ECE606: Solid State Devices Lecture 16 p-n diode AC Response

Size: px
Start display at page:

Download "ECE606: Solid State Devices Lecture 16 p-n diode AC Response"

Transcription

1 ECE66: Solid State Devices Lecture 16 - diode C esose Gerhard Klimeck gekco@urdue.edu Klimeck ECE66 Fall 1 otes adoted from lam Toic Ma Equilibrium DC Small sigal Large Sigal Circuits Diode Schottky Diode i o-equilibrium (Eteral DCC voltage alied) BJT/HBT MOSFET Klimeck ECE66 Fall 1 otes adoted from lam

2 Why should we study C esose? Motivatio adio Juctio Caacitace Coductace Series esistace Diffusio Caacitace Klimeck ECE66 Fall 1 otes adoted from lam 3 Outlie 1) Coductace ad series resistace ) Majority carrier juctio caacitace 3) Miority carrier diffusio caacitace 4) Coclusio ef. SDF, Chater 7 Klimeck ECE66 Fall 1 otes adoted from lam 4

3 Forward Bias Coductace o q( S I ) β / m ( 1) I = I e l(i) 1 6,7 3 I Io β l = q( S I ) I m m = G (), diff (1), mbiolar () C J G 5 S 4 m d qβ I I di ( ) o = S 1 g C diff = m qβ ( I I ) S FB Forward Bias Coductace Klimeck ECE66 Fall 1 otes adoted from lam 5 everse Bias Coductace o q( S I ) β / m i ( 1) q B bi q i B bi G I = I e I 1 qi B = g B bi C J 5 4 l(i) 1 6,7 S 3 everse Bias Coductace C diff Klimeck ECE66 Fall 1 otes adoted from lam 6

4 Outlie 1) Coductace ad series resistace ) Majority carrier juctio caacitace 3) Miority carrier diffusio caacitace 4) Coclusio Klimeck ECE66 Fall 1 otes adoted from lam 7 Juctio Caacitace Forward biased diode C sigal Coductace DC F F Juctio Caacitace Series esistace Diffusio Caacitace Deletio width modulatio > Charge modulatio Majority carrier effect < Klimeck ECE66 Fall 1 otes adoted from lam 8

5 Majority Carrier Juctio Caacitace ρ < Juctio Caacitace Coductace Series esistace ρ > Diffusio Caacitace Measure Majority Carrier Juctio Caacitace Ksε Ksε CJ = = W W Ksε Ksε ( bi ) qd q Cj a Klimeck ECE66 Fall 1 otes adoted from lam 9 Measuremet of Built-i Potetial 1 C q ( ) K ε J D s ( ) bi (ssume sigle sided - juctio) measure C J Derive bi from C J measuremets lot C J - bi Klimeck ECE66 Fall 1 otes adoted from lam 1

6 .. d ariable Doig 1 C q ( ) K ε J D s ( bi ) measure C J D ( ) = 1 qksε d(1 CJ ) Measure doig cocetratio as a fuctio of ositio d Charge lot C J - < = > Klimeck ECE66 Fall 1 otes adoted from lam 11 Dielectric elaatio Time (majority side) Majority side J = qµ E qd eglect d 1 dj = G dt q d ( ) 1 ( µ E) DC d d q de = = Dµ dt q d d How log does it take for the sigal to cross the juctio? Ksε =.1 s d σ ery fast -side de q = d k ε ( ) S ( D ) d q Dµ σ = = dt k ε k ε S S σ t k ε t S d ( t) = e = e Klimeck ECE66 Fall 1 otes adoted from lam 1

7 Outlie 1) Coductace ad series resistace ) Majority carrier juctio caacitace 3) Miority carrier diffusio caacitace 4) Coclusio Klimeck ECE66 Fall 1 otes adoted from lam 13 Diffusio Caacitace for Miority Carriers Miority Carrier side J d = qµ E qd d C C 1 dj = r q d g DC DC C jωt jωt jωt dc ac dc ac dc ac = D d ( ) ( ) e d e e Klimeck ECE66 Fall 1 otes adoted from lam 14

8 Diffusio Caacitace for Miority Carriers jωt jωt ( dc ace ) d ( dc ac e ) dc e = D d d d jω = D d d j t dc j t ac dc j t ac ace ω e ω e ω jωt ac d dc dc L L D C : = D = e Be d dc d ac ac ( jω ) d L * L * L * C : = D 1 = Ce De Ce ac ( ) ( ) L = D / 1 jω = / 1 jω * * Klimeck ECE66 Fall 1 otes adoted from lam 15 C Boudary Coditios qdc i kt dc ( = ) = e 1 jωt dc ace q i kt = e 1 jωt ace jωt ( dc ace ) jωt qdc qace i kt kt e e 1 jωt ( dc ace ) Taylor easio q dc jωt i q kt ace e 1 1 kt q q dc ac i kt ac ( = ) = e = C kt Klimeck ECE66 Fall 1 otes adoted from lam 16

9 C Curret ad Imedace q q dc ac i kt ac ( = ) = e kt = C L * L * L * ( ) = Ce De Ce ac Fially d qd q Jac = qd = e d L kt q dc ac ac i kt * = C Curret J q D Yac = = e G qdc ac i kt * 1 ac L kt jω C Imedace Klimeck ECE66 Fall 1 otes adoted from lam 17 Diffusio Coductace ad Caacitace GD ω Y G jωc G jω ac = D D 1 Searate i real & imagiary arts G D G 1/ = 1 ω 1 ωc D G 1/ = 1 ω 1 Product of G D ad C D frequecy-ideedet CD 1/ ω Klimeck ECE66 Fall 1 otes adoted from lam 18

