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姓名	傅筱婷(Hsiao-Ting Fu)  查詢紙本館藏	畢業系所	光電科學與工程學系
論文名稱	進階簡型演算法應用於優化光學薄膜設計 (Optimizing the optical thin film design by modified simplex algorithm)
相關論文	★ 以反應性射頻磁控濺鍍搭配HMDSO電漿聚合鍍製氧化矽摻碳薄膜阻障層之研究 ★ 軟性電子阻水氣膜之有機層組成研究 ★ 利用介電質-金屬對稱膜堆設計雙曲超穎材料並分析其光學特性 ★ 石墨烯透明導電膜與其成長模型之研究 ★ 以磁控電漿輔助化學氣相沉積法製鍍有機矽阻障層之研究 ★ 以電漿聚合鍍製氧化矽摻碳氫薄膜應力之研究 ★ 利用有限元素方法分析光譜合束器之多層介電質繞射光柵之繞射效率 ★ 化學氣相沉積石墨烯透明導電膜之製程與分析 ★ 應用光學導納軌跡法提升太陽能選擇性吸收膜之光熱轉換效率研究 ★ 單晶銅成長石墨烯及其可撓性之研究 ★ 高反射多層膜抗雷射損傷閥值之研究 ★ 高穿透類鑽碳膜之研究 ★ 裝備具有低光斑的抗眩光膜層 ★ 透鏡品質檢測基於四波橫向剪切干涉儀 ★ 利用介電係數趨近零材料設計層狀寬帶超穎吸收膜 ★ 抑制層對降低電漿輔助原子層沉積二氧化鉿薄膜結晶之研究
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摘要(中)	這些年光電科技迅速發展，使得高品質的光學元件需求越來越高，而元件上的光學薄膜為了達到這些目標，隨著電腦科學日漸普及，能處理大量數學演算法也變得很重要。一個優異的模擬軟體可以在鍍膜前對於想要的目標值進行優化，也能夠在鍍膜後針對失敗的膜層分析其實際值與理想值的差異。 本實驗藉著簡型演算法來對光學薄膜進行優化，由簡型演算法文獻中可得知收斂至最佳解的辦法主要由反射、收縮與擴張這三個機制來主導，其如何移動頂點進行收斂影響著找尋最佳解的能力，因此針對這些機制的移動方法使用變動參數進行改善，修改後的簡型優化成功率提高10%，且優化結果與目標值的標準差最低可至0.08。當實際鍍膜的結果與模擬結果的光譜不同時，可以利用反向工程來找到原因，本實驗中反向工程的最佳結果能達到回推結果的光譜與實際鍍膜的光譜相關係數為1。若是初始值和目標值差異較大時，利用智能加層優化方法，此方法為新概念的加層辦法，除了能夠有效地優化光譜，達到優化結果與目標值的標準差低至0.23，還能避免薄膜厚度出現太薄的情況(小於5 nm)，優化出的膜層結構無不合理的膜層或厚度，可智能化的選擇最佳結構優化並達到使用者要求的目標值。
摘要(英)	In recent years, the rapid development of optoelectronic technology has made the demand for high-quality optical components higher and higher. In order to achieve these goals, optical films on components have become more and more important. Therefore, an excellent simulation software can not only optimize for the desired target value before coating, but also analyze the difference between the actual value and the ideal value for the failed film after coating. In this research, simplex algorithm is selected to optimize optical thin films. It is well known that the simplex algorithm mainly converges to the optimal solution based on the three mechanisms: reflection, contraction and expansion. Therefore, the movement of vertices has a great effect on finding an optimizing solution. In this thesis, using the adaptive parameters which is the most effective way to improve it. The rate of successful optimization can enhance 10% by modified simplex algorithm (MSA). Besides, the optimized value can be situated in 0.08 deviation of the target. When the spectrum of real coating is different from simulation, reverse engineering (RE) can effectively estimate the film thickness difference between these two. A simple spectral fitting can achieve the coefficient of determination, denoted R2 or r2 and pronounced R squared (RSQ) equal to 1. Intelligent layering simplex algorithm (ILSA) can be used in the problems that the difference between the initial spectrum and the target is large. Its layering method is a new concept that has higher accuracy of optimization in the desired spectrum. Most important of all, this way can avoid unreasonable thickness that thinner than 5nm and the optimized value can be situated in 0.23 deviation of the target. ILSA is a more realistic algorithm within thin film coating regime.
