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  1. NTU Theses and Dissertations Repository
  2. 重點科技研究學院
  3. 元件材料與異質整合學位學程
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/103876
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dc.contributor.advisor吳瑞北zh_TW
dc.contributor.advisorRuey-Beei Wuen
dc.contributor.author簡汝靜zh_TW
dc.contributor.authorJu-Ching Chienen
dc.date.accessioned2026-08-20T16:14:33Z-
dc.date.available2026-08-21-
dc.date.copyright2026-08-20-
dc.date.issued2026-
dc.date.submitted2026-08-10 16:40:03-
dc.identifier.citation[1] High Bandwidth Memory DRAM, Joint Electron Device Engineering Council (JEDEC), JESD270-4A, Release Version 1.1, December 2025. [Online]. Available: https://www.jedec.org/standards-documents/docs/jesd270-4a
[2] High Bandwidth Memory, Wikipedia, The Free Encyclopedia. [Online]. Available: https://en.wikipedia.org/wiki/High_Bandwidth_Memory
[3] C. E. Rasmussen and C. K. Williams, Gaussian Processes for Machine Learning, no. 3. Cambridge, MA, USA: MIT Press, 2006, Sec.2.2.
[4] K. Son, S. Kim, M. Kim, D. Lho, K. Kim, H. Park, G. Park, and J. Kim, “Signal integrity analysis of high speed channel considering thermal distribution,” in IEEE 30th Conf. Electr. Perform. Electron. Packag. Syst., Oct. 2021, Austin, TX, USA.
[5] S.-F. Yang, W.-C. Wang, Y.-T. Lin, C.-C. Hung, H.-Y. Tung, and J. Hsieh, “Signal integrity designs at organic interposer CoWoS-R for the HBM3 - 9.2Gbps high speed interconnection of the 2.5D-IC chiplets integration,” in IEEE 74th Electron. Compon. Technol. Conf., May 2025, Denver, Colorado, USA.
[6] K. Son, K. Kim, S. Choi, J. Yoon, J. Kim, J. Hong, H. Kim, J. Lee, and J. Kim, “The significance of thermal-aware universal chiplet interconnect express (UCIe) interface design in 2.5D/3D ICs,” in IEEE Electr. Design Adv. Packag. Syst., Dec. 2023, Rose-Hill, Mauritius.
[7] H.-C. Kuo, P.-C. Pan, L.-C. Hung, M.-F. Jhong, and C.-C. Wang, “Worst eye performance analysis for advanced package die-to-die interconnects,” in 2025 IEEE 29th Workshop Signal Power Integrity, May 2025, Gaeta, Italy.
[8] P. Triverio, S. Grivet-Talocia, and M. S. Nakhla, “A parameterized macromodeling strategy with uniform stability test,” IEEE Trans. Adv. Packag., vol. 32, no. 1, pp. 205–215, Feb. 2009.
[9] P. Manfredi, D. Vande Ginste, D. De Zutter, and F. G. Canavero, “Generalized decoupled polynomial chaos for nonlinear circuits with many random parameters,” IEEE Microw. Wireless Compon. Lett., vol. 25, no. 8, pp. 505–507, Aug. 2015.
[10] W.-D. Guo, J.-H. Lin, C.-M. Lin, T.-W. Huang, and R.-B. Wu, “Fast methodology for determining eye-diagram characteristics of lossy transmission lines,” IEEE Trans. Adv. Packag., vol. 32, no. 1, pp. 175–183, Feb. 2009.
[11] T. Lu, J. Sun, K. Wu, and Z. Yang, “High-speed channel modeling with machine learning methods for signal integrity analysis,” IEEE Trans. Electromagn. Compat., vol. 60, no. 6, pp. 1957–1964, Dec. 2018.
[12] H. Ma, E.-P. Li, A. C. Cangellaris, and X. Chen, “Comparison of machine learning techniques for predictive modeling of high-speed links,” in Proc. IEEE 28th Conf. Elect. Perform. Electron. Packag. Syst., May 2019, Montreal, QC, Canada.
[13] Q. Wu, H. Wang, and W. Hong, “Multistage collaborative machine learning and its application to antenna modeling and optimization,” IEEE Trans. Antennas Propagat., vol. 68, no. 5, pp. 3397–3409, May 2020.
[14] C. Harvey, Md. S. Faruk, and S. J. Savory, “Data-driven erbium-doped fiber amplifier gain modeling using Gaussian process regression,” IEEE Photon. Technol. Lett., vol. 36, no. 18, pp. 1097–1100, Sept. 2024.
