. (1999). Properties of aluminum nitride thin films for piezoelectric transducers and microwave filter applications. Applied Physics Letters, 74(20), 3032-3034.
(46) Lueng, C. M., Chan, H. L., Surya, C., & Choy, C. L. (2000). Piezoelectric coefficient of aluminum nitride and gallium nitride. Journal of applied physics, 88(9), 5360-5363.
(47) Ruby, R. C., Bradley, P., Oshmyansky, Y., Chien, A., & Larson, J. D. (2001, October). Thin film bulk wave acoustic resonators (FBAR) for wireless applications. In 2001 IEEE Ultrasonics Symposium. Proceedings. An International Symposium (Cat. No. 01CH37263) (Vol. 1, pp. 813-821). IEEE.
(48) Wingqvist, G. (2010). AlN-based sputter-deposited shear mode thin film bulk acoustic resonator (FBAR) for biosensor applications—A review. Surface and Coatings Technology, 205(5), 1279-1286.
(49) Guy, I. L., Muensit, S., & Goldys, E. M. (1999). Extensional piezoelectric coefficients of gallium nitride and aluminum nitride. Applied Physics Letters, 75(26), 4133-4135.
(50) Kamohara, T., Akiyama, M., Ueno, N., Nonaka, K., & Tateyama, H. (2005). Growth of highly c-axis-oriented aluminum nitride thin films on molybdenum electrodes using aluminum nitride interlayers. Journal of crystal growth, 275(3-4), 383-388.
(51) Cibert, C., Chatras, M., Champeaux, C., Cros, D., & Catherinot, A. (2007). Pulsed laser deposition of aluminum nitride thin films for FBAR applications. Applied surface science, 253(19), 8151-8154.
(52) Yang, C. M., Uehara, K., Kim, S. K., Kameda, S., Nakase, H., & Tsubouchi, K. (2003, October). Highly c-axis-oriented AlN film using MOCVD for 5GHz-band FBAR filter. In IEEE Symposium on Ultrasonics, 2003 (Vol. 1, pp. 170-173). IEEE.
(53) Lehmann, C., & Sigmund, P. (1966). On the mechanism of sputtering. physica status solidi (b), 16(2), 507-511.
(54) Martínez, F. L., Toledano-Luque, M., Gandía, J. J., Cárabe, J., Bohne, W., Röhrich, J., ... & Mártil, I. (2007). Optical properties and structure of HfO2 thin films grown by high pressure reactive sputtering. Journal of Physics D: Applied Physics, 40(17), 5256.
(55) Seah, M. P., Clifford, C. A., Green, F. M., & Gilmore, I. S. (2005). An accurate semi‐empirical equation for sputtering yields I: for argon ions. Surface and Interface Analysis: An International Journal devoted to the development and application of techniques for the analysis of surfaces, interfaces and thin films, 37(5), 444-458.
(56) Berg, S., Blom, H. O., Larsson, T., & Nender, C. (1987). Modeling of reactive sputtering of compound materials. Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films, 5(2), 202-207.
(57) Fassel, V. A. (1978). Quantitative elemental analyses by plasma emission spectroscopy. Science, 202(4364), 183-191.
(58) Uhov, A. A., Gerasimov, V. A., Kostrin, D. K., & Selivanov, L. M. (2014, November). Use of compact spectrometer for plasma emission qualitative analysis. In Journal of Physics: Conference Series (Vol. 567, No. 1, p. 012039). IOP Publishing.
(59) Nölte, J. (2021). ICP Emission Spectrometry: a practical guide. John Wiley & Sons.
(60) Aragón, C., & Aguilera, J. A. (2008). Characterization of laser induced plasmas by optical emission spectroscopy: A review of experiments and methods. Spectrochimica Acta Part B: Atomic Spectroscopy, 63(9), 893-916.
(61) Ma, J., Ashfold, M. N., & Mankelevich, Y. A. (2009). Validating optical emission spectroscopy as a diagnostic of microwave activated CH 4/Ar/H 2 plasmas used for diamond chemical vapor deposition. Journal of Applied Physics, 105(4), 043302.
(62) Srivastava, N., Wang, C., & Dibble, T. S. (2009). A study of OH radicals in an atmospheric AC discharge plasma using near infrared diode laser cavity ringdown spectroscopy combined with optical emission spectroscopy. The European Physical Journal D, 54(1), 77-86.
(63) Aragón, C., & Aguilera, J. A. (2008). Characterization of laser induced plasmas by optical emission spectroscopy: A review of experiments and methods. Spectrochimica Acta Part B: Atomic Spectroscopy, 63(9), 893-916.
(64) Yang, I., Han, M. S., Yim, Y. H., Hwang, E., & Park, S. R. (2004). A strategy for establishing accurate quantitation standards of oligonucleotides: quantitation of phosphorus of DNA phosphodiester bonds using inductively coupled plasma–optical emission spectroscopy. Analytical biochemistry, 335(1), 150-161.
(65) Coulombier, M., Guisbiers, G., Colla, M. S., Vayrette, R., Raskin, J. P., & Pardoen, T. (2012). On-chip stress relaxation testing method for freestanding thin film materials. Review of Scientific Instruments, 83(10), 105004.