10 Small Sigal -diode Coclusio 1) Small sigal resose relevat for may aalog alicatios. ) Small sigal arameters always refer to the DC oeratig coditios, as such the arameter chages with bias coditio. 3) Imortat to distiguish betwee majority ad miority carrier caacitace. Their relative imortace deeds o secific alicatios. Klimeck ECE66 Fall 1 otes adoted from lam 19 ECE66: Solid State Devices - diode Large Sigal esose Gerhard Klimeck gekco@urdue.edu Klimeck ECE66 Fall 1 otes adoted from lam

11 Equilibrium DC Small sigal Digital Sigals: switch o ad off Large Sigal Toic Ma Circuits Diode Schottky BJT/HBT MOSFET Klimeck ECE66 Fall 1 otes adoted from lam 1 Outlie 1) Large sigal resose ad charge cotrol model ) Tur-off characteristics 3) Tur-o characteristics 4) Other alicatios 5) Coclusio ef. SDF, Chater 8 Klimeck ECE66 Fall 1 otes adoted from lam

12 Digital, Large Sigal licatios :If trasitio is slow, every oit is i quasi-equlibrium treat them like DC 1 :1 :- ---:If trasitio is very fast - to 1 l(i) 1 1 to Klimeck ECE66 Fall 1 otes adoted from lam 3 Closer Look to Fast Trasitio (t) Before trasitio occur costat voltage, curret chage from I F to I t costat curret, voltage chage form 1 to l(i) 1 :1 :- i(t) I F 1 to -.1I - 1 -I t r t s t rr Klimeck ECE66 Fall 1 otes adoted from lam 4

13 Defiitios (t) i(t) -.1I I F t t s Charge storage time t r ecovery time t rr.. everse recovery time -I t r t s t rr Klimeck ECE66 Fall 1 otes adoted from lam 5 Cotiuity Equatios Full aalytical solutio imossible for large sigal. ( D ) D = q 1 = J r g q J 1 = J r g q J = qµ E qd P P P = qµ E qd P P P Klimeck ECE66 Fall 1 otes adoted from lam = i, diff = i, diff Charge cotrol equatios: roimatio whe you have large trasiet resose 6

14 How Does Curret Fli Without oltage Fliig? Where did the charge go? 1. Back to the left-had side. ecombie with the tra ad the majority carrier (,t) forward bias, miority carrier has bee ijected ecombiatio (,t) eoetially dyig out d d (,t) d > d < DC Whe voltage start to chage, (,t) suddely beded dowwards, d/d chage sig ad curret fli from to Klimeck ECE66 Fall 1 otes adoted from lam 7 Large Sigal Charge Cotrol Model 1 dj = r q d g miority carrier d J = qµ E qd d ( ) ( ) d = D t d Klimeck ECE66 Fall 1 otes adoted from lam 8

15 Large Sigal Charge Cotrol Model ( ) ( ) d = D t d (q), itegrate W ( ) d d ( q ) W W W d d (,t) q q d = D d d rea uder the give time Curret goig out ( ) d ( q ) d q = D D d d = W = = idiff Curret comig i ecombiatio W ( ) q d (Total charge that is buildig i)= (et electros flowig i) (recombiatio) Klimeck ECE66 Fall 1 otes adoted from lam 9 Outlie 1) Large sigal resose ad charge cotrol model ) Tur-off characteristics 3) Tur-o characteristics 4) Other alicatios 5) Coclusio Klimeck ECE66 Fall 1 otes adoted from lam 3

16 Tur-off Characteristics: Determie (t s ) IF S 1 F j - P I = idiff i(t) -.1I I F ( ) t < = I F = (Time Ideedet) -I t r Why does the curret remai costat eve with t>? Klimeck ECE66 Fall 1 otes adoted from lam t s t rr 31 Boudary Coditio IF S 1 F j - P I = idiff ( ) t < = I F = ( ) = I = ( ) F ote. For a caacitor, voltage ca ot chage istatly. So charge ca ot chage istatly. Klimeck ECE66 Fall 1 otes adoted from lam 3

17 Tur-off Curret Trasiet IF S F j - P I Sice j ca t be larger tha the bad ga, which is smaller tha, the diode will be forced to suly the egative curret I Klimeck ECE66 Fall 1 otes adoted from lam 33 Tur-off Curret Trasiet IF S (,t) See how the sloe chage F P j - I = idiff roimately costat! t > = I t s I = l I ( ) / ( t ) / s ( ts ) d = I ts ( ) dt Klimeck ECE66 Fall 1 otes adoted from lam 34

18 Storage Time S F IF j - P I to 1 l(i) t I ( ) / I I = l = l I ( t ) / I F s s 1 to (,t) (t s ) Shorte it for fast resose! i Klimeck ECE66 Fall 1 otes adoted from lam 35 Tur-off oltage Trasiet S IF F j - P I a (t) ts t kt (, t) υ ( t) = l q o t / ( ) ( t) = l I ( I I ) e F llows easy calculatio of (,t). Klimeck ECE66 Fall 1 otes adoted from lam (,t) 36

19 ecovery Time erf tr tr e I = 1.1 t I r π F to 1 l(i) 1 to Useful formula erf ( ) = 1 e t r Klimeck ECE66 Fall 1 otes adoted from lam 37 ecovery Time l(i) ef. Sze/g,. 117 to 1 1 to t r t s W I D I F trr = tr t f I I F ( W L ) ( W L ) Klimeck ECE66 Fall 1 otes adoted from lam 38

20 Outlie 1) Large sigal resose ad charge cotrol model ) Tur-off characteristics 3) Tur-o characteristics 4) Other alicatios 5) Coclusio Klimeck ECE66 Fall 1 otes adoted from lam 39 Tur-o Characteristics: Boudary Coditio IF S 1 F j - P I = idiff ( t = ) t = I F ( t = ) = I F Klimeck ECE66 Fall 1 otes adoted from lam 4

21 Tur-o Characteristics S F IF j - P I (,t) time = idiff t > = I F t t ( ) ( ) 1 t = t e = IF 1 e Check: (t=)= (t )=I F Klimeck ECE66 Fall 1 otes adoted from lam 41 Outlie 1) large sigal resose ad charge cotrol model ) tur-off characteristics 3) tur-o characteristics 4) other alicatios 5) coclusio Klimeck ECE66 Fall 1 otes adoted from lam 4

