晶片小型化已成為現代科技的趨勢，不可避免導致熱密度急遽上升，因此「熱管理」成為產業中重要的一環。熱遮罩能夠保護內部區域不受外部溫度影響，並且能夠維持外部溫度場不變。至今，熱遮罩已經透過轉換熱力學和雙層理論得以實現。然而，兩者皆須使用兩種以上的材料來達到保護的效果，這將不可避免地導致「界面熱阻」的問題產生。「界面熱阻」會導致溫度不連續進一步造成遮罩的保護效果不佳，但過往的研究都忽略了這個問題。本研究提出透過等效熱導率理論設計單一材料無界面熱阻熱遮罩。採用單一材料除了能夠避免界面熱阻產生的問題，也增加了對於材料選擇的多元性。此外，我們也發現等效熱導率理論並不適用於高度較高的遮罩保護範圍，因此本研究利用深度學習方法改良此理論的缺點，使其可不受高度之限制。另外，深度學習方法也可以用於設計任何遮罩形狀，可有效應用於保護不同形狀之元件。由於原先設計之無界面熱阻熱遮罩頂端為開放式設計，若是要實際應用於保護內部電子元件其效果可能有限，因此本研究利用深度學習方法在其頂部加入一層遮頂，結果表明還可降低熱遮罩整體之高度。最後，由於利用深度學習方法需先計算大量數據，增加計算時間成本，因此本研究也提出一經驗公式用於設計圓形無界面熱阻熱遮罩，結果表明確實可以利用此公式計算出相較於等效熱導率理論還更精準之圓形無界面熱阻熱遮罩。總結來說，本研究提出的方法不僅在目前的研究範圍內取得了顯著的成果，還具有廣泛的應用潛力。這些方法可以延伸至各種熱傳遞問題，包括建築物的熱能管理、工業製程中的熱交換系統，以及能源產業中的熱效率提升。因此，透過不斷的研究和改進，這些技術將能夠滿足不同行業對熱管理的需求，推動相關領域的創新和進步。

To date, thermal cloaking has been realized through transformation of thermodynamics and bilayer theory. However, both approaches require the use of multiple materials to achieve the cloaking, which inevitably leads to the issue of "interfacial thermal resistance". This resistance causes temperature discontinuities, which compromise the protective effectiveness of the cloak, an issue often overlooked in previous study. This study proposes a novel design for a single-material thermal cloak that eliminates interfacial thermal resistance by utilizing the concept of equivalent thermal conductivity. By employing a single material, this approach not only avoids the ITR problem but also broadens the range of material choices.
Furthermore, this thesis found that the equivalent thermal conductivity theory becomes less effective for cloaks with larger protective ranges. To address this limitation, this study leverages deep learning methods, which are not constrained by height restrictions. These methods also allow for the design of thermal cloaks in various shapes, enabling effective protection of components with diverse geometries. The original ITR-free cloak design featured an open-top structure, which may limit its practical applications in protecting internal electronic components. Therefore, this study incorporates deep learning to add a top cover layer, demonstrating that it can further reduce the overall height of the thermal cloak. Given that deep learning methods require processing large datasets, leading to increased computational time, this study also proposes an empirical formula for designing circular ITR-free thermal cloaks. The results indicate that this formula can more accurately calculate circular ITR-free cloaks compared to the equivalent thermal conductivity theory.
In conclusion, the methods proposed in this study not only yield significant results within the current research scope but also hold broad application potential. These techniques can be extended to various heat transfer problems, such as thermal management in buildings, industrial heat exchange systems, and improving thermal efficiency in the energy sector. With continued research and refinement, these technologies could meet the thermal management needs of diverse industries, driving innovation and progress in related fields.

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