基於生成對抗式半監督學習之介電超穎介面設計

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姓名

侯明宏(MING-HUNG HOU) 查詢紙本館藏

畢業系所

光電科學與工程學系

論文名稱

基於生成對抗式半監督學習之介電超穎介面設計
(Design of Dielectric Metasurface Based on Generative Adversarial Semi-Supervised Learning)

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摘要(中)

本研究針對超穎介面中，因為幾何結構與材料數據匱乏而導致設計困難，提出了解決方法以提升設計效率。為此我們採用了條件深度卷積生成對抗網路，通過學習光譜數據來訓練模型，實現了使用光譜特性即可預測幾何結構的能力。為了提高運算效率，我們使用了嚴格耦合波展開法，結合GPU加速運算，大幅提高光學響應計算的速度。我們構建了多種幾何結構與材料特性的資料庫，涵蓋不同波長下的穿透光譜資訊，其中包括x偏振光、y偏振光、左旋圓偏振光與右旋圓偏振光入射時的穿透光譜。通過嚴格耦合波展開法與條件深度卷積生成對抗網路結合，我們在設計階段能夠快速生成並驗證符合需求的奈米結構，大幅減少了傳統設計所需的計算時間與資源。這項基於人工智慧的逆向設計方法為超穎介面的設計提供了一種高效、精確的替代方案，並有望在光學元件的設計與開發中發揮重要作用。隨著AI模型的進一步完善，我們期望該方法能推動光學元件設計領域的發展，為未來的科學研究與工業應用提供強有力的支持。

摘要(英)

This thesis proposes a solution to the scarcity of geometric structures and material data while designing metasurface, so that the design efficiency can be improved. We employed a conditional deep convolutional generative adversarial network (cDCGAN), training the model by learning spectral data to enable the prediction of geometric structures based on spectral characteristics. To improve computational efficiency, we utilized the rigorous coupled-wave analysis (RCWA) method combined with GPU-accelerated computing, significantly increasing the speed of optical response calculations. We constructed a database of various geometric structures and material properties, encompassing transmission spectral information across different wavelengths, including transmission spectrum under incident x-polarized light, y-polarized light, left-handed circularly polarized light, and right-handed circularly polarized light. By integrating the RCWA with the cDCGAN, we can rapidly generate and validate nanoscale structures that satisfy design requirements during the design phase, greatly reducing the computational time and resources required by traditional methods. This artificial intelligencebased inverse design approach provides an efficient and precise alternative for designing metasurface and is expected to play a significant role in the design and development of optical components. With further advancements in AI models, we anticipate that this method will drive progress in the field of optical component design, offering robust support for future scientific research and industrial applications.

關鍵字(中)

★ 超穎介面
★ 逆向設計

關鍵字(英)

論文目次

摘要 i
Abstract ii
致謝 iv
目錄 v
圖目錄 vi
表目錄 x
第1章緒論 1
1-1 研究背景 1
1-2 超穎介面介紹 2
1-3 基於深度學習的超穎介面應用回顧 3
1-4 研究動機 14
第2章深度學習的介紹 15
2-1 卷積網路的基礎要素與技術 15
2-1-1 卷積操作的核心要素 15
2-1-2 正則化 17
2-1-3 損失函數種類 20
2-1-4 優化器種類 21
2-2 深度學習基礎層 23
2-2-1 卷積層(Convolutional Layer) 23
2-2-2 轉置卷積層(Transposed Convolution Layer) 24
2-2-3 全連接層(Fully Connected Layer) 24
2-2-4 池化層(Pooling Layer) 25
2-3 深度學習進階層 26
2-3-1 激活層種類(Activation Layer) 26
2-3-2 Batch Normalization Layer 29
2-4 機器學習常見問題 30
2-5 深度學習模型種類介紹 32
2-5-1 卷積神經網路(CNN) 32
2-5-2 循環神經網路(RNN) 33
2-5-3 生成對抗網路(GAN) 33
2-5-4 Transformer 34
第3章數據集的建立與神經網路架構 37
3-1 數據集的計算工具 37
3-2 資料集 38
3-3 條件深度卷積生成對抗網路架構 40
3-4 結果 44
3-5 應用 50
第4章結論 54
參考文獻 55

參考文獻

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指導教授

王智明

審核日期

2025-1-7

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