22 Diffusio Time Electros get scattered to the other side, the average time is = idiff ( ) ( t = ) = i diff diff (,t) ( ) recombiatio (, t) = C D' o recombiatio diff () q W ( ) = = i () diff qd W W = D Klimeck ECE66 Fall 1 otes adoted from lam 1 W ~ ( D / W ) Oly half of them goes to the right W Diffusio velocity 43 Two lie derivatio of Steady State Diode Curret = idiff (,t) i diff = diff diff 1 qβ ( 1) i q e W D i = diff W W D q ( β 1) idiff = = q e Eact Eressio! Klimeck ECE66 Fall 1 otes adoted from lam 44

23 Large Sigal -diode Coclusio Large sigal resose of devices of great imortace for digital alicatios. alytical solutio of artial differetial equatio ofte difficult (if ot imossible), therefore aroimate methods like Charge-cotrol aroimatio ofte hel simlify the solutio ad still rovide a great deal of isight ito the dyamics of switchig oeratio. Be careful i usig the boudary coditio which is ofte dictated by eteral circuit coditios. Klimeck ECE66 Fall 1 otes adoted from lam 45

Doped semiconductors: donor impurities

Doped semiconductors: donor impurities Doed semicoductors: door imurities A silico lattice with a sigle imurity atom (Phoshorus, P) added. As comared to Si, the Phoshorus has oe extra valece electro which, after all bods are made, has very

More information

Semiconductor Devices

Semiconductor Devices emicoductor evices Prof. Zbigiew Lisik epartmet of emicoductor ad Optoelectroics evices room: 116 e-mail: zbigiew.lisik@p.lodz.pl Uipolar devices IFE T&C JFET Trasistor Uipolar evices - Trasistors asic

More information

ECE606: Solid State Devices Lecture 18. Bipolar Transistors a) Introduction b) Design (I)

ECE606: Solid State Devices Lecture 18. Bipolar Transistors a) Introduction b) Design (I) 606: Solid State Devices Lecture 18 ipolar Trasistors a) Itroductio b) Desig (I) Gerhard Klimeck gekco@purdue.edu 1 ackgroud ase! Poit cotact Germaium trasistor Ralph ray from Purdue missed the ivetio

More information

3 Energy. 3.3. Non-Flow Energy Equation (NFEE) Internal Energy. MECH 225 Engineering Science 2

3 Energy. 3.3. Non-Flow Energy Equation (NFEE) Internal Energy. MECH 225 Engineering Science 2 MECH 5 Egieerig Sciece 3 Eergy 3.3. No-Flow Eergy Equatio (NFEE) You may have oticed that the term system kees croig u. It is ecessary, therefore, that before we start ay aalysis we defie the system that

More information

4.1.4 Electrical Characterisation of MOVPE Grown n- and pn-gaas Nanowires

4.1.4 Electrical Characterisation of MOVPE Grown n- and pn-gaas Nanowires 3 Bi-Aual Reort 28/29 - Solid-State Electroics Deartmet 4.1.4 Electrical Characterisatio of MOVPE Grow - ad -GaAs Naowires Scietist: C. Gutsche, I. Regoli, A. Lysov Itroductio Recetly, we reseted a cotrolled

More information

Heat (or Diffusion) equation in 1D*

Heat (or Diffusion) equation in 1D* Heat (or Diffusio) equatio i D* Derivatio of the D heat equatio Separatio of variables (refresher) Worked eamples *Kreysig, 8 th Ed, Sectios.4b Physical assumptios We cosider temperature i a log thi wire

More information

Building Blocks Problem Related to Harmonic Series

Building Blocks Problem Related to Harmonic Series TMME, vol3, o, p.76 Buildig Blocks Problem Related to Harmoic Series Yutaka Nishiyama Osaka Uiversity of Ecoomics, Japa Abstract: I this discussio I give a eplaatio of the divergece ad covergece of ifiite

More information

pn-junctions Semiconductor Metal Oxide Diode - The Simplest IC-device Advanced Digital IC-Design What is a MOS-transistor?

pn-junctions Semiconductor Metal Oxide Diode - The Simplest IC-device Advanced Digital IC-Design What is a MOS-transistor? Advaced Digital IC-Desig his lecture will he MOS-trasistor Refresh the MOS-trasistor fuctio ad models Especially, short chael effects Digital IC-Desig he Diode i a IC-device he Diode Diodes appears i all

More information

Normal Distribution.

Normal Distribution. Normal Distributio www.icrf.l Normal distributio I probability theory, the ormal or Gaussia distributio, is a cotiuous probability distributio that is ofte used as a first approimatio to describe realvalued

More information

Soving Recurrence Relations

Soving Recurrence Relations Sovig Recurrece Relatios Part 1. Homogeeous liear 2d degree relatios with costat coefficiets. Cosider the recurrece relatio ( ) T () + at ( 1) + bt ( 2) = 0 This is called a homogeeous liear 2d degree

More information

Math 113 HW #11 Solutions

Math 113 HW #11 Solutions Math 3 HW # Solutios 5. 4. (a) Estimate the area uder the graph of f(x) = x from x = to x = 4 usig four approximatig rectagles ad right edpoits. Sketch the graph ad the rectagles. Is your estimate a uderestimate

More information

CV-measurement basics

CV-measurement basics V-measuremet basics M-capacitor V measuremet measuremet of differetial by small ac modulatio of the bias voltage (dc d d G G M j G M ω M j ω Y M G M G M M ualitative poit of view ad diagram i geeral (here

More information

Incremental calculation of weighted mean and variance

Incremental calculation of weighted mean and variance Icremetal calculatio of weighted mea ad variace Toy Fich faf@cam.ac.uk dot@dotat.at Uiversity of Cambridge Computig Service February 009 Abstract I these otes I eplai how to derive formulae for umerically

