电子噪声与低噪声设计
本书致力于利用随机噪声理论分析和解释电子系统中噪声的产生和传播问题，介绍各种噪声源相关的机制和模型，说明不同噪声的特性和传播方式，以及线性电路中的噪声分析方法和噪声特性测量方法，并详细介。。。内容简介电子噪声包括内部固有噪声和外部干扰噪声。电子噪声是影响检测系统性能的主要因素之一。在通信系统中,噪声可能导致信息传输错误本书致力于利用随机噪声理论分析和解释电子系统中噪声的产生和传播冋题,介绍各种噪声源相关的机制和模型,说明不同噪声的特性和传播方式,以及线性电路中的噪声分析方法和噪声特性测量方法,并详细介绍各种不同噪声的抑制方法,给出大量实例,总结出低噪声设计的规则和要点。木书可用作电子工程、自动化、测试技术与仪器等专业的本科生或研究生教材,也可供涉及电子噪声和电磁兼容性的工程技术人员参考。本书封面贴有清华大学出版社防伪标签,无标签者不得销售版权所有,侵权必究。侵权举报电话:010-6278298913701121933图书在版编目(CIP)数据电子噪声与低噪声设计/高晋占编著.一北京:清华大学出版社,2016ISBN978-7302-43559-4I.①电…Ⅱ.①高…Ⅲ.①电子系统一噪声②电子系统一低噪声一设计ⅣN.①TN911.4②TN722.3中国版本图书馆CIP数据核字(2016)第081960号责任编辑:王一玲封面设计:常雪影责任校对:梁毅责任印制:沈露出版发行:清华大学出版社pogtlt:http://www.tup.com.cn,http://www.wqbook.com地址:北京清华大学学研大厦A座邮编:100084社总机:010-62770175邮购:010-62786544投稿与读者服务:010-62776969,c-service(@tup.tsinghua.edu.cn质量反馈:010-62772015, zhiliang tup. tsinghua.edu.cn印装者:清华大学印刷厂经销:全国新华书店开本:185mm×260mm印张:21字数:522千字版次:2016年6月第1版印次:2016年6月第1次印刷印数:1~1500定价:59.00元产品编号:06269401在电子电路和系统中,噪声是个重要问题。噪声污染有用信号,并使信号包含的信息增加了不确定性。电子噪声是影响检测系统性能的主要因素之一。在通信系统中,噪声可能导致信息传输错误。即使在噪声阈值较高的数字电路和计算机系统中,严重的噪声可能造成存储位的变化和程序运行混乱噪声包括内部固有噪声和外部干扰噪声。内部固有噪声是由载流子的随机运动引起的,有些固有噪声源可以通过在制造过程中提高加工质量加以控制,但其中大多数是基础噪声,不取决于技术。而外部干扰噪声是由外部噪声源发岀,经过某种耦合渠道对电路污染的结果。这两种噪声具有不同原因,它们需要不同的处理方法,在多数书籍和文献中,这两种噪声都是分别对待的,外部干扰噪声通常是电磁兼容性(EMC)相关书籍的主题。但是,这两种噪声引起的问题是类似的,应该综合在一起考虑。在处理其中的一种噪声时,有理由必须把另一种噪声也考虑在内。例如,当处理弱信号的电路无法正常工作时,污染了有用信号的噪声是源自于该电路本身还是从外部拾取的,从用户的角度来看都是无关紧要的。在这两种情况下噪声都会掩盖信号,在最坏的情况下则不能恢复信息内容。因此,只努力抑制电路的固有噪声,但缺乏抵御干扰噪声的保护手段,电路的噪声特性就会大打折扣。另外,在设计屏蔽措施时,努力把干扰噪声降低到固有噪声幅度之下,往往没有多大意义。本书涵盖上述两种噪声,致力于分析和解释电子系统中各种噪声的来源和性质,介绍各种噪声源的机制和模型,说明不同噪声的特性和耦合方式,以及线性电路中的噪声分析方法和噪声特性测量方法,介绍各种噪声的抑制措施,给出低噪声设计的规则和方法。许多种噪声具有随机性,其描述方式和分析方法不同于确定性信号,不太容易理解,本书第1章首先介绍随机噪声的基本原理和特性,这是后续各章及延续阅读的理论基础。第2~5章致力于固有噪声,这种噪声取决于电子器件和电路设计。第2章介绍各种固有噪声源的特性和描述方法;第3章介绍各种噪声参数和噪声分析方法;第4章介绍电子系统中常见的电子器件的噪声源、噪声模型和噪声特性;第5章介绍常用的噪声性能测量方法。前言第6~8章致力于外部干扰噪声,这种噪声受设备的物理结构和电路布局的影响很大。第6章介绍各种干扰噪声源和干扰耦合途径,除电磁噪声外,还特别介绍机械原因或温度扰动引起的噪声;第7章介绍干扰噪声抑制方法,重点是屏蔽和接地;第8章介绍常见干扰噪声源的噪声产生机制和预防措施。第9章介绍低噪声电路设计的方法和技术,包括选择低噪声有源器件,确定电路组态和工作点,噪声匹配的实现等,特别分析了反馈对噪声性能的影响。本书可用作电子、通信、自动化、测试技术与仪器等专业的高年级本科生或研究生教材,也可供涉及电子噪声和电磁兼容性的工程技术人员参考。由于作者水平所限,书中难免存在缺点和错误,恳请广大读者批评指正高晋占2015年10月于清华园符号說明1.基本符号X电抗的通用符号,单位为Ω频率通用符号,单位为Hz导纳的通用符号,单位为Sfo中心频率,单位为Hz阻抗的通用符号,单位为Ω截止频率,单位为Hz角频率通用符号,单位为rad/s电流通用符号,单位为A2.线性系统符号距离或长度,单位为mA(t)幅度函数电压通用符号,单位为V)相位函数器件内部的等效电阻,单位为9G(a)幅频特性函数B系统频带宽度,单位为Hz相频特性函数B电纳的通用符号,单位为Sh(t)冲激响应函数C电容的通用符号,单位为FH(j)频率响应函数E数学期望运算子H()传递函数电导的通用符号,单位为SH(x1)离散传递函数电流的有效值,单位为A3.随机噪声符号平均直流电流,单位为A噪声电压L电感的通用符号,单位为H噪声电流互感的通用符号,单位为H噪声电压的均方值P功率的通用符号,单位为W噪声电流的均方值R电阻或等效电阻,单位为ΩE电路的输入电阻,单位为Ω噪声电压的有效值,En=√eR电路的输出电阻,单位为噪声电流的有效值,n=√R负载电阻,单位为Ω噪声电压的平方根谱密度,单位R信号源内阻,单位为Ω为V/√Hz电压的有效值,单位为V噪声电流的平方根谱密度,单位热力学温度(旧称绝对温度),单为A/√Hz位为K热噪声电压符号说明热噪声电流共射接法下集射极之间的微变电散弹噪声电压阻散弹噪声电流场效应管漏源之间的等效电阻1/f噪声电压导通电阻1/f噪声电流二极管,场效应管的漏极F噪声系数( noise factor)场效应管的栅极噪声因数( noise figure),单位为dBS场效应管的源极S信噪比二极管电流,漏极电流B等效噪声带宽共射接法下的基极电流△f窄带宽度共射接法下的集电极电流p(x)x的概率密度函数共射接法下的发射极电流x的均值共基接法下的电流放大倍数,a=x的方差△Ic/△Ix的标准差共射接法下的电流放大倍数,B=x的均方值△Ic/△IBC2(x)x的自协方差函数共射接法下的直流电流放大倍Cx(x)x的归一化自协方差函数数,B=Ic/IBCx(z)x和y的互协方差函数5.