(66) Friesen, C., & Thompson, C. V. (2002). Reversible stress relaxation during precoalescence interruptions of Volmer-Weber thin film growth. Physical review letters, 89(12), 126103.
(67) Ma, C. H., Huang, J. H., & Chen, H. (2002). Residual stress measurement in textured thin film by grazing-incidence X-ray diffraction. Thin solid films, 418(2), 73-78.
(68) Breiland, W. G., Lee, S. R., & Koleske, D. D. (2004). Effect of diffraction and film-thickness gradients on wafer-curvature measurements of thin-film stress. Journal of applied physics, 95(7), 3453-3465.
(69) Qin, M., Ji, V., Wu, Y. N., Chen, C. R., & Li, J. B. (2005). Determination of proof stress and strain-hardening exponent for thin film with biaxial residual stresses by in-situ XRD stress analysis combined with tensile test. Surface and Coatings Technology, 192(2-3), 139-144.
(70) Luo, Q., & Jones, A. H. (2010). High-precision determination of residual stress of polycrystalline coatings using optimised XRD-sin2ψ technique. Surface and Coatings Technology, 205(5), 1403-1408.
(71) Marwala, T. (2018). Handbook Of Machine Learning-Volume 1: Foundation Of Artificial Intelligence. World Scientific.
(72) Wang, H., Ma, C., & Zhou, L. (2009). A brief review of machine learning and its application. In 2009 international conference on information engineering and computer science (pp. 1-4). IEEE.
(73) Saravanan, R., & Sujatha, P. (2018, June). A state of art techniques on machine learning algorithms: a perspective of supervised learning approaches in data classification. In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 945-949). IEEE.
(74) Ghahramani, Z. (2003, February). Unsupervised learning. In Summer School on Machine Learning (pp. 72-112). Springer, Berlin, Heidelberg.
(75) Hastie, T., Tibshirani, R., & Friedman, J. (2009). Unsupervised learning. In The elements of statistical learning (pp. 485-585). Springer, New York, NY.
(76) Orhan, U., Hekim, M., & Ozer, M. (2011). EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Systems with Applications, 38(10), 13475-13481.
(77) Noriega, L. (2005). Multilayer perceptron tutorial. School of Computing. Staffordshire University.
(78) Ramchoun, H., Idrissi, M. A. J., Ghanou, Y., & Ettaouil, M. (2016). Multilayer Perceptron: Architecture Optimization and Training. IJIMAI, 4(1), 26-30.
(79) Lavania, S., Kumam, B., Matey, P. S., Annepu, V., & Bagadi, K. (2015, March). Equalization of Stanford University Interim channels using adaptive multilayer perceptron NN model. In 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (pp. 1-6). IEEE.
(80) Kainen, P., Kůrková, V., & Vogt, A. (2000). Best approximation by Heaviside perceptron networks. Neural Networks, 13(7), 695-697.
(81) Stathakis, D. (2009). How many hidden layers and nodes?. International Journal of Remote Sensing, 30(8), 2133-2147.
(82) Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2(4), 433-459.
(83) Bro, R., & Smilde, A. K. (2014). Principal component analysis. Analytical methods, 6(9), 2812-2831.
(84) Ringnér, M. (2008). What is principal component analysis?. Nature biotechnology, 26(3), 303-304.
(85) Pansu, M., & Gautheyrou, J. (2007). Handbook of soil analysis: mineralogical, organic and inorganic methods. Springer Science & Business Media.
(86) Yang, Q., & Zhao, L. R. (2008). Characterization of nano-layered multilayer coatings using modified Bragg law. Materials characterization, 59(9), 1285-1291.
(87) Li, X., Shih, W. Y., Vartuli, J. S., Milius, D. L., Aksay, I. A., & Shih, W. H. (2002). Effect of a Transverse Tensile Stress on the Electric‐Field‐Induced Domain Reorientation in Soft PZT: In Situ XRD Study. Journal of the American Ceramic Society, 85(4), 844-850.
(88) Qin, M., Ji, V., Wu, Y. N., Chen, C. R., & Li, J. B. (2005). Determination of proof stress and strain-hardening exponent for thin film with biaxial residual stresses by in-situ XRD stress analysis combined with tensile test. Surface and Coatings Technology, 192(2-3), 139-144.
(89) Ma, C. H., Huang, J. H., & Chen, H. (2002). Residual stress measurement in textured thin film by grazing-incidence X-ray diffraction. Thin solid films, 418(2), 73-78.
(90) Pandey, A., Dutta, S., Prakash, R., Dalal, S., Raman, R., Kapoor, A. K., & Kaur, D. (2016). Growth and evolution of residual stress of AlN films on silicon (100) wafer. Materials Science in Semiconductor Processing, 52, 16-23.
(91) Yarar, E., Hrkac, V., Zamponi, C., Piorra, A., Kienle, L., & Quandt, E. (2016). Low temperature aluminum nitride thin films for sensory applications. AIP Advances, 6(7), 075115.
(92) Hossler, D., & Bontrager, B. (2014). Handbook of strategic enrollment management. John Wiley & Sons.