More information

Current Carrying Capacity Analysis of TR42.7 update to IEEE802.3at

Current Carrying Capacity Analysis of TR42.7 update to IEEE802.3at IEEE80.3at Task Force Curret Carryig Capacity Aalysis of TR4.7 update to IEEE80.3at Ja 007, Moterey CA Yair Darsha Micro semi/powerdsie Curret Carryig Capacity Aalysis of TR4.7 update to IEEE80.3at Ja

More information

5: Introduction to Estimation

5: Introduction to Estimation 5: Itroductio to Estimatio Cotets Acroyms ad symbols... 1 Statistical iferece... Estimatig µ with cofidece... 3 Samplig distributio of the mea... 3 Cofidece Iterval for μ whe σ is kow before had... 4 Sample

More information

Drift-Diffusion Model: Introduction

Drift-Diffusion Model: Introduction Drift-Diffusio Model: Itroductio Preared by: Dragica Vasileska Associate Professor Arizoa State Uiversity The oular drift-diffusio curret equatios ca be easily derived from the oltzma trasort equatio by

More information

Convexity, Inequalities, and Norms

Convexity, Inequalities, and Norms Covexity, Iequalities, ad Norms Covex Fuctios You are probably familiar with the otio of cocavity of fuctios. Give a twicedifferetiable fuctio ϕ: R R, We say that ϕ is covex (or cocave up) if ϕ (x) 0 for

More information

Case Study. Normal and t Distributions. Density Plot. Normal Distributions

Case Study. Normal and t Distributions. Density Plot. Normal Distributions Case Study Normal ad t Distributios Bret Halo ad Bret Larget Departmet of Statistics Uiversity of Wiscosi Madiso October 11 13, 2011 Case Study Body temperature varies withi idividuals over time (it ca

More information

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations CS3A Hadout 3 Witer 00 February, 00 Solvig Recurrece Relatios Itroductio A wide variety of recurrece problems occur i models. Some of these recurrece relatios ca be solved usig iteratio or some other ad

More information

Cantilever Beam Experiment

Cantilever Beam Experiment Mechaical Egieerig Departmet Uiversity of Massachusetts Lowell Catilever Beam Experimet Backgroud A disk drive maufacturer is redesigig several disk drive armature mechaisms. This is the result of evaluatio

More information

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx SAMPLE QUESTIONS FOR FINAL EXAM REAL ANALYSIS I FALL 006 3 4 Fid the followig usig the defiitio of the Riema itegral: a 0 x + dx 3 Cosider the partitio P x 0 3, x 3 +, x 3 +,......, x 3 3 + 3 of the iterval

More information

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method Chapter 6: Variace, the law of large umbers ad the Mote-Carlo method Expected value, variace, ad Chebyshev iequality. If X is a radom variable recall that the expected value of X, E[X] is the average value

More information

Unit 8: Inference for Proportions. Chapters 8 & 9 in IPS

Unit 8: Inference for Proportions. Chapters 8 & 9 in IPS Uit 8: Iferece for Proortios Chaters 8 & 9 i IPS Lecture Outlie Iferece for a Proortio (oe samle) Iferece for Two Proortios (two samles) Cotigecy Tables ad the χ test Iferece for Proortios IPS, Chater

More information

Find the inverse Laplace transform of the function F (p) = Evaluating the residues at the four simple poles, we find. residue at z = 1 is 4te t

Find the inverse Laplace transform of the function F (p) = Evaluating the residues at the four simple poles, we find. residue at z = 1 is 4te t Homework Solutios. Chater, Sectio 7, Problem 56. Fid the iverse Lalace trasform of the fuctio F () (7.6). À Chater, Sectio 7, Problem 6. Fid the iverse Lalace trasform of the fuctio F () usig (7.6). Solutio:

More information

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here).

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here). BEGINNING ALGEBRA Roots ad Radicals (revised summer, 00 Olso) Packet to Supplemet the Curret Textbook - Part Review of Square Roots & Irratioals (This portio ca be ay time before Part ad should mostly

More information

Properties of MLE: consistency, asymptotic normality. Fisher information.

Properties of MLE: consistency, asymptotic normality. Fisher information. Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout

More information

Research Article Sign Data Derivative Recovery

Research Article Sign Data Derivative Recovery Iteratioal Scholarly Research Network ISRN Applied Mathematics Volume 0, Article ID 63070, 7 pages doi:0.540/0/63070 Research Article Sig Data Derivative Recovery L. M. Housto, G. A. Glass, ad A. D. Dymikov

More information

Measuring Magneto Energy Output and Inductance Revision 1

Measuring Magneto Energy Output and Inductance Revision 1 Measurig Mageto Eergy Output ad Iductace evisio Itroductio A mageto is fudametally a iductor that is mechaically charged with a iitial curret value. That iitial curret is produced by movemet of the rotor

More information

CHAPTER 3 THE TIME VALUE OF MONEY

CHAPTER 3 THE TIME VALUE OF MONEY CHAPTER 3 THE TIME VALUE OF MONEY OVERVIEW A dollar i the had today is worth more tha a dollar to be received i the future because, if you had it ow, you could ivest that dollar ad ear iterest. Of all

More information

Terminology for Bonds and Loans

Terminology for Bonds and Loans ³ ² ± Termiology for Bods ad Loas Pricipal give to borrower whe loa is made Simple loa: pricipal plus iterest repaid at oe date Fixed-paymet loa: series of (ofte equal) repaymets Bod is issued at some

More information

Study on the application of the software phase-locked loop in tracking and filtering of pulse signal

Study on the application of the software phase-locked loop in tracking and filtering of pulse signal Advaced Sciece ad Techology Letters, pp.31-35 http://dx.doi.org/10.14257/astl.2014.78.06 Study o the applicatio of the software phase-locked loop i trackig ad filterig of pulse sigal Sog Wei Xia 1 (College