其他符号Cx(x)x和y的归一化互协方差函数电磁辐射速度,c=2.998×10m/sR2(r)x的自相关函数h普朗克( Planck)常数,h=6.62R2(x)x和y的互相关函数1034JsS(f)噪声的功率谱密度函数k玻耳兹曼( Boltzmann)常数,k=S2(f)噪声电压的功率谱密度函数1.38×1023J/K(f)噪声电流的功率谱密度函数电子电荷,q=1.602×10-°C2(f)x的功率谱密度函数波长,mS2(f)x和y的互功率谱密度函数介质的介电常数p(x)x的归一化自相关函数自由空间的介电常数,Eo=8.85×(x)x和y的归一化互相关函数10 pF/mmJ|雅可比( Jacobi)行列式对自由空间的相对介电常数,En=4.半导体器件参数符号基极介质的磁导率C集电极自由空间的磁导率,A0=4x发射极10Hm1=4x×10pH/mmfr晶体管的特征频率,即共射接法对自由空间的相对磁导率,=下电流放大倍数为1的频率,单/0位为Hz介质的电导g跨导铜的电导,=5.82×107S/m基区体电阻对铜的相对电导,01=a/0rb’e发射结的微变等效电阻CMRR共模抑制比第1章随机噪声基础1.1随机噪声概述…………1.1.1噪声定义与分类1111.1.2内部固有噪声和外部干扰噪声比较1.1.3噪声的影响1.2随机噪声的概率分析方法…3471.3随机噪声的统计特征…1.3.1均值、方差与均方值········,····,·,,··,,,,,,······,·······,·,,1.3.2相关函数与协方差函数…1.3.3功率谱密度函数151.4常见随机噪声171.4.1白噪声与有色噪声171.4.2窄带噪声………………………………………………………191.5随机噪声通过电路系统的响应…211.5.1随机噪声通过线性系统的响应……………………………211.5.2随机噪声通过非线性系统的响应24第2章电子系统中的固有噪声源……………………………………………………292.1热噪声302.1.1热噪声的起源…302.1.2热噪声的特性……………………302.2扩散噪声……………………………………352.3散弹噪声362.4量子噪声………………………………………………………………………………39Ⅵ目录2.5产生-复合噪声(G-R噪声)…………………………………………………………402.61/f噪声…422.7爆裂噪声……………………………………………………………………452.8雪崩噪声…第3章噪声参数与噪声分析503.1.功率和增益3.1.1功率的各种常用定义513.1.2资用功率和资用功率增益…3.1.3可交换功率和可交换功率增益553.2等效噪声带宽…563.3线性一端口的噪声参数……603.3.1等效噪声电阻…………………………………613.3.2等效噪声温度623.3.3其他噪声参数……633.4线性二端口的噪声模型与噪声参数653.4.1E-Ⅰ噪声模型及等效输入噪声电阻…………………3.4.2等效输入噪声温度………693.4.3工作噪声温度……………723.4.4噪声系数……733.4.5噪声测度………………………………………………………………813.5二端口噪声分析………833.5.1二端口的噪声模型变换…………………………………………………833.5.2等效噪声源相关时二端口的噪声分析…………84第4章电子器件噪声884.1电阻噪声………884.1.1电阻的噪声机制与噪声指标…………………………894.1.2低噪声电阻的选择4.2电容、电感和电池噪声934.3半导体二极管的噪声特性…………………………………………………………954.4双极型晶体管(BJT)的噪声特性……994.4.1BJT的结构、等效电路和噪声源………………………………………994.4.2BJT的噪声参数1024.4.3BJT噪声的频率分布……………………………………………………1044.5场效应管(FET)的噪声特性1075.1FET的结构与噪声源…………1074.5.2FET的噪声等效电路与噪声参数1104.6运算放大器的噪声特性………………………………………………………113目录4.6.1运算放大器的等效输入噪声模型………………………………1134.6.2运算放大器噪声性能计算1164.7传感器电路噪声分析………………………………………………………122第5章噪声性能测量1335.1噪声测量常用方法……1335.2噪声测量中的检波器和平均器………………………………………………………1365.3噪声功率和有效值的测量误差1404噪声功率谱密度测量………………………………………………………1425.5二端口等效输人噪声测量1465.6噪声系数测量…………………………………………………………………1475.7噪声温度测量……1545.8其他噪声性能的测量和计算………………………………………………1575.9噪声发生器160第6章干扰噪声1666.1外部噪声源………………………………………1676.1.1自然噪声源…1686.1.2电磁噪声源1706.1.3静电噪声源…1736.1.4非电起源的干扰噪声源………………………………………………1746.1.5干扰噪声的频谱分布1766.2干扰噪声耦合途径……………………………………………………………1776.2.1传导耦合…1796.2.2电场耦合………………………………………………………1836.2.3磁场耦合………1866.2.4电磁辐射耦合………1916.2.5耦合模式……………………………193第7章干扰噪声抑制方法…1967.1电磁屏蔽………………………………………………………………………1967.1.1场传播与波阻抗1977.1.2屏蔽层的吸收损耗……………………………………………………2007.1.3屏蔽层的反射损耗2027.1.4屏蔽层中的多次反射…………………………………………2067.1.5屏蔽效能分析与综合2087.1.6影响屏蔽效能的其他因素…………………………………2117.1.7屏蔽总结2147.2电缆屏蔽层接地216
