使用白光干涉儀以基因演算法從反射光譜振幅計算光學常數

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：38

、訪客IP：18.118.12.50

姓名

蕭義勝(Yi-sheng Siao) 查詢紙本館藏

畢業系所

光電科學與工程學系

論文名稱

使用白光干涉儀以基因演算法從反射光譜振幅計算光學常數
(Calculation of optical constants from reflected spectral amplitude with Genetic Algorithm by white light interferometer)

相關論文

★ 半導體雷射控制頻率	★ 比較全反射受挫法與反射式干涉光譜法在生物感測上之應用
★ 193nm深紫外光學薄膜之研究	★ 超晶格結構之硬膜研究
★ 交錯傾斜微結構薄膜在深紫外光區之研究	★ 膜堆光學導納量測儀
★ 紅外光學薄膜之研究	★ 成對表面電漿波生物感知器應用在去氧核糖核酸及微型核糖核酸雜交反應檢測
★ 成對表面電漿波生物感測器之研究及其在生醫上的應用	★ 探討硫化鎘緩衝層之離子擴散處理對CIGS薄膜元件效率影響
★ 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	★ 掃描式白光干涉儀應用在量測薄膜之光學常數
★ 量子點窄帶濾光片	★ 以量測反射係術探測光學薄膜之特性
★ 嵌入式繼光鏡顯微超頻譜影像系統應用在口腔癌切片及活體之設計及研究	★ 軟性電子阻水氣膜之有機層組成研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

基因演算法應用在數值分析上一直有傑出的表現，本論文將基因演算
法應用於反推薄膜光學常數，在單層膜且沒有吸收時，可以很精確的反推
出薄膜光學常數。
本論文用五種樣本測量光譜並使用基因演算法擬合光學常數，並將多
次擬合後的光學常數取平均值，將計算出的光譜與測量光譜比較，使用的
材料有Ta2O5、Nb2O5、以及SiO2，其中Ta2O5 與Nb2O5 折射率與厚度較大，
因此在基因演算法中所設定的上下限可以比較廣，薄膜折射率範圍可以在1
到3而薄膜厚度範圍可以在1到1000nm，而SiO2樣本折射率與厚度均較小，
造成光譜沒有轉折點較難擬合，因此將薄膜折射率範圍設定在1.4 到1.5 時
才能得出正確的薄膜厚度。
本論文中的Ta2O5 與Nb2O5 材料，折射率精確度可以優於2%，膜厚精
確度可優於1%，但總體而言擬合的折射率與厚度誤差均小於5%，而SiO2
材料經修正範圍後，擬合出的折射率誤差更可以小於1%，厚度誤差小於
3%。
同一片薄膜樣本，其表面厚度也不一定會各點完全相同，橢圓偏振儀
只能測量出薄膜上某一點的薄膜光學常數，本論文使用白光干涉儀的快速
測量光譜搭配基因演算法的精確擬合，可以將二維的薄膜各點厚度求出，
將來可以應用在工業上的快速測量與大面積的測量，減少測量時間成本。

摘要(英)

The genetic algorithm (GA) is excellent at solving global numerical
optimization problems. We propose GA to acquire optical constant of thin films
from spectral amplitude retrieved using Fourier transform of the Interferogram.
The optical parameters such as refractive index and thickness of thin films
are essential for comparison of samples which are produced using different
methods. These optical parameters are usually determined by photometric
methods or Ellipsometer. The optical thickness, the product of refractive index
and thickness determine the spectrum of the thin films. As optical constant in
multiple of quarter wave we can find spectrum peaks. Under the condition when
the single layer sample has larger optical thickness, we can acquire the optical
parameters accurately through GA.
In this study, we select five samples which are different materials and
thickness and use GA to fit the spectrum to acquire the optical parameters of
each sample. We use GA to fit the spectrum 1000 times and use the average
optical parameters to calculate the spectrum comparing with the measured
spectrum. We use three material Ta2O5, Nb2O5 and SiO2 as the samples. The
refractive index of Ta2O5 and Nb2O5 are higher than SiO2, so it is easy for GA to
acquire the optical constants under a wide range of the optical parameters. For
Ta2O5 and Nb2O5, we set the range of refractive index between one to three and
the thickness between 1 nm to 1000 nm. For SiO2, we change the range of
refractive index between 1.4 to 1.5.
The minimum errors of the calculated refractive index of Ta2O5 and Nb2O5
are smaller than 2% and the thickness are smaller than 1%. The total errors of
the optical parameters are smaller than 5%. The errors of the calculated
refractive index of SiO2 are smaller than 1% and the thickness are smaller than
3%.
The same thin film sample would have different optical parameters on its
two-dimensional surface. The Ellipsometer only measure one point on the thin
film at a time and it cost at least thirty minute in a process. In this study, we use
white-light interferometer with GA to measure the two-dimensional optical
parameters on the thin film surface in a short time.