More information

HEXFET MOSFET TECHNOLOGY. n n n n n n

HEXFET MOSFET TECHNOLOGY. n n n n n n PD - 90495G POWER MOSFET THRU-HOLE (TO-254) Product Summary Part Number RDS(o) ID IRFM9140 0.20Ω -18 IRFM9140 JNTX2N7236 JNTXV2N7236 JNS2N7236 REF:MIL-PRF-19500/595 100V, P-CHNNEL HEXFET MOSFET TECHNOLOGY

More information

Confidence Intervals for One Mean

Confidence Intervals for One Mean Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a

More information

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling Taig DCOP to the Real World: Efficiet Complete Solutios for Distributed Multi-Evet Schedulig Rajiv T. Maheswara, Milid Tambe, Emma Bowrig, Joatha P. Pearce, ad Pradeep araatham Uiversity of Souther Califoria

More information

RF Engineering Continuing Education Introduction to Traffic Planning

RF Engineering Continuing Education Introduction to Traffic Planning RF Egieerig otiuig Educatio Itroductio to Traffic Plaig Queuig Systems Figure. shows a schematic reresetatio of a queuig system. This reresetatio is a mathematical abstractio suitable for may differet

More information

I. Chi-squared Distributions

I. Chi-squared Distributions 1 M 358K Supplemet to Chapter 23: CHI-SQUARED DISTRIBUTIONS, T-DISTRIBUTIONS, AND DEGREES OF FREEDOM To uderstad t-distributios, we first eed to look at aother family of distributios, the chi-squared distributios.

More information

Enhancing Oracle Business Intelligence with cubus EV How users of Oracle BI on Essbase cubes can benefit from cubus outperform EV Analytics (cubus EV)

Enhancing Oracle Business Intelligence with cubus EV How users of Oracle BI on Essbase cubes can benefit from cubus outperform EV Analytics (cubus EV) Ehacig Oracle Busiess Itelligece with cubus EV How users of Oracle BI o Essbase cubes ca beefit from cubus outperform EV Aalytics (cubus EV) CONTENT 01 cubus EV as a ehacemet to Oracle BI o Essbase 02

More information

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 9 Spring 2006

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 9 Spring 2006 Exam format UC Bereley Departmet of Electrical Egieerig ad Computer Sciece EE 6: Probablity ad Radom Processes Solutios 9 Sprig 006 The secod midterm will be held o Wedesday May 7; CHECK the fial exam

More information

Queuing Systems: Lecture 1. Amedeo R. Odoni October 10, 2001

Queuing Systems: Lecture 1. Amedeo R. Odoni October 10, 2001 Queuig Systems: Lecture Amedeo R. Odoi October, 2 Topics i Queuig Theory 9. Itroductio to Queues; Little s Law; M/M/. Markovia Birth-ad-Death Queues. The M/G/ Queue ad Extesios 2. riority Queues; State

More information

Chapter 5 Unit 1. IET 350 Engineering Economics. Learning Objectives Chapter 5. Learning Objectives Unit 1. Annual Amount and Gradient Functions

Chapter 5 Unit 1. IET 350 Engineering Economics. Learning Objectives Chapter 5. Learning Objectives Unit 1. Annual Amount and Gradient Functions Chapter 5 Uit Aual Amout ad Gradiet Fuctios IET 350 Egieerig Ecoomics Learig Objectives Chapter 5 Upo completio of this chapter you should uderstad: Calculatig future values from aual amouts. Calculatig

More information

CD4016BMS. CMOS Quad Bilateral Switch. Applications. Features. Description. Functional Diagram. Pinout. November 1994

CD4016BMS. CMOS Quad Bilateral Switch. Applications. Features. Description. Functional Diagram. Pinout. November 1994 SEMICONDUCTOR CD16BMS November 199 Features Trasmissio or Multilexig of Aalog or Digital Sigals High Voltage Tye (V Ratig) V Digital or ±1V Peak-to-Peak Switchig 8Ω Tyical O-State Resistace for 1V Oeratio

More information

Solutions to Selected Problems In: Pattern Classification by Duda, Hart, Stork

Solutions to Selected Problems In: Pattern Classification by Duda, Hart, Stork Solutios to Selected Problems I: Patter Classificatio by Duda, Hart, Stork Joh L. Weatherwax February 4, 008 Problem Solutios Chapter Bayesia Decisio Theory Problem radomized rules Part a: Let Rx be the

More information

2-3 The Remainder and Factor Theorems

2-3 The Remainder and Factor Theorems - The Remaider ad Factor Theorems Factor each polyomial completely usig the give factor ad log divisio 1 x + x x 60; x + So, x + x x 60 = (x + )(x x 15) Factorig the quadratic expressio yields x + x x

More information

Section 11.3: The Integral Test

Section 11.3: The Integral Test Sectio.3: The Itegral Test Most of the series we have looked at have either diverged or have coverged ad we have bee able to fid what they coverge to. I geeral however, the problem is much more difficult

More information

Buck Converter Losses Under the Microscope

Buck Converter Losses Under the Microscope Buck Coverter Losses Uder the Microscope sychroous stepdow coverters, the parasitic iductaces associated with pcboard traces ad device packagig affect power losses, while limitig MOSFET switchig speeds.

