隨著光譜分析技術在各領域應用的快速發展，微型化光譜儀的需求日益增長。傳統光譜儀依賴光柵或稜鏡等色散元件，體積龐大且成本高昂，限制了其在便攜式設備中的應用。本論文提出基於超穎介面的壓縮感知光譜重建系統，透過奈米轉印技術製作鋁洞陣列結構，結合深度學習增強方法實現高精度的微型光譜儀。利用嚴格耦合波分析法設計49個具有不同幾何參數的超穎介面單元，線寬範圍從90 nm至390 nm，週期與線寬比為2及2.5，在400-700 nm可見光範圍內展現差異化的光學響應，平均相關係數為0.2942。製程採用PFPE軟模具複製矽母模結構，透過NOA63作為透明聚合物基底，以熱蒸鍍沉積50 nm鋁薄膜，最後使用膠帶移除平坦區域的鋁，形成次波長孔洞陣列。實驗量測顯示各超穎介面單元間平均相關係數為0.3536，條件數為828。在光譜重建方面，實現並評估了十種壓縮感知方法，包括Ridge迴歸、ADMM-TV、FISTA等，並開發四層全連接神經網路進行深度學習增強。統計分析顯示，Ridge（α=0.01）方法經深度學習增強後達到高度顯著改善（p=0.002），效果量達Cohen's d=1.008，重建結果幾乎完全接近真實光譜，在採用ADMM-TV配合深度學習增強時，可達到約7-8 nm的有效光譜解析度。本研究成功實現了無色散元件的微型光譜儀，證實超穎介面結合壓縮感知與深度學習在光譜重建的可行性。

With rapid development of spectroscopic applications, demand for miniaturized spec-trometers has increased. Traditional spectrometers rely on dispersive elements like gratings, resulting in bulky and expensive systems. This thesis presents compressed sensing spectral reconstruction based on metasurfaces, fabricating aluminum hole ar-rays through nanoimprint technology combined with deep learning enhancement. Us-ing RCWA, 49 metasurface units were designed with linewidths from 90-390 nm and period-to-linewidth ratios of 2 and 2.5, showing differentiated optical responses in 400-700 nm with average correlation coefficient of 0.2942. Fabrication employed PFPE soft molds replicating silicon masters, NOA63 as transparent polymer substrate, 50 nm aluminum deposition via thermal evaporation, and selective tape removal forming subwavelength hole arrays. Measurements showed average correlation coef-ficient of 0.3536 between units with condition number 828. Ten compressed sensing methods were implemented including Ridge regression, ADMM-TV, and FISTA, with four-layer neural network for enhancement. Statistical analysis revealed Ridge (α=0.01) achieved highly significant improvement (p=0.002) with Cohen's d=1.008 after deep learning, producing near-perfect spectral reconstruction. When ADMM-TV is employed in combination with deep learning enhancement, an effective spectral resolution of approximately 7–8 nm can be achieved.

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