關鍵字(中)

★ 白光干涉儀
★ 薄膜光學常數
★ 基因演算法

關鍵字(英)

★ Genetic Algorithm
★ optical costant
★ optical properties
★ white light interferometer

論文目次

目錄
摘要 ................................................................................................................. i
Abstract ................................................................................................................ ii
誌謝 ............................................................................................................... iii
目錄 ............................................................................................................... iv
圖目錄 .............................................................................................................. vii
表目錄 ............................................................................................................... ix
第一章緒論 ......................................................................................................... 1
1-1 前言 ......................................................................................................... 1
1-2 研究動機與目的 ..................................................................................... 1
1-3 基因演算法的應用與簡介 ..................................................................... 2
1-4 論文研究方向 ......................................................................................... 4
第二章基礎理論 ................................................................................................. 6
2-1 基因演算法理論 ..................................................................................... 6
2-1-1 初始化(Initialization) ....................................................................... 6
2-1-2 決定適應函數(Fitness Function) ..................................................... 7
2-1-3 編碼(Encoding) ................................................................................ 7
2-1-4 選擇(Selection) ................................................................................ 7
2-1-5 交配(Crossover) ............................................................................... 8
2-1-6 突變(Mutation) ............................................................................... 10
2-1-7 停止條件(Terminal Condition) ...................................................... 10
2-2 薄膜矩陣之理論 ................................................................................... 11
第三章擬合程式與測量儀器 ........................................................................... 13
3-1 程式設計 ............................................................................................... 13
3-2 模擬流程與步驟 ................................................................................... 14
3-2-1 初始化(Initialization) ..................................................................... 14
3-2-2 決定適應函數(Fitness Function) ................................................... 14
3-2-3 編碼(Encoding) .............................................................................. 15
3-2-4 選擇(Selection) .............................................................................. 15
3-2-5 交配(Crossover) ............................................................................. 15
3-2-6 突變(Mutation) ............................................................................... 16
3-2-7 停止條件(Terminal Condition) ...................................................... 16
3-3 程式之參數設定 ................................................................................... 16
3-4 量測與分析儀器 ................................................................................... 17
3-4-1 白光干涉儀(White-Light Interferometer) ..................................... 17
3-4-2 可見光近紅外光光譜儀 ................................................................ 18
3-4-3 橢圓偏振儀(Ellipsometery) ........................................................... 18
第四章實驗與誤差分析 ................................................................................... 20
4-1 橢圓偏振儀量測結果： ....................................................................... 20
4-2 基因演算法擬合光譜： ....................................................................... 21
4-3 基因演算法擬合白光干涉儀光譜： .................................................. 22
4-4 計算二維薄膜厚度： ........................................................................... 33
第五章結論 ....................................................................................................... 38
參考文獻 ..................................................................................................... 40