More information

CHAPTER 3 DIGITAL CODING OF SIGNALS

CHAPTER 3 DIGITAL CODING OF SIGNALS CHAPTER 3 DIGITAL CODING OF SIGNALS Computers are ofte used to automate the recordig of measuremets. The trasducers ad sigal coditioig circuits produce a voltage sigal that is proportioal to a quatity

More information

http://www.webassign.net/v4cgijeff.downs@wnc/control.pl

http://www.webassign.net/v4cgijeff.downs@wnc/control.pl Assigmet Previewer http://www.webassig.et/vcgijeff.dows@wc/cotrol.pl of // : PM Practice Eam () Questio Descriptio Eam over chapter.. Questio DetailsLarCalc... [] Fid the geeral solutio of the differetial

More information

Time Value of Money, NPV and IRR equation solving with the TI-86

Time Value of Money, NPV and IRR equation solving with the TI-86 Time Value of Moey NPV ad IRR Equatio Solvig with the TI-86 (may work with TI-85) (similar process works with TI-83, TI-83 Plus ad may work with TI-82) Time Value of Moey, NPV ad IRR equatio solvig with

More information

AP Calculus AB 2006 Scoring Guidelines Form B

AP Calculus AB 2006 Scoring Guidelines Form B AP Calculus AB 6 Scorig Guidelies Form B The College Board: Coectig Studets to College Success The College Board is a ot-for-profit membership associatio whose missio is to coect studets to college success

More information

Chapter 7 Methods of Finding Estimators

Chapter 7 Methods of Finding Estimators Chapter 7 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 011 Chapter 7 Methods of Fidig Estimators Sectio 7.1 Itroductio Defiitio 7.1.1 A poit estimator is ay fuctio W( X) W( X1, X,, X ) of

More information

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return EVALUATING ALTERNATIVE CAPITAL INVESTMENT PROGRAMS By Ke D. Duft, Extesio Ecoomist I the March 98 issue of this publicatio we reviewed the procedure by which a capital ivestmet project was assessed. The

More information

Solving Logarithms and Exponential Equations

Solving Logarithms and Exponential Equations Solvig Logarithms ad Epoetial Equatios Logarithmic Equatios There are two major ideas required whe solvig Logarithmic Equatios. The first is the Defiitio of a Logarithm. You may recall from a earlier topic:

More information

3. Greatest Common Divisor - Least Common Multiple

3. Greatest Common Divisor - Least Common Multiple 3 Greatest Commo Divisor - Least Commo Multiple Defiitio 31: The greatest commo divisor of two atural umbers a ad b is the largest atural umber c which divides both a ad b We deote the greatest commo gcd

More information

BENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets

BENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets BENEIT-CST ANALYSIS iacial ad Ecoomic Appraisal usig Spreadsheets Ch. 2: Ivestmet Appraisal - Priciples Harry Campbell & Richard Brow School of Ecoomics The Uiversity of Queeslad Review of basic cocepts

More information

Guidelines for a Good Presentation. Luis M. Correia

Guidelines for a Good Presentation. Luis M. Correia Guidelies for a Good Presetatio Luis M. Correia Outlie Basic riciles. Structure. Sizes ad cotrast. Style. Examles. Coclusios. Basic Priciles The resetatio of a work is iteded to show oly its major asects,

More information

GCSE STATISTICS. 4) How to calculate the range: The difference between the biggest number and the smallest number.

GCSE STATISTICS. 4) How to calculate the range: The difference between the biggest number and the smallest number. GCSE STATISTICS You should kow: 1) How to draw a frequecy diagram: e.g. NUMBER TALLY FREQUENCY 1 3 5 ) How to draw a bar chart, a pictogram, ad a pie chart. 3) How to use averages: a) Mea - add up all

More information

INVESTMENT PERFORMANCE COUNCIL (IPC)

INVESTMENT PERFORMANCE COUNCIL (IPC) INVESTMENT PEFOMANCE COUNCIL (IPC) INVITATION TO COMMENT: Global Ivestmet Performace Stadards (GIPS ) Guidace Statemet o Calculatio Methodology The Associatio for Ivestmet Maagemet ad esearch (AIM) seeks

More information

Bond Valuation I. What is a bond? Cash Flows of A Typical Bond. Bond Valuation. Coupon Rate and Current Yield. Cash Flows of A Typical Bond

Bond Valuation I. What is a bond? Cash Flows of A Typical Bond. Bond Valuation. Coupon Rate and Current Yield. Cash Flows of A Typical Bond What is a bod? Bod Valuatio I Bod is a I.O.U. Bod is a borrowig agreemet Bod issuers borrow moey from bod holders Bod is a fixed-icome security that typically pays periodic coupo paymets, ad a pricipal

More information

INVESTMENT PERFORMANCE COUNCIL (IPC) Guidance Statement on Calculation Methodology

INVESTMENT PERFORMANCE COUNCIL (IPC) Guidance Statement on Calculation Methodology Adoptio Date: 4 March 2004 Effective Date: 1 Jue 2004 Retroactive Applicatio: No Public Commet Period: Aug Nov 2002 INVESTMENT PERFORMANCE COUNCIL (IPC) Preface Guidace Statemet o Calculatio Methodology

More information

PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY AN ALTERNATIVE MODEL FOR BONUS-MALUS SYSTEM

PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY AN ALTERNATIVE MODEL FOR BONUS-MALUS SYSTEM PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY Physical ad Mathematical Scieces 2015, 1, p. 15 19 M a t h e m a t i c s AN ALTERNATIVE MODEL FOR BONUS-MALUS SYSTEM A. G. GULYAN Chair of Actuarial Mathematics

More information

Z-TEST / Z-STATISTIC: used to test hypotheses about. µ when the population standard deviation is unknown

Z-TEST / Z-STATISTIC: used to test hypotheses about. µ when the population standard deviation is unknown Z-TEST / Z-STATISTIC: used to test hypotheses about µ whe the populatio stadard deviatio is kow ad populatio distributio is ormal or sample size is large T-TEST / T-STATISTIC: used to test hypotheses about

More information

Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem

Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem Lecture 4: Cauchy sequeces, Bolzao-Weierstrass, ad the Squeeze theorem The purpose of this lecture is more modest tha the previous oes. It is to state certai coditios uder which we are guarateed that limits

More information

This document contains a collection of formulas and constants useful for SPC chart construction. It assumes you are already familiar with SPC.