- 2020-12-04下载
- 积分：1
Lectures on Stochastic Programming-Model
这是一本关于随机规划比较全面的书！比较难，不太容易啃，但是读了之后收获很大。这是高清版的！To Julia, Benjamin, Daniel, Nalan, and Yael;to Tsonka Konstatin and Marekand to the memory of feliks, Maria, and dentcho2009/8/20pagContentsList of notationserace1 Stochastic Programming ModelsIntroduction1.2 Invento1.2.1The news vendor problem1.2.2Constraints12.3Multistage modelsMultiproduct assembl1.3.1Two-Stage Model1.3.2Chance Constrained ModeMultistage modelPortfolio selection131.4.1Static model14.2Multistage Portfolio selection14.3Decision rule211.5 Supply Chain Network Design22Exercises2 Two-Stage Problems272.1 Linear Two-Stage Problems2.1.1Basic pi272.1.2The Expected Recourse Cost for Discrete Distributions 302.1.3The Expected Recourse Cost for General Distributions.. 322.1.4Optimality Conditions垂Polyhedral Two-Stage Problems422.2.1General Properties422.2.2Expected recourse CostOptimality conditions2.3 General Two-Stage Problems82.3.1Problem Formulation, Interchangeability482.3.2Convex Two-Stage Problems2.4 Nonanticipativity2009/8/20page villContents2.4.1Scenario formulation2.4.2Dualization of Nonanticipativity Constraints2.4.3Nonanticipativity duality for general Distributions2.4.4Value of perfect infExercises3 Multistage problems3. 1 Problem Formulation633.1.1The general setting3.1The Linear case653.1.3Scenario trees3.1.4Algebraic Formulation of nonanticipativity constraints 7lDuality....763.2.1Convex multistage problems·763.2.2Optimality Conditions3.2.3Dualization of Feasibility Constraints3.2.4Dualization of nonanticipativity ConstraintsExercises4 Optimization models with Probabilistic Constraints874.1 Introduction874.2 Convexity in Probabilistic Optimization4.2Generalized Concavity of Functions and measures4.2.2Convexity of probabilistically constrained sets1064.2.3Connectedness of Probabilistically Constrained Sets... 113Separable probabilistic Constraints.1144.3Continuity and Differentiability Properties ofDistribution functions4.3.2p-Efficient Points.1154.3.3Optimality Conditions and Duality Theory1224 Optimization Problems with Nonseparable Probabilistic Constraints.. 1324.4Differentiability of Probability Functions and OptimalityConditions13344.2Approximations of Nonseparable ProbabilisticConstraints134.5 Semi-infinite Probabilistic Problems144E1505 Statistical Inference155Statistical Properties of Sample Average Approximation Estimators.. 