[15] H. Ma, E.-P. Li, A. C. Cangellaris, and X. Chen, “Support vector regression-based active subspace (SVR-AS) modeling of high-speed links for fast and accurate sensitivity analysis,” IEEE Access, vol. 8, pp. 74339–74348, Apr. 2020.
[16] K.-B. Wu, T.-Y. Kuo, C.-C. Hung, B. Lin, I.-H. Peng, M.-T. Yang, and R.-B. Wu, “Novel RDL design of wafer-level packaging for signal/power integrity in LPDDR4 application,” IEEE Trans. Compon., Packag., Manuf. Technol., vol. 8, no. 8, pp. 1431–1439, Aug. 2018.
[17] Z. Brari and S. Belghith, “A new method for the detection of epilepsy and epileptic seizures based on the variance of EEG signals and its derivatives with a simple kernel trick,” in Int. Conf. Adv. Syst. Emergent Technol., Dec. 2020, Hammamet, Tunisia.
[18] MATLAB R2023b, MathWorks. [Online]. Available: www.mathworks.com
[19] S. H. Hall and H. L. Heck, Advanced Signal Integrity for High-Speed Digital Designs. John Wiley & Sons, Inc., 2011
[20] “Pseudorandom Binary Sequence” on Wikipedia, The Free Encyclopedia. [Online]. Available: https://zh.wikipedia.org/wiki/偽亂數二進位數列
[21] Q2D Extractor, ANSYS Inc. [Online]. Available: www.ansys.com
[22] F.-R. Bai, C.-Y. Lu, C.-M. Lin, and R.-B. Wu, “Vector fitting method in transient thermal analysis for heterogeneous multilayered packaging,” in IEEE Electr. Design Electron. Packag. Syst. (EDAPS), Dec. 2025, Sapporo, Japan.
[23] Designer, ANSYS Inc. [Online]. Available: www.ansys.com
[24] 李愷,於 DDR 模組中多條耦合線的快速眼圖指標解析法,國立臺灣大學碩士論文,2023年8月。
[25] Advanced Design System, Keysight Technologies, 2024. [Online]. Available: www. keysight.com
[26] “Latin Hypercube Sampling” on Wikipedia, The Free Encyclopedia. [Online]. Available: https://zh.wikipedia.org/wiki/拉丁超立方抽樣
[27] High Frequency Structure Simulator (HFSS), Release 2023.R1, ANSYS Inc. [Online]. Available: www.ansys.com
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/103876-
dc.description.abstract本論文應用高斯過程迴歸法 (Gaussian Process Regression, GPR) 於訊號完整度分析,藉由具備物理意義之簡化變數取代原始多維設計參數,從而建立傳輸訊號系統所響應眼圖之預測模型;爰此,僅需少量訓練資料即可達到高準確度之眼圖預測,並且大幅地降低模擬時間與資料需求。此外,結合導線的導電係數與介電常數隨溫度變化之關係,所建立之GPR模型可以有效地考慮熱效應之影響,快速地建構符合JEDEC規範之設計範圍,而適用High Bandwidth Memory (HBM) 連線於Chip-on-Wafer-on-Substrate (CoWoS) 與Integrated Fan-Out (InFO) 等電子封裝結構,以提升高速連線系統之設計效率。就延伸之應用,本研究亦將GPR與主動子空間法 (Active Subspace Method) 結合應用於天線設計問題,藉由降維分析而建立共振頻率與輸入阻抗之近似解析設計關係,再以微帶天線為例,求得幾何參數之低維設計空間,能快速地設計天線之長寬尺寸與饋入位置。綜上所述,本論文所提出之方法可在維持預測準確度之情況下,大幅地降低計算成本,並且可應用於高速連線結構與微帶天線設計之問題,展現良好之效率與實用性。zh_TW
dc.description.abstractBased on the Gaussian Process Regression (GPR), the reduced variables with a physics meaning are investigated in this thesis to replace the high-dimensional design parameters so as to analyze the signal integrity of a transmission system by constructing an efficiently responded eye-digram prediction model. This proposed approach achieves high prediction accuracy by employing only a small amount of training data, yet significantly reducing both the simulation time and data requirements. Concerning the temperature-dependent behavior of the metal conductivity and dielectric permittivity through the interconnect, this developed GPR model effectively accounts for its thermal effect to enable a rapid construction of the design