雖然對具有不規則板形和多層堆疊的印刷電路板（PCB）進行電源分布網路（PDN）的建模與阻抗曲線模擬是一項艱鉅的任務，但這仍是電源分布網路（PDN）設計和性能評估的關鍵過程。在特定頻率範圍內，PCB板的阻抗曲線需要滿足目標阻抗的要求。本文提出了一種新的深度學習方法PIPPON來預測PDN阻抗，該方法包含了一個專門處理阻抗曲線極點範圍的候選網絡(proposal network)。實驗結果顯示，PIPPON不僅比先前的深度學習方法能產生更準確的阻抗預測結果，還保持了與先前方法同等級的快速計算時間。同時，因PIPPON專注於阻抗的極點部分，使得在判斷阻抗曲線是否符合目標阻抗要求時擁有更為準確的判定。

While it is a difficult task to model and simulate a power distribution network’s (PDN) impedance profile for printed circuit boards (PCBs) with irregular board shapes and multi-layer stackup, it is a crucial process for the design and performance evaluation of the PDN. This paper proposes a new deep learning method PIPPON for PDN impedance prediction which contains a proposal network that specializes in the prediction of impedance profile in the range around a pole point. The result shows that PIPPON not only produces more accurate results (with 30% relative error reduction) than the previous deep learning method but also maintains the same level of fast computation time as the previous method. Meanwhile, PIPPON focuses on impedance pole points resulting in a more accurate picture of whether the impedance profile meets the target impedance requirement.

Contents
Chapter 1. Introduction------------ 1
Chapter 2. Related Works----------- 4
Chapter 3. The Proposed Framework-- 7
Chapter 4. Experimental Results--- 14
Chapter 5. Conclusion------------- 22
References------------------------ 23

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