關鍵字(中)	★ 光學薄膜 ★ 優化模擬 ★ 反向工程 ★ 簡型演算法	關鍵字(英)	★ optical thin film ★ optimization ★ reverse engineering ★ simplex algorithm
論文目次	摘要 i Abstract ii 致謝 iv Table of Contents v Chapter 1：Introduction 1 1-1 Previous remarks 1 1-2 Motivation 2 1-3 Structure of thesis 3 Chapter 2：Theory and Literature Review 5 2-1 Basic theory of optical thin film 5 2-1-1 Electromagnetic wave 5 2-1-2 Reflection and transmission of single interface 7 2-1-3 Characteristics of single layer film and multilayer film matrix 11 2-2 Introduction of algorithm 14 2-2-1 Common algorithm 15 2-2-2 Nelder-Mead Simplex 21 Chapter 3：Program Architecture 26 3-1 Program architecture 26 3-2 Modified simplex algorithm(MSA) 27 3-2-1 Introduction of MSA 27 3-2-2 Program flow of MSA 33 3-3 Intelligent layering simplex algorithm (ILSA) 37 3-3-1 Introduction of ILSA 37 3-3-2 Program flow of ILSA 44 3-4 Reverse engineering 45 3-4-1 Introduction of reverse engineering 45 3-4-2 Program flow of reverse engineering 46 Chapter 4：Results and Discussion 48 4-1 Modified simplex algorithm(MSA) 48 4-1-1 Long-wave-pass filter 48 4-1-2 Anti-reflection thin films (AR) 53 4-2 Reverse engineering (RE) 56 4-3 Intelligent layering simplex algorithm (ILSA) 65 4-3-1 Beam splitter 65 4-3-2 Short-wave-pass filter 68 Chapter 5. Conclusion 74 Reference 77
參考文獻	[1] 李正中，薄膜光學與鍍膜技術，第八版，藝軒圖書出版社，新北市，2016年6月 [2] Nelder J. A., Mead R., “A Simplex Method for Function Minimization”, The Computer Journal, Vol 7(4), pp. 308–313, 1965 [3] Lenstra J. K., Kan A. H. G. R., “Some Simple Applications of the Travelling Salesman Problem”, Operational Research Quarterly, Vol 26(4), pp. 717, 1975 [4] Zhou Y., Zhou Y., Luo Q., Abdel-Basset M., “A simplex method-based social spider optimization algorithm for clustering analysis”, Engineering Applications of Artificial Intelligence, Vol 64, pp. 67–82, 2017 [5] Dorigo M., Gambardella L. M., “Ant colonies for the travelling salesman problem”, Biosystems, Vol 43(2), pp. 73–81, 1997 [6] Mirjalili S., Mirjalili S. M., Hatamlou A., “Multi-Verse Optimizer: a nature-inspired algorithm for global optimization”, Neural Computing and Applications, Vol 27(2), pp. 495–513, 2015 [7] Rajan A., Malakar T., “Optimal reactive power dispatch using hybrid Nelder–Mead simplex based firefly algorithm”, International Journal of Electrical Power & Energy Systems, Vol 66, pp. 9–24, 2015 [8] Mandic D. P., “A Generalized Normalized Gradient Descent Algorithm”, IEEE Signal Processing Letters, Vol 11(2), pp. 115–118, 2004 [9] GILL P. E., MURRAY W., “Quasi-Newton Methods for Unconstrained Optimization”, IMA Journal of Applied Mathematics, Vol 9(1), pp. 91–108, 1972 [10] Berahas A. S., Byrd R. H., Nocedal J., “Derivative-Free Optimization of Noisy Functions via Quasi-Newton Methods”, SIAM Journal on Optimization, Vol 29(2), pp. 965–993, 2019 [11] Powell M. J. D., “Restart procedures for the conjugate gradient method”, Mathematical Programming, Vol 12(1), pp. 241–254, 1977 [12] Exl L., Fischbacher J., Kovacs A., Oezelt H., Gusenbauer M., Schrefl T., “Preconditioned nonlinear conjugate gradient method for micromagnetic energy minimization”, Computer Physics Communications, Vol 235, pp. 179-186, 2018 [13] Kirkpatrick S., Gelatt C. D., Vecchi M. P., “Optimization by Simulated Annealing”, Science, Vol 220(4598), pp. 671–680, 1983 [14] Wei L., Zhang Z., Zhang D., Leung S. C. H., “A simulated annealing algorithm for the capacitated vehicle routing problem with two-dimensional loading constraints”, European Journal of Operational Research, Vol 265(3), pp. 843–859, 2018 [15] Yang T., Asanjan A. A., Faridzad M., Hayatbini N., Gao X., Sorooshian S., “An enhanced artificial neural network with a shuffled complex evolutionary global optimization with principal component analysis”, Information Sciences, Vol 418-419, pp. 302–316, 2017 [16] Üstün B., Melssen W. J., Oudenhuijzen M., Buydens L. M. C., “Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization”, Analytica Chimica Acta, Vol 544(1-2), pp. 292–305, 2005 [17] Han M., Wang J., Wang Z., Sui Q., Jia L., Li S., Bo D., “Experimental research of microseismic source localization based on improved simplex optimization algorithm”, Chinese Automation Congress (CAC), 2017 [18] Karimi A., Siarry P., “Global Simplex Optimization—A simple and efficient metaheuristic for continuous optimization. Engineering Applications of Artificial Intelligence”, Vol 25(1), pp. 48–55, 2012 [19] Han W., Van Dang C., Kim J.