This document contains a collection of formulas and constants useful for SPC chart construction. It assumes you are already familiar with SPC. SPC Formulas ad Tables 1 This documet cotais a collectio of formulas ad costats useful for SPC chart costructio. It assumes you are already familiar with SPC. Termiology Geerally, a bar draw over a symbol

More information

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008 I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces

More information

Traffic Modeling and Prediction using ARIMA/GARCH model

Traffic Modeling and Prediction using ARIMA/GARCH model Traffic Modelig ad Predictio usig ARIMA/GARCH model Preseted by Zhili Su, Bo Zhou, Uiversity of Surrey, UK COST 285 Symposium 8-9 September 2005 Muich, Germay Outlie Motivatio ARIMA/GARCH model Parameter

More information

Approximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find

Approximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find 1.8 Approximatig Area uder a curve with rectagles 1.6 To fid the area uder a curve we approximate the area usig rectagles ad the use limits to fid 1.4 the area. Example 1 Suppose we wat to estimate 1.

More information

Lesson 17 Pearson s Correlation Coefficient

Lesson 17 Pearson s Correlation Coefficient Outlie Measures of Relatioships Pearso s Correlatio Coefficiet (r) -types of data -scatter plots -measure of directio -measure of stregth Computatio -covariatio of X ad Y -uique variatio i X ad Y -measurig

More information

I. Why is there a time value to money (TVM)?

I. Why is there a time value to money (TVM)? Itroductio to the Time Value of Moey Lecture Outlie I. Why is there the cocept of time value? II. Sigle cash flows over multiple periods III. Groups of cash flows IV. Warigs o doig time value calculatios

More information

CS103X: Discrete Structures Homework 4 Solutions

CS103X: Discrete Structures Homework 4 Solutions CS103X: Discrete Structures Homewor 4 Solutios Due February 22, 2008 Exercise 1 10 poits. Silico Valley questios: a How may possible six-figure salaries i whole dollar amouts are there that cotai at least

More information

MOSFET Small Signal Model and Analysis

MOSFET Small Signal Model and Analysis Just as we did with the BJT, we ca csider the MOSFET amplifier aalysis i tw parts: Fid the DC peratig pit The determie the amplifier utput parameters fr ery small iput sigals. + V 1 - MOSFET Small Sigal

More information

Lesson 15 ANOVA (analysis of variance)

Lesson 15 ANOVA (analysis of variance) Outlie Variability -betwee group variability -withi group variability -total variability -F-ratio Computatio -sums of squares (betwee/withi/total -degrees of freedom (betwee/withi/total -mea square (betwee/withi

More information

Modelling of the migration effect occurring ISFET-based coulometric sensor-actuator system

Modelling of the migration effect occurring ISFET-based coulometric sensor-actuator system Aalytica Chimica Acta, 237 (1990) 71-81 Elsevier Sciece Publishers B.V., Amsterdam 71 at a Modellig of the migratio effect occurrig ISFET-based coulometric sesor-actuator system J. Luo *, W. Olthuis, B.H.

More information

Infinite Sequences and Series

Infinite Sequences and Series CHAPTER 4 Ifiite Sequeces ad Series 4.1. Sequeces A sequece is a ifiite ordered list of umbers, for example the sequece of odd positive itegers: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29...

More information

AP Calculus BC 2003 Scoring Guidelines Form B

AP Calculus BC 2003 Scoring Guidelines Form B AP Calculus BC Scorig Guidelies Form B The materials icluded i these files are iteded for use by AP teachers for course ad exam preparatio; permissio for ay other use must be sought from the Advaced Placemet

More information

Stock Price Pinning near Option Expiration Dates

Stock Price Pinning near Option Expiration Dates Stock Price Piig ear Optio Expiratio Dates Marco Avellaeda, New York Uiversity Geady Kasya, New York Uiversity Michael D. Lipki, Katama Tradig & Columbia Uiversity George Papaicolaou Coferece Paris, December

More information

Elementary Theory of Russian Roulette

Elementary Theory of Russian Roulette Elemetary Theory of Russia Roulette -iterestig patters of fractios- Satoshi Hashiba Daisuke Miematsu Ryohei Miyadera Itroductio. Today we are goig to study mathematical theory of Russia roulette. If some

More information

Output Analysis (2, Chapters 10 &11 Law)

Output Analysis (2, Chapters 10 &11 Law) B. Maddah ENMG 6 Simulatio 05/0/07 Output Aalysis (, Chapters 10 &11 Law) Comparig alterative system cofiguratio Sice the output of a simulatio is radom, the comparig differet systems via simulatio should

More information

THE ABRACADABRA PROBLEM

THE ABRACADABRA PROBLEM THE ABRACADABRA PROBLEM FRANCESCO CARAVENNA Abstract. We preset a detailed solutio of Exercise E0.6 i [Wil9]: i a radom sequece of letters, draw idepedetly ad uiformly from the Eglish alphabet, the expected

More information

Sequences and Series

Sequences and Series CHAPTER 9 Sequeces ad Series 9.. Covergece: Defiitio ad Examples Sequeces The purpose of this chapter is to itroduce a particular way of geeratig algorithms for fidig the values of fuctios defied by their

More information

A probabilistic proof of a binomial identity

A probabilistic proof of a binomial identity A probabilistic proof of a biomial idetity Joatho Peterso Abstract We give a elemetary probabilistic proof of a biomial idetity. The proof is obtaied by computig the probability of a certai evet i two

More information

Here are a couple of warnings to my students who may be here to get a copy of what happened on a day that you missed.

