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研究生: 陳建宇
Jian-Yu Chen
論文名稱: 基於迴歸分析之類比電路行為模型自動產生器
Regression-Based Behavioral Model Generator for Analog Circuit Blocks
指導教授: 劉建男
Chien-Nan Liu
口試委員:
學位類別: 碩士
Master
系所名稱: 資訊電機學院 - 電機工程學系
Department of Electrical Engineering
論文出版年: 2020
畢業學年度: 103
語文別: 中文
論文頁數: 67
中文關鍵詞: 行為模型迴歸
外文關鍵詞: Behavioral Model, Regression
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  • 加速類比與數位混合訊號模擬的時間是現在單晶片系統設計設計驗證中很重要的一環,針對特定電路以硬體描述語言的方式對特定電路建立其行為模型,是一種有效率的混合訊號系統驗證方式,在本論文中我們希望能針對任意的電路發展一套自動建立行為模型方法,因為整個電路行為過於複雜,我們將設計的層級從電晶體層級拉到行為階層,透過將電路分割成小區塊,使其行為簡單化,並且使用迴歸分析的技巧將雜散的電路資訊公式化,以降低驗證流程的複雜度並且達到加速電路模擬的效果,由幾個電路上的實驗結果來看,我們確實能夠建構出對應的電路行為模型,並維持一定的準確度


    Speeding up analog and mixed signal simulation is important in SoC design verification. Modeling circuit blocks by hardware description language for specific circuits and building their behavioral models is an efficient verification approach for AMS systems. In this thesis, we focus on the automatic generation of a behavioral model for arbitrary circuit netlist. By separating the circuit into several building blocks, the complexity of circuit behavior is reduced such that its behavioral model can be built by regression-based approach.
    With those behavioral models, the verification complexity and the simulation time can be reduced significantly. As shown in the experimental results on several circuits, the proposed approach is able to generate the corresponding behavioral models automatically with good accuracy.

    摘要 v abstract vi 目錄 vii 圖目錄 x 第一章、緒論 1 1- 1 研究動機 1 1- 2 行為模式(Behavioral Modeling) 4 1- 3 論文結構 7 第二章、背景知識 9 2- 1 宏模型簡介(Macromodel) 9 2- 2 符號函式方法(Symbolic Approach) 11 2- 3 演算式方法(Algorithmic Approach) 13 2-3-1 片段線性估算法(Piecewise Approximation Methods) 17 2- 4 迴歸分析 (Regression Analysis) 19 第三章、行為模型產生器 22 3- 1 行為模型產生器流程 22 3- 2 二階段架構分析(Two-stage Structure Analysis) 23 3-2-1 數位架構分析(digital Circuit Structure Analysis) 25 3-2-2 類比架構分析(Analog Circuit Structure Analysis) 29 3-2-3 優勢圖(Dominance Graph) 33 3- 3 定義構件之模型迴歸公式 35 3- 4 時間資訊模型(Timing Information Model) 39 第四章、階層式模型產生 41 4- 1 架構分析結果 41 4- 2 行為模型模擬結果 43 第五章、未來目標 49 第六章、參考文獻 50

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