IX.2. A Semiconductor Device Primer

Size: px
Start display at page:

Download "IX.2. A Semiconductor Device Primer"

Transcription

1 1 IX.2. Smcoductor vc Prmr blograhy: 1. Grov,.S., Physcs ad chology of Smcoductor vcs (Joh Wly & Sos, w Yor, 1967) 2. Sz, S.M., Physcs of Smcoductor vcs (Joh Wly & Sos, w Yor, 1981) K S988, IS Kttl, C., Itroducto to Sold Stat Physcs (Joh Wly & Sos, w Yor, 1996) QC176.K5, IS Carrr Coctratos h robablty of a lctro stat th coducto bad bg flld s gv by th rm-rac dstrbuto f ( 1 ) ( ) / + 1 h trasto broads as th thrmal rgy crass, as llustratd for 0.005, ad 0.1 V. rm-rac strbuto ucto 1.0 Probablty of Ocuacy (300K) rgy Uts of th rm Lvl Itroducto to Radato tctors ad lctrocs, 30-Mar-99 IX.2.a. Smcoductor vc Prmr - og ad ods Hlmuth Slr LL

2 2 h dsty of atoms a S or G crystal s about atoms/cm 3. Sc th mmum carrr dsty of trst ractcal dvcs s of ordr to cm -3, vry small ocuacy robablts ar qut mortat. rm-rac strbuto ucto Probablty of Occuacy V (300K) V (400K) rgy Uts of rm Lvl I slco th bad ga s 1.12 V. If th rm lvl s at mdga, th bad-dgs wll b 0.56 V abov ad blow. s s aart from th lot, rlatvly larg dvatos from th rm lvl,.. xtrmly small occuacs, wll stll yld sgfcat carrr dsts Itroducto to Radato tctors ad lctrocs, 30-Mar-99 IX.2.a. Smcoductor vc Prmr - og ad ods Hlmuth Slr LL

3 3 h umbr of occud lctro stats s dtrmd by summg ovr all avalabl stats multld by th occuato robablty for ach dvdual stat m f ( ) Sc th dsty of stats ar th bