類神經網路於光柵設計之應用

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姓名

尤鈺臻(Yu-Jhen You) 查詢紙本館藏

畢業系所

光電科學與工程學系

論文名稱

類神經網路於光柵設計之應用
(Grating Profile Generation using Artificial Neural Networks)

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摘要(中)

類神經網路近年來在醫學、工程及金融方面都被廣泛地應用，此方法可經由給予適當的訓練資料及神經網路架構的設計，使類神經網路執行單一特定任務。本研究使用類神經網路進行光柵設計，以目標的繞射效率為輸入，相對應的光柵結構為輸出，進行類神經網路訓練。使用類神經網路方法除了可以避免一般在光柵設計中需解決馬克斯威爾方程式的反向問題，更可以達到即時的光柵設計，光柵計算時間大約0.2秒，可應用於快速光柵設計。研究中使用最簡單的類神經網路架構，即可達到光柵設計的功能，其繞射效率和理論值相近，更可以驗證類神經網路應用於光柵設計的可行性。研究中亦嘗試使用類神經網路進行超穎介面全像片之設計，使用三道不同方向之光切換重建出三種不同圖案，期望可以降低在設計時所需的計算量。

摘要(英)

Neural networks have been successfully applied in many applications. With appropriate training data and fine design of the artificial neural networks structure, the neural networks can be trained to carry out specific tasks. In literature, to design the grating profile for specific diffraction efficiencies requires to solve the inverse Maxwell’s equation with the optimization methods such as the genetic algorithm. The optimization process is time-consuming. By using the neural networks method to perform the learning and testing processes, we could obtain the desired grating profile with a very short time around 0.2 second. In this study, we use the Rigorous Coupled-Wave Analysis method to obtain the diffraction efficiencies of the gratings with specific dimensions. The diffraction efficiencies and the grating profiles serve as the input and the target output respectively to train the neural networks. The grating profiles of the specific diffraction efficiencies can be generated within one second and can be applied for fast grating profile generation. In order to reduce the computation time when encoding the hologram on the metasurface, we also try to use the neural networks to design the metasurface hologram. In our work, we used three light beams from different directions to switch different diffraction patterns on the hologram.

關鍵字(中)

★ 光柵設計
★ 類神經網路
★ 繞射效率

關鍵字(英)

★ grating design
★ neural networks
★ diffraction efficiency

論文目次

摘要 II
Abstract VI
誌謝 VII
目錄 VII
圖目錄 X
表目錄 XII
第一章、緒論 1
1.1 研究動機 1
1.2 人工智慧於光學結構設計之應用 2
1.3 研究方法 3
1.4 結論 5
第二章、類神經網路(Artificial Neural Networks) 6
2.1 人工類神經網路介紹及發展歷史 6
2.2 類神經網路基本單元及架構 9
2.3 類神經網路學習方法 13
2.4 結論 15
第三章、嚴格耦合波分析(Rigorous coupled-wave analysis) 16
3.1 理論介紹 16
3.2 結論 20
第四章、類神經網路應用於光柵設計 22
4.1 實驗流程 22
4.2 訓練資料產生 22
4.3 類神經網路訓練 28
4.4 類神經網路測試 35
4.5 結論 39
第五章、類神經網路應用於超穎介面全像片設計 40
5.1 超穎介面(Metasurface)原理簡介 40
5.2 計算流程 43
5.3 結論 50
第六章、結論與未來展望 51
6.1 總結 51
6.2 未來展望 51
參考文獻 52

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指導教授

陳啟昌(Chii-Chang Chen)

審核日期

2019-8-19

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