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研究生:陳正凱
研究生(外文):Cheng-Kai Chen
論文名稱:基因演算法在深次微米MOSFET元件參數萃取與模擬之應用
論文名稱(外文):A Genetic Algorithm for Deep-Submicron MOSFET Parameters Extraction and Simulation
指導教授:孫春在孫春在引用關係李義明李義明引用關係
指導教授(外文):Chuen-Tsai SunYiming Li
學位類別:碩士
校院名稱:國立交通大學
系所名稱:資訊科學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
論文頁數:119
中文關鍵詞:參數萃取基因演算法金氧半場效電晶體系統整合晶片
外文關鍵詞:parameter extractionGenetic algorithmMOSFETSOC
相關次數:
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  • 下載下載:64
  • 收藏至我的研究室書目清單書目收藏:2
基因演算法自從被發表以來,已經被廣泛的應用在各種領域中,如最佳化設計、超大型積體電路佈線設計、系統穩定控制、圖形識別、影像處理﹒﹒﹒等等,在本篇論文中,我們嘗試將基因演算法應用在深次微米MOSFET元件的模式參數萃取上,傳統設計超大型積體電路時,設計師如何設定電路模式中的各個物理參數,使其電路特性(如電流-電壓曲線圖)符合所需,往往是個非常困難的問題,我們透過基因演算法,自動、快速、準確地萃取出最適當的參數設定,大大的降低元件及電路的開發成本,而這套開發完成的自動化萃取參數的流程,以MOSFET元件與電路作為探討範例,模擬結果與實驗數據相比較,呈現它的高精確度及方法的極佳效率性,更可以應用在不同的元件設計上,如:奈米元件電路設計、系統整合晶片設計、RF高頻元件電路設計﹒﹒﹒等等。

Genetic algorithm is a stochastic-based optimization strategy with its randomly but systematically search strategy which is usually applied for solving complex problem, such as simulated model parameters extraction. To characterize the properties of MOSFET accurately, various compact models have been proposed for deep-submicron and nanoscale MOSFET device simulation. Each model consists of diverse governing equations and parameters. It leads to a multivariable optimization problem to be solved and extracted efficiently for the device applications. Different approaches, for instance the direct method and numerical method have been applied to extract and optimize the model parameters. In this work we present a unified multi-objective evolutionary approach for BSIM3 MOSFET model parameter extraction. In contract to conventional time-consuming large-scale approach, our genetic algorithm includes: (1) a physical-based weight function; (2) floating-point operators; and (3) dynamic mutation techniques, and solves the problem efficiently. The proposed method outputs a set of optimal parameters for device simulation; in our simulation experiences, this method is stable and accurate. Comprehensive comparisons among models are reported for the parameters sensitivity test. Simulations and measurements for sub-micron MOSFETs compact models are examined to show the accuracy and robustness of the method. The developed CAD tool can be further applied to extract nanoscale MOSFTEs parameters for advanced VLSI circuit design and SOC applications.

Abstract
Acknowledgments
List of Tables
List of Figures
1. Introduction
1.1. Motivation
1.2. Background
1.2.1. Semiconductor Device Simulation
1.2.2. Model Parameters Extraction
1.2.3. Genetic Algorithm
1.2.4. Historical Development
1.3 Objectives
1.4 Outline of the Thesis
2. MOSFET Compact Models and Parameters Extraction Methods
2.1. The MOSFET BSIM3 Compact Model
2.1.1. A Physical-Based Derivation of the Unified I-V Model
2.1.2. A Classification of Extracted Parameters
2.2. Conventional Parameters Extraction Methods
3. Genetic Algorithm for Deep-Submicron MOSFET Simulation
3.1. Problem Definition and Analysis
3.2. Evolutionary Computation Steps
3.2.1. Gene Encoding
3.2.2. Competition Procedure
3.2.3. Selection Method
3.2.4. Recombination Process
3.2.5. Mutation Scheme
3.2.6. Termination Criterion
4. Simulation Results and Discussion
4.1. Accuracy and Efficiency of the Developed BSIM3v3 Simulator and Optimizer
4.2. Effects of Different Weight Functions
4.3. The Uniqueness of Extracted Parameters
4.4. The Sensitivities of the Extracted Parameters
5. Conclusions
5.1. Summary
5.2. Suggestions and Future Works
References
Appendix A-The EKV I-V model in DC simulation
Appendix B-The MosM9 I-V model in DC simulation
Appendix C-The BSIM3v3 I-V model in DC simulation
Appendix D-List of Tables
Appendix E-List of Figures
Appendix F-VITA

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