1555.1.1Consistency of SAA estimators1575.1.2Asymptotics of the saa Optimal value1635.1.3Second order asStochastic Programs5.2 Stoch1745.2.1Consistency of solutions of the SAA GeneralizedEquatio1752009/8/20pContents5.2.2Atotics of saa generalized equations estimators 1775.3 Monte Carlo Sampling Methods180Exponential Rates of Convergence and Sample sizeEstimates in the Case of a finite Feasible se1815.3.2Sample size estimates in the General Case1855.3.3Finite Exponential Convergence1915.4 Quasi-Monte Carlo Methods1935.Variance-Reduction Techniques198Latin hmpling1985.5.2Linear Control random variables method200ng and likelihood ratio methods 205.6 Validation analysis5.6.1Estimation of the optimality g2025.6.2Statistical Testing of Optimality Conditions2075.7Constrained Probler5.7.1Monte Carlo Sampling Approach2105.7.2Validation of an Optimal solution5.8 SAA Method Applied to Multistage Stochastic Programmin205.8.1Statistical Properties of Multistage SAA Estimators22l5.8.2Complexity estimates of Multistage Programs2265.9 Stochastic Approximation Method2305.9Classical Approach5.9.2Robust sA approach..23359.3Mirror Descent sa method235.9.4Accuracy Certificates for Mirror Descent Sa Solutions.. 244Exercis6 Risk Averse Optimi2536.1 Introductio6.2 Mean-Risk models.2546.2.1Main ideas of mean -Risk analysis546.2.2Semideviation6.2.3Weighted Mean Deviations from Quantiles.2566.2.4Average value-at-Risk2576.3 Coherent risk measures2616.3.1Differentiability Properties of Risk Measures2656.3.2Examples of risk Measures..2696.3.3Law invariant risk measures and Stochastic orders2796.3.4Relation to Ambiguous Chance Constraints2856.4 Optimization of risk measures.2886.4.1Dualization of Nonanticipativity Constraints2916.4.2Examples...2956.5 Statistical Properties of Risk measures6.5.IAverage value-at-Ris6.52Absolute semideviation risk measure301Von mises statistical functionals3046.6The problem of moments306中2009/8/20page xContents6.7 Multistage Risk Averse Optimization3086.7.1Scenario tree formulation3086.7.2Conditional risk mappings3156.7.3Risk Averse multistage Stochastic Programming318Exercises3287 Background material3337.1 Optimization and Convex Analysis..334Directional Differentiability3347.1.2Elements of Convex Analysis3367.1.3Optimization and duality3397.1.4Optimality Conditions.............3467.1.5Perturbation analysis3517.1.6Epiconvergence3572 Probability3597.2.1Probability spaces and random variables7.2.2Conditional Probability and Conditional Expectation... 36372.3Measurable multifunctions and random functions3657.2.4Expectation Functions.3687.2.5Uniform Laws of Large Numbers...,,3747.2.6Law of Large Numbers for Random Sets andSubdifferentials3797.2.7Delta method7.2.8Exponential Bounds of the Large Deviations Theory3877.2.9Uniform Exponential Bounds7.3 Elements of Functional analysis3997.3Conjugate duality and differentiability.......... 4017.3.2Lattice structure4034058 Bibliographical remarks407Biibliography415Index4312009/8/20pageList of Notationsequal by definition, 333IR", n-dimensional space, 333A, transpose of matrix(vector)A, 3336I, domain of the conjugate of risk mea-C(X) space of continuous functions, 165sure p, 262CK, polar of cone C, 337Cn, the space of nonempty compact sub-C(v,R"), space of continuously differ-sets of r 379entiable mappings,176set of probability density functions,I Fr influence function. 