交大電子研究所特邀德國 #Aachen大學 Christoph Jungemann教授——8/13~24開授暑修課程【 Simulation of Electronic Noise in Devices】,歡迎本系所及外系所學生踴躍來修習。7/6(五)前洽電子系所辦公室~
#上課時間:自8月13至8月24日,每週一至週四BCD及週五BCDEFG。
#加選:請於7/6(五)下午17:00前至 #電子系所辦公室(ED107)找翁小姐加選。
——106 學年(暑修)課程綱要 Summer Course Outlines ——
#課程名稱Course Name:
(英文)Simulation of electronic noise in devices
(中文)半導體元件中之電子雜訊模擬
#學分數Credits:2
#語言Language:English
#修課對象 Attendee:Student
教學要點概述Evaluation:考試Exam 60%、作業Assignment 40%
開課單位:電子研究所 Institute of Electronics
課程概述與目標Objectives:
Electronic noise is an unavoidable property of semiconductor devices and degrades their performance. It has to be taken into account in the design of analog circuits.
In the first part of the course, briefly the fundamentals of analog circuit theory (N-ports, gain and stability etc.) are reviewed. Next basic concepts of noise in devices and circuits are introduced. Based on these concepts the impedance field method is derived and compact models for noise in devices (pn diode, bipolar transistor, MOSFET) are developed. The noise properties of these devices are discussed.
In the second part of the course, the numerical methods necessary for device simulation are introduced. This covers grids, discretization of partial differential equations, solvers for large systems of equations, the drift-diffusion model, stationary calculations, transient simulations and their stability and analysis in the frequency domain. Finally, numerical devices simulation of electronic noise will be explained.
In the exercise of the first part problems relating to the theory are solved. In the second part, a simple device simulation program in MATLAB is developed.
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徵才機關
國立屏東科技大學
人員區分
其他人員
官職等
職稱
校務基金進用研究人員
職系
名額
3
性別
不拘
工作地點
90-屏東縣
有效期間
107/05/01~107/05/15
資格條件
聘期自本校通知報到日起聘,以一年一聘為原則,但計畫期限在一年以內者,應依實際所需時間聘用,任期最長以三年為限。惟如因計畫持續需要,得聘期得至計畫執行期限結束時止。
◆徵聘單位:研究總中心(機電系統整合領域)
◆徵聘職稱:研究助理等級以上
◆名額:1
◆一般資格條件:具教育部認可之電子、電機、資訊等相關系所碩士(含)以上學位。具碩士學位者,以研究助理職級聘用(比照講師之研究人員);具博士以上學位,以助理研究員職級聘用(以比照助理教授之研究人員) 職級聘用。
◆專長領域或特殊資格條件(含研究著作要求):
(機電系統整合)如同時具有以下能力者,尤佳:
1.三年以上經驗從事工業機械組件的設計與整合。
2.在自動化機器的機電組件方面具有概念和詳細設計,驗證,測試和產品運作等專業技能。
3.具有創建和執行測試和評估計劃的經驗。
4.具有組態管理的經驗。
5.對於電路板、處理器、晶片、電子設備以及電腦軟硬體(包括應用程式與編程)具有專業知識與技能。
6.具有製作精確技術計劃、藍圖、繪圖和模型所需的設計技術、工具和原理的技能。
7.具有故障排除、修理、校準和維護電子設備的經驗。
◆Department:General Research Service Center
◆Position:Research Assistant level (above)
◆Vacancy:1
◆General Requirement:A MS or PhD’s degree recognized by the Ministry of Education of the R.O.C. in relevant fields of mechanical engineering, electrical engineering, or electromechanical engineering is required. MS degree available for research assistant as lecturer. PhD’s degree available for assistant researcher as assistant professor.
◆Specialization or Special Qualification(research and publication requirement included): Electromechanical System Integration If applicants have the following criteria,it is particularly good.
1.A minimum of three years of experience on designing and integration industrial mechanical components.
2. Demonstrated design expertise in electromechanical components for automatic machines, including conceptual and detailed design, validation, test and product implementation.
3. Experience creating and executing testing and evaluation plans.
4. Experience with configuration management.
5. Knowledge of circuit boards, processors, chips, electronic equipment, and computer hardware and software, including applications and programming.
6. Knowledge of design techniques, tools, and principals involved in production of precision technical plans, blueprints, drawings, and models.
7. Experience in troubleshooting, repairing, calibrating, and maintaining electronic equipment.
工作項目
◆徵聘單位:研究總中心(大數據分析領域)
◆徵聘職稱:研究助理等級以上
◆名額:1名
◆一般資格條件:具教育部認可之統計、數學、資訊分析等相關系所碩士(含)以上學位。具碩士學位者,以研究助理職級聘用(比照講師之研究人員);具博士以上學位,以助理研究員職級聘用(以比照助理教授之研究人員) 職級聘用。
◆專長領域或特殊資格條件(含研究著作要求):
(大數據分析)如同時具有以下能力者,尤佳:
1.三年以上資料分析、模式預測、數據挖掘等相關經驗。
2.專精於資料庫的建構與維護。
3.具有利用SAS, SPSS, Python, Matlab, Stata, or R.等統計軟體的能力。
4.具有人際交流,書面/口頭交流和團隊合作技巧。
5.具有與資深管理者、教職員與IT專業人士溝通能力。
◆Department:General Research Service Center
◆Position:Research Assistant level (above)
◆Vacancy:1
◆General Requirement:A MS or PhD’s degree recognized by the Ministry of Education of the R.O.C. in relevant fields of in statistics, mathematics, informatics analytics is required. MS degree available for research assistant as lecturer. PhD’s degree available for assistant researcher as assistant professor.