solution-spaces that comply with JEDEC specifications. The method further applies to the HBM link in such as the CoWoS and InFO packaging structures, thereby improving the design efficiency of the high-speed interconnect systems. For a patch-antenna design, this thesis integrates GPR with Active Subspace Method through dimensionality reduction to approximate the analytical design relationships between the resonant frequency and input impedance. A reduced design space of the patch antenna is thus exploited to tune its dimensions and feeding positions to meet the specific frequency responses. Overall, the proposed methodology maintains the high prediction accuracy while significantly reducing the computational cost to be applied for both the SI analysis of high-speed links and the agile design of patch antennas, demonstrating strong efficiency and practical value.en
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dc.description.tableofcontents口試委員會審定書 i
致謝 ii
摘要 iii
Abstract iv
目次 v
圖次 viii
表次 x
第1章 緒論 1
1.1 研究動機 1
1.2 文獻回顧 2
1.3 重要貢獻 3
1.4 章節概要 4
第2章 理論背景 6
2.1 高斯過程迴歸 (Gaussian Process Regression, GPR) 6
2.2 超參數求解 10
2.2.1 梯度下降法 (Gradient Descent) 11
2.2.2 共軛梯度法 (Conjugate gradient method) 12
2.3 主動子空間法 (Active Subspace Method) 15
2.3.1 理論介紹 16
2.3.2 應用分析 17
第3章 眼圖介紹與傳輸線等效電路 21
3.1 眼圖原理及分析 21
3.1.1 眼圖形成原理 21
3.1.2 偽隨機二進位序列 (Pseudo-Random Binary Sequence, PRBS) 23
3.2 傳輸線等效電路 23
3.3 等效電路參數擷取 25
3.4 眼圖高度之擷取 28
3.4.1 符碼間干擾 (Inter-Symbol-Interference, ISI) 29
3.4.2 眼圖最劣序列 (Worst Bit) 31
第4章 應用高斯過程迴歸法於眼高分析與設計 34
4.1 高斯過程迴歸法眼高預測模型 34
4.1.1 模型輸入簡化變數選擇 34
4.1.2 主動子空間法 36
4.2 高斯過程迴歸法與人工神經網路之比較 40
4.3 眼高設計圖 42
4.3.1 眼高設計圖之用途 42
4.3.2 眼高設計圖之分析 43
4.3.3 源端電阻與負載電容對訊號完整度的影響 46
第5章 結合高斯過程迴歸核方法與主動子空間法於微帶天線設計 50
5.1 微帶天線問題的訓練資料 50
5.1.1 微帶天線結構 50
5.1.2 拉丁超立方抽樣與樣本空間 51
5.1.3 共振頻率與輸入阻抗之資料處理 52
5.2 高斯過程迴歸法之應用與分析 54
5.2.1 共振頻率 54
5.2.2 輸入阻抗 55
5.3 主動子空間法分析 56
5.3.1 共振頻率設計簡化公式 56
5.3.2 輸入阻抗設計簡化公式 59
5.4 微帶天線設計圖 61
第6章 結論 65
參考文獻 67
Publications 70
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dc.language.isozh_TW-
dc.subject高斯過程迴歸法-
dc.subject訊號完整度-
dc.subject高頻寬記憶體連線-
dc.subject眼高等高線圖-
dc.subject主動子空間法-
dc.subject微帶天線-
dc.subjectGaussian Process Regression-
dc.subjectSignal Integrity-
dc.subjectHigh Bandwidth Memory Interconnects-
dc.subjectEye-Height Contour-
dc.subjectActive Subspace Method-
dc.subjectPatch Antenna-
dc.title應用高斯過程迴歸法於訊號完整度與天線的代理模型與設計zh_TW
dc.titleSurrogate Modeling and Design for Signal Integrity and Antennas Using Gaussian Process Regressionen
dc.typeThesis-
dc.date.schoolyear114-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee陳泓銓;黃銘崇;林建民;邱煥凱zh_TW
dc.contributor.oralexamcommitteeHung-Chuan Chen;Ming-Chong Huang;Chien-Min Lin;Hwann-Kaeo Chiouen
dc.subject.keyword高斯過程迴歸法; 訊號完整度; 高頻寬記憶體連線; 眼高等高線圖; 主動子空間法; 微帶天線zh_TW
dc.subject.keywordGaussian Process Regression; Signal Integrity; High Bandwidth Memory Interconnects; Eye-Height Contour; Active Subspace Method; Patch Antennaen
dc.relation.page70-
dc.identifier.doi10.6342/NTU202603641-
dc.rights.note未授權-
dc.date.accepted2026-08-13-
dc.contributor.author-college重點科技研究學院-
dc.contributor.author-dept元件材料與異質整合學位學程-
dc.date.embargo-liftN/A-
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