3042L, orthogonal of (linear) space L, 41Sz, set of contact points, 3990(1), generic constant, 188b(k; a, N), cdf of binomial distribution,Op(), term, 382214S, the set of &-optimal solutions of theo, distance generating function, 236true problem, 18g(x), right-hand-side derivative, 297Va(a), Lebesgue measure of set A C RdCl(A), topological closure of set A, 334195conv(C), convex hull of set C, 337W,(U), space of Lipschitz continuousCorr(X, Y), correlation of X and Y 200functions. 166. 353CoV(X, Y, covariance of X and y, 180[a]+=max{a,0},2ga, weighted mean deviation, 256IA(, indicator function of set A, 334Sc(, support function of set C, 337n(n.f. p). space. 399A(x), set ofdist(x, A), distance from point x to set Ae multipliers vectors334348dom f, domain of function f, 333N(μ,∑), nonmal distribution,16Nc, normal cone to set C, 337dom 9, domain of multifunction 9, 365IR, set of extended real numbers. 333o(z), cdf of standard normal distribution,epif, epigraph of function f, 333IIx, metric projection onto set X, 231epiconvergence, 377convergence in distribution, 163SN, the set of optimal solutions of the0(x,h)d order tangent set 348SAA problem. 156AVOR. Average value-at-Risk. 258Sa, the set of 8-optimal solutions of thef, set of probability measures, 306SAA problem. 181ID(A, B), deviation of set A from set Bn,N, optimal value of the Saa problem,334156IDIZ], dispersion measure of random vari-N(x), sample average function, 155able 7. 2541A(, characteristic function of set A, 334吧, expectation,361int(C), interior of set C, 336TH(A, B), Hausdorff distance between setsLa」, integer part of a∈R,219A and B. 334Isc f, lower semicontinuous hull of funcN, set of positive integers, 359tion f, 3332009/8/20pageList of notationsRc, radial cone to set C, 337C, tangent cone to set C, 337V-f(r), Hessian matrix of second orderpartial derivatives, 179a. subdifferential. 338a, Clarke generalized gradient, 336as, epsilon subdifferential, 380pos w, positive hull of matrix W, 29Pr(A), probability of event A, 360ri relative interior. 337upper semideviation, 255Le, lower semideviation, 255@R. Value-at-Risk. 25Var[X], variance of X, 149, optimal value of the true problem, 1565=(51,……,5), history of the process,{a,b},186r, conjugate of function/, 338f(x, d), generalized directional deriva-g(x, h), directional derivative, 334O,(, term, 382p-efficient point, 116lid, independently identically distributed,1562009/8/20page xlllPrefaceThe main topic of this book is optimization problems involving uncertain parametersfor which stochastic models