◆Specialization or Special Qualification(research and publication requirement included): Big data analysis If applicants have the following criteria,it is particularly good.
1. Three years or more of experience in data analysis, predictive modeling, data mining or related.
2. Expertise in building and maintaining databases.
3. Knowledge of statistical software packages, such as SAS, SPSS, Python, Matlab, Stata, or R.
4. Effective interpersonal, written/verbal communication and teamwork skills.
5. Ability to communicate well with senior level administrators, faculty, staff, and IT professionals.
工作地址
==================
◆徵聘單位:研究總中心(人工智慧領域)
◆徵聘職稱:研究助理等級以上
◆名額:1名
◆一般資格條件:具教育部認可之資料科學、數學、物理、電腦科學等相關系所碩士(含)以上學位。具碩士學位者,以研究助理職級聘用(比照講師之研究人員);具博士以上學位,以助理研究員職級聘用(以比照助理教授之研究人員) 職級聘用。
◆專長領域或特殊資格條件(含研究著作要求):
(人工智慧)如同時具有以下能力者,尤佳:
1.三年或以上的機器學習和人工智慧技術及其在開放資源技術中的實施經驗。
2.具有從各種來源檢索,操縱,融合和利用多個結構化和非結構化數據集的經驗。
3.具有使用分佈式處理體系架構和公開來源工具(如Spark,Python或R)分析大量數據的經驗。
4.具有能夠設計從數據收集到生產部署的分析週期的能力。
5.能由廣泛的可用數據中評估任務價值。
6.能識別數據科學可以應用的問題並提出解決方案。
7.能識別和分析異常數據。
8.能夠評估現有方法,模型和演算法的可行性,以識別方法的能力和局限性。
◆Department:General Research Service Center
◆Position:Research Assistant level (above)
◆Vacancy:1
◆General Requirement:A MS or PhD’s degree recognized by the Ministry of Education of the R.O.C. in relevant fields of data science, math, computer science, physical science is required. MS degree available for research assistant as lecturer. PhD’s degree available for assistant researcher as assistant professor.
◆Specialization or Special Qualification(research and publication requirement included): Artificial intelligence If applicants have the following criteria,it is particularly good.
1. Three years or more of experience machine learning and artificial intelligence techniques and their implementations in open source technologies.
2.Experience in retrieving, manipulating, fusing, and exploiting multiple structured and unstructured data sets from various sources.
3. Experience with analyzing large volumes of data using distributed processing architectures (ie. Hadoop) with open source tools (e.g. Spark, Python, or R)
4. Ability to design analytical lifecycle from data collection to production deployment.
5. Ability to assess mission value in a wide range of available data.
6. Ability to identify problems to which data science can be applied and initiate solutions.
7.Ability to identify and analyze anomalous data (including metadata)
8. Ability to assess feasibility of existing methods, models and algorithms recognizing the capabilities and limitations of methods.
electronic circuit analysis and design 在 國立陽明交通大學電子工程學系及電子研究所 Facebook 的精選貼文
【Talk】Stochastic Simulation, Modeling & Optimization of Complex Systems via Parameterized Model Reduction
Welcome all to join it !!!
Time : 10:30am-12:00pm, April 29, 2014
Venue : ED528, Engineering Building 4, National Chiao Tung University
Prof. Luca Daniel
Massachusetts Institute of Technology
Abstract
Many complex systems developed by engineers (e.g. iPads, sensor body networks, power delivery networks, magnetic resonance imaging machines) or found in nature (e.g. the human cardiovascular system, or the geophysical oil/water/gas reservoir networks) can be viewed as large collections of interconnected dynamical system components The performance or characteristics of each individual component critically depend on what engineers or scientist refer to as “second order effects”, and can be captured only by resorting to accurate partial differential equation descriptions (e.g. Poisson, Maxwell, Navier-Stokes equations etc…). In addition, such components are often affected by random uncertainties in parameters and in geometries. In the first part of this talk I will illustrate how recent advances in computational techniques have made it possible to quantify efficiently and accurately the effect of second order effects and random uncertainties in individual system components. In the second part of this talk I will show how parameterized model order reduction techniques are beginning to enable the efficient simulation, design and optimization of “entire” complex networks of interconnected dynamical systems. Examples of complex systems analysis and optimization will be presented from the electrical engineering world including network of integrated circuit interconnect, RF inductors, micro-electro-mechanical sensors, low noise RF amplifiers, and power amplifiers, as well as city/state wide power distribution grids. At the end of the talk I will outline how the same stochastic field solver and parameterized compact dynamical modeling techniques used for designing complex electronic systems can be used to handle complex systems in other fields (e.g. to control undesired local heat deposition in human tissues by the RF power used in high resolution MRI machines, or to diagnose diseases of the human cardiovascular system, or to enable water/oil/gas reservoir exploration.)
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