-W., Kim Y.-J., Jung S.-Y., “Global-Simplex Optimization Algorithm Applied to FEM-Based Optimal Design of Electric Machine”, IEEE Transactions on Magnetics, Vol 53(6), pp. 1–4, 2018 [20] Luersen M. A., Le Riche R., “Globalized Nelder–Mead method for engineering optimization”, Computers & Structures, Vol 82(23-26), pp. 2251–2260, 2004 [21] Sadatsakkak S. A., Ahmadi M. H., Bayat R., Pourkiaei S. M., Feidt M., “Optimization density power and thermal efficiency of an endoreversible Braysson cycle by using non-dominated sorting genetic algorithm”, Energy Conversion and Management, Vol 93, pp. 31–39, 2015 [22] Bezerra M. A., dos Santos Q. O., Santos A. G., Novaes C. G., Ferreira S. L. C., de Souza V. S., “Simplex optimization: A tutorial approach and recent applications in analytical chemistry”, Microchemical Journal, Vol 124, pp. 45–54, 2016 [23] Koshel R. J., “Simplex optimization method for illumination design”, Optics Letters, Vol 30(6), pp. 649, 2005 [24] Murillo Pulgarín J. A., Alañón Molina A., Jiménez García E., “Simplex optimization of the variables influencing the determination of pefloxacin by time-resolved chemiluminescence”, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Vol 193, pp. 117–124, 2018 [25] Luo C., Zhang S.-L., Yu B., “Some modifications of low-dimensional simplex evolution and their convergence”, Optimization Methods and Software, Vol 28(1), pp. 54–81, 2013 [26] 林育賢，動態干涉儀應用於量測薄膜之光學常數，國立中央大學，碩士論文，民國一百零五年 [27] Nicolas D., Jean-Mare A., “A Combined Nelder-Mead Simplex and Genetic Algorithm” [28] H. Rahami1, A. Kaveh, M. Aslani1, R. Najian Asl1, “A HYBRID MODIFIED GENETIC-NELDER MEAD SIMPLEX ALGORITHM FOR LARGE-SCALE TRUSS OPTIMIZATION”, Int. J. Optim. Civil Eng, Vol 1, pp. 29-46, 2011 [29] Guessasma S., Zhang W., Zhu J., “Local mechanical behavior mapping of a biopolymer blend using nanoindentation, finite element computation, and simplex optimization strategy”, Journal of Applied Polymer Science, Vol 134(24), 2017 [30] Qiu M., Ming Z., Li J., Gai K., Zong Z., “Phase-Change Memory Optimization for Green Cloud with Genetic Algorithm”, IEEE Transactions on Computers, Vol 64(12), pp. 3528–3540, 2015 [31] Hamid N. F. A., Rahim N. A., Selvaraj J., “Solar cell parameters identification using hybrid Nelder-Mead and modified particle swarm optimization”, Journal of Renewable and Sustainable Energy, Vol 8(1), pp. 015502, 2016 [32] Tsoukalas I., Kossieris P., Efstratiadis A., Makropoulos C., “Surrogate-enhanced evolutionary annealing simplex algorithm for effective and efficient optimization of water resources problems on a budget”, Environmental Modelling & Software, Vol 77, pp. 122–142, 2016 [33] Fan S.-K. S., Zahara, E., “ A hybrid simplex search and particle swarm optimization for unconstrained optimization”, European Journal of Operational Research, Vol 181(2), pp. 527–548, 2007 [34] Farshi B., Gheshmi S., Miandoabchi E., “Optimization of injection molding process parameters using sequential simplex algorithm”, Materials & Design, Vol 32(1), pp. 414–423, 2011 [35] Gao F., Han L., “Implementing the Nelder-Mead simplex algorithm with adaptive parameters”, Computational Optimization and Applications, Vol 51(1), pp. 259–277, 2010 [36] Kočí V., Kočí J., Čáchová M., Vejmelková E., Černý R., “Multi-parameter optimization of lime composite design using a modified downhill simplex method”, Composites Part B: Engineering, Vol 93, pp. 184–189, 2016 [37] Klein K., Neira J., “Nelder-Mead Simplex Optimization Routine for Large-Scale Problems: A Distributed Memory Implementation”, Computational Economics, Vol 43(4), pp. 447–461, 2013 [38] Wang P. C., Shoup T. E., “Parameter sensitivity study of the Nelder–Mead Simplex Method”, Advances in Engineering Software, Vol 42(7), pp. 529–533, 2011 [39] Chen Z., Wu L., Lin P., Wu Y., Cheng, S., “Parameters identification of photovoltaic models using hybrid adaptive Nelder-Mead simplex algorithm based on eagle strategy”, Applied Energy, Vol 182, pp. 47–57, 2016 [40] Xu S., Wang Y., Wang Z., “Parameter estimation of proton exchange membrane fuel cells using Eagle strategy based on JAYA algorithm and Nelder-Mead simplex method”, Energy, 2019 [41] Omran M. G. H., Clerc M., “The Adaptive Population-based Simplex method”, International Conference on Digital Information Processing and Communications (ICDIPC), 2015
指導教授	郭倩丞	審核日期	2019-7-18
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