參考文獻

[1] 李正中, 薄膜光學與鍍膜技術, 第6 版. 台北市: 藝軒圖書文具有限公司,
2009.
[2] R. T. Phillips, "A numerical method for determining the complex refractive
index from reflectance and transmittance of supported thin films," J. Phys. D:
Appl. Phys, vol. 16, pp. 489-497, 1983.
[3] R. E. Denton, et al., "The determination of the optical constants of thin films
from measurements of reflectance and transmittance at normal incidence,"
Journal of Physics D: Applied Physics, vol. 5, pp. 852-863, 1972.
[4] J. C. Manifacier, et al., "A simple method for determination of the optical
constant n , k and the thickness of weekly absorbing thin films," J. Phy. E.
Sci., vol. 9, pp. 1002-1004, 1976.
[5] 何聰慧, "光學薄膜常數之計算," Master Thesis, IOS, NCU, Taiwan, 2002.
[6] 蕭家鼎, "以模擬退火法推算薄膜之光學常數," Master Thesis, IOS, NCU,
Taiwan, 2005.
[7] S. Kirkpatrick, et al., "Optimization by simulated annealing," Science, vol.
220, pp. 671-680, 1983.
[8] J. H. Holland, Adaptation in Natural and Artificial Systems: The University
of Michigan Press, Ann Arbor, 1975.
[9] L. Davis, Genetic algorithms and simulated annealing, 1987.
[10] D. D. Spinellis and C. T. Papadopoulos, "Genetic Algorithms Versus
Simulated Annealing," 2008.
[11] D. E. Goldberg and J. H. Holland, "Genetic algorithms and machine
learning," 1988.
[12] D. E. Goldberg, Genetic algorithms in search, optimization and machine
learning.: Addison Wesley, 1989.
[13] H. Mühlenbein, "Parallel genetic algorithms, population genetics and
combinatorial optimization," presented at the Proceedings of the third
international conference on Genetic algorithms, George Mason University,
United States, 1989.
41
[14] J. J. Grefenstette, "Genetic algorithms and machine learning," presented at
the Proceedings of the sixth annual conference on Computational learning
theory, Santa Cruz, California, United States, 1993.
[15] D. E. Goldberg, "Genetic and evolutionary algorithms come of age,"
Communications of the ACM, vol. 37, pp. 113-119, 1994.
[16] M. Srinivas and L. M. Patnaik, "Adaptive probabilities of crossover and
mutation in genetic algorithms," 1994.
[17] A. Laurence, "Adapting operator probabilities in genetic algorithms," Tuson,
1995.
[18] I. Chang-Hwan, et al., "Hybrid genetic algorithm for electromagnetic
topology optimization," Magnetics, IEEE Transactions on, vol. 39, pp.
2163-2169, 2003.
[19] K. Deb, et al., "A fast and elitist multi objective genetic algorithm NSGA-II,"
IEEE transactions on evolutionary computation, vol. 5, 2002.
[20] S. Guindon, et al., "A Simple, Fast, and Accurate Algorithm to Estimate
Large Phylogenies by Maximum Likelihood," Systematic Biology, vol. 52, pp.
696-704, 2003.
[21] S. Phillips, et al., "Maximum entropy modeling of species geographic
distributions," Ecological Modelling, vol. 190, pp. 231-259, 2006.
[22] S. Kalogirou, "Optimization of solar systems using artificial neural-networks
and genetic algorithms," Applied Energy, vol. 77, pp. 383-405, 2004.
[23] J. M. Cabello, et al., "Optimization of the size of a solar thermal electricity
plant by means of genetic algorithms," Renewable Energy, vol. 36, pp.
3146-3153, 2011.
[24] 盧書豪, "菁英微調突變式基因演算法於多極值函數之最佳化," Master
Thesis, Mechanical Engineering, NTUST, Taiwan, 2005.
[25] 陳奕仁, "適應性基因演算法結合菁英政策於線性馬達定位機台之主動式
振動控制器設計," Mechanical Engineering, NSYSU, Taiwan, 2001.
[26] A. Chipperfield, et al. (2011). Evolutionary Computation Research
Team:Genetic Algorithm Toolbox. Available:
http://www.shef.ac.uk/acse/research/ecrg/gat.html

指導教授

李正中(Cheng-Chung Lee)

審核日期

2011-7-20

推文