are available. Although many ways have been proposed tomodel uncertain quantities stochastic models have proved their flexibility and usefulnessin diverse areas of science. This is mainly due to solid mathematical foundations andtheoretical richness of the theory of probabilitystochastic processes, and to soundstatistical techniques of using real dataOptimization problems involving stochastic models occur in almost all areas of scienceand engineering, from telecommunication and medicine to finance This stimulates interestin rigorous ways of formulating, analyzing, and solving such problems. Due to the presenceof random parameters in the model, the theory combines concepts of the optimization theory,the theory of probability and statistics, and functional analysis. Moreover, in recent years thetheory and methods of stochastic programming have undergone major advances. all thesefactors motivated us to present in an accessible and rigorous form contemporary models andideas of stochastic programming. We hope that the book will encourage other researchersto apply stochastic programming models and to undertake further studies of this fascinatinand rapidly developing areaWe do not try to provide a comprehensive presentation of all aspects of stochasticprogramming, but we rather concentrate on theoretical foundations and recent advances inselected areas. The book is organized into seven chapters The first chapter addresses modeling issues. The basic concepts, such as recourse actions, chance(probabilistic)constraintsand the nonanticipativity principle, are introduced in the context of specific models. Thediscussion is aimed at providing motivation for the theoretical developments in the book,rather than practical recommendationsChapters 2 and 3 present detailed development of the theory of two-stage and multistage stochastic programming problems. We analyze properties of the models and developoptimality conditions and duality theory in a rather general setting. Our analysis coversgeneral distributions of uncertain parameters and provides special results for discrete distributions, which are relevant for numerical methods. Due to specific properties of two- andmultistage stochastic programming problems, we were able to derive many of these resultswithout resorting to methods of functional analvsisThe basic assumption in the modeling and technical developments is that the proba-bility distribution of the random data is not influenced by our actions(decisions). In someapplications, this assumption could be unjustified. However, dependence of probability dis-tribution on decisions typically destroys the convex structure of the optimization problemsconsidered, and our analysis exploits convexity in a significant way
- 2020-12-09下载
- 积分：1