Here are a couple of warnings to my students who may be here to get a copy of what happened on a day that you missed. This documet was writte ad copyrighted by Paul Dawkis. Use of this documet ad its olie versio is govered by the Terms ad Coditios of Use located at http://tutorial.math.lamar.edu/terms.asp. The olie versio

More information

CHAPTER 4: NET PRESENT VALUE

CHAPTER 4: NET PRESENT VALUE EMBA 807 Corporate Fiace Dr. Rodey Boehe CHAPTER 4: NET PRESENT VALUE (Assiged probles are, 2, 7, 8,, 6, 23, 25, 28, 29, 3, 33, 36, 4, 42, 46, 50, ad 52) The title of this chapter ay be Net Preset Value,

More information

Basic Measurement Issues. Sampling Theory and Analog-to-Digital Conversion

Basic Measurement Issues. Sampling Theory and Analog-to-Digital Conversion Theory ad Aalog-to-Digital Coversio Itroductio/Defiitios Aalog-to-digital coversio Rate Frequecy Aalysis Basic Measuremet Issues Reliability the extet to which a measuremet procedure yields the same results

More information

Asymptotic Growth of Functions

Asymptotic Growth of Functions CMPS Itroductio to Aalysis of Algorithms Fall 3 Asymptotic Growth of Fuctios We itroduce several types of asymptotic otatio which are used to compare the performace ad efficiecy of algorithms As we ll

More information

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is 0_0605.qxd /5/05 0:45 AM Page 470 470 Chapter 6 Additioal Topics i Trigoometry 6.5 Trigoometric Form of a Complex Number What you should lear Plot complex umbers i the complex plae ad fid absolute values

More information

One-sample test of proportions

One-sample test of proportions Oe-sample test of proportios The Settig: Idividuals i some populatio ca be classified ito oe of two categories. You wat to make iferece about the proportio i each category, so you draw a sample. Examples:

More information

All Digital Timing Recovery and FPGA Implementation

All Digital Timing Recovery and FPGA Implementation All Digital imig Recovery ad FPGA Implemetatio Daiel Cárdeas, Germá Arévalo Abstract Clock ad data recovery CDR is a importat subsystem of every commuicatio device sice the receiver must recover the exact

More information

S. Tanny MAT 344 Spring 1999. be the minimum number of moves required.

S. Tanny MAT 344 Spring 1999. be the minimum number of moves required. S. Tay MAT 344 Sprig 999 Recurrece Relatios Tower of Haoi Let T be the miimum umber of moves required. T 0 = 0, T = 7 Iitial Coditios * T = T + $ T is a sequece (f. o itegers). Solve for T? * is a recurrece,

More information

*The most important feature of MRP as compared with ordinary inventory control analysis is its time phasing feature.

*The most important feature of MRP as compared with ordinary inventory control analysis is its time phasing feature. Itegrated Productio ad Ivetory Cotrol System MRP ad MRP II Framework of Maufacturig System Ivetory cotrol, productio schedulig, capacity plaig ad fiacial ad busiess decisios i a productio system are iterrelated.

More information

Chapter 4: Switch realization

Chapter 4: Switch realization Chapter 4. Switch Realizatio 4.1. Switch applicatios Sigle-, two-, ad four-quadrat switches. Sychroous rectifiers 4.2. A brief survey of power semicoductor devices Power diodes, MOSFETs, BJTs, IGBTs, ad

More information

INFINITE SERIES KEITH CONRAD

INFINITE SERIES KEITH CONRAD INFINITE SERIES KEITH CONRAD. Itroductio The two basic cocepts of calculus, differetiatio ad itegratio, are defied i terms of limits (Newto quotiets ad Riema sums). I additio to these is a third fudametal

More information

Automatic Tuning for FOREX Trading System Using Fuzzy Time Series

Automatic Tuning for FOREX Trading System Using Fuzzy Time Series utomatic Tuig for FOREX Tradig System Usig Fuzzy Time Series Kraimo Maeesilp ad Pitihate Soorasa bstract Efficiecy of the automatic currecy tradig system is time depedet due to usig fixed parameters which

More information

Project Deliverables. CS 361, Lecture 28. Outline. Project Deliverables. Administrative. Project Comments

Project Deliverables. CS 361, Lecture 28. Outline. Project Deliverables. Administrative. Project Comments Project Deliverables CS 361, Lecture 28 Jared Saia Uiversity of New Mexico Each Group should tur i oe group project cosistig of: About 6-12 pages of text (ca be loger with appedix) 6-12 figures (please

More information

Estimating Probability Distributions by Observing Betting Practices

Estimating Probability Distributions by Observing Betting Practices 5th Iteratioal Symposium o Imprecise Probability: Theories ad Applicatios, Prague, Czech Republic, 007 Estimatig Probability Distributios by Observig Bettig Practices Dr C Lych Natioal Uiversity of Irelad,

More information

Partial Di erential Equations

Partial Di erential Equations Partial Di eretial Equatios Partial Di eretial Equatios Much of moder sciece, egieerig, ad mathematics is based o the study of partial di eretial equatios, where a partial di eretial equatio is a equatio

More information

1 Computing the Standard Deviation of Sample Means

1 Computing the Standard Deviation of Sample Means Computig the Stadard Deviatio of Sample Meas Quality cotrol charts are based o sample meas ot o idividual values withi a sample. A sample is a group of items, which are cosidered all together for our aalysis.

More information

How to use what you OWN to reduce what you OWE

How to use what you OWN to reduce what you OWE How to use what you OWN to reduce what you OWE Maulife Oe A Overview Most Caadias maage their fiaces by doig two thigs: 1. Depositig their icome ad other short-term assets ito chequig ad savigs accouts.

More information

1 Correlation and Regression Analysis

1 Correlation and Regression Analysis 1 Correlatio ad Regressio Aalysis I this sectio we will be ivestigatig the relatioship betwee two cotiuous variable, such as height ad weight, the cocetratio of a ijected drug ad heart rate, or the cosumptio

More information

Amendments to employer debt Regulations

Amendments to employer debt Regulations March 2008 Pesios Legal Alert Amedmets to employer debt Regulatios The Govermet has at last issued Regulatios which will amed the law as to employer debts uder s75 Pesios Act 1995. The amedig Regulatios

More information

Simple Annuities Present Value.

Simple Annuities Present Value. Simple Auities Preset Value. OBJECTIVES (i) To uderstad the uderlyig priciple of a preset value auity. (ii) To use a CASIO CFX-9850GB PLUS to efficietly compute values associated with preset value auities.

More information