(93) Hoyle, R. H. (Ed.). (2012). Handbook of structural equation modeling. Guilford press.
(94) Echlin, P. (2011). Handbook of sample preparation for scanning electron microscopy and X-ray microanalysis. Springer Science & Business Media.
(95) Ayache, J., Beaunier, L., Boumendil, J., Ehret, G., & Laub, D. (2010). Sample preparation handbook for transmission electron microscopy: techniques (Vol. 2). Springer Science & Business Media.
(96) Keyse, R. J., Garratt-Reed, A. J., Goodhew, P. J., & Lorimer, G. W. (2018). Introduction to scanning transmission electron microscopy. Routledge.
(97) Inkson, B. J. (2016). Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) for materials characterization. In Materials characterization using nondestructive evaluation (NDE) methods (pp. 17-43). Woodhead Publishing.
(98) Boden, M. (2002). A guide to recurrent neural networks and backpropagation. the Dallas project.
(99) Ganin, Y., & Lempitsky, V. (2015, June). Unsupervised domain adaptation by backpropagation. In International conference on machine learning (pp. 1180-1189). PMLR.
(100) Murad, D. F., Heryadi, Y., Wijanarko, B. D., Isa, S. M., & Budiharto, W. (2018, September). Recommendation system for smart LMS using machine learning: a literature review. In 2018 International Conference on Computing, Engineering, and Design (ICCED) (pp. 113-118). IEEE.
(101) Abadias, G., Chason, E., Keckes, J., Sebastiani, M., Thompson, G. B., Barthel, E., ... & Martinu, L. (2018). Stress in thin films and coatings: Current status, challenges, and prospects. Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films, 36(2), 020801.
(102) Gupta, N., Pandey, A., Vanjari, S. R. K., & Dutta, S. (2019). Influence of residual stress on performance of AlN thin film based piezoelectric MEMS accelerometer structure. Microsystem Technologies, 25(10), 3959-3967.
(103) Lu, Y., Reusch, M., Kurz, N., Ding, A., Christoph, T., Kirste, L., ... & Žukauskaitė, A. (2018). Surface morphology and microstructure of pulsed DC magnetron sputtered piezoelectric AlN and AlScN thin films. physica status solidi (a), 215(9), 1700559.
(104) Iqbal, A., & Mohd-Yasin, F. (2018). Reactive sputtering of aluminum nitride (002) thin films for piezoelectric applications: A review. Sensors, 18(6), 1797.
(105) Natta, L., Mastronardi, V. M., Guido, F., Algieri, L., Puce, S., Pisano, F., ... & De Vittorio, M. (2019). Soft and flexible piezoelectric smart patch for vascular graft monitoring based on aluminum nitride thin film. Scientific reports, 9(1), 1-10.
(106) Anggraini, S. A., Uehara, M., Yamada, H., & Akiyama, M. (2019). Mg and Ti codoping effect on the piezoelectric response of aluminum nitride thin films. Scripta Materialia, 159, 9-12.
(107) Anggraini, S. A., Uehara, M., Yamada, H., & Akiyama, M. (2019). Mg and Ti codoping effect on the piezoelectric response of aluminum nitride thin films. Scripta Materialia, 159, 9-12.
(108) Kuwano, N., Kaur, J., & Rahmah, S. (2019). Electron microscopy determination of crystallographic polarity of aluminum nitride thin films. Micron, 116, 80-83.
(109) Huang, M. S., Yu, S. Y., Chiang, P. C., Huang, B. H., Saito, T., Huang, C. C., ... & Lan, W. C. (2020). Effect of mechanobiology of cell response on titanium with multilayered aluminum nitride/tantalum thin film. Applied Sciences, 10(2), 645.
(110) Ju, H., Jia, P., Xu, J., Yu, L., Asempah, I., & Geng, Y. (2018). Crystal structure and high temperature tribological behavior of niobium aluminum nitride films. Materialia, 3, 202-211.
(111) Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.
(112) Galántai, A. (2000). The theory of Newton′s method. Journal of Computational and Applied Mathematics, 124(1-2), 25-44.
(113) Saad, Y., Yeung, M., Erhel, J., & Guyomarc′h, F. (2000). A deflated version of the conjugate gradient algorithm. SIAM Journal on Scientific Computing, 21(5), 1909-1926.
(114) Wang, W., & Lu, Y. (2018, March). Analysis of the mean absolute error (MAE) and the root mean square error (RMSE) in assessing rounding model. In IOP conference series: materials science and engineering (Vol. 324, No. 1, p. 012049). IOP Publishing.
(115) Patil, M. S., Singh, P., Kalpande, S. K., & Mohod, C. (2020, February). Predicting Clear Sky Index for Performance Assessment of Roof Top on Grid PV Plant. In 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE) (pp. 186-190). IEEE.
(116) Bagwan, W. A., & Gavali, R. S. (2020). Delineating changes in soil erosion risk zones using RUSLE model based on confusion matrix for the Urmodi river watershed, Maharashtra, India. Modeling Earth Systems and Environment, 1-14.
指導教授 利定東(Tomi T. Li) 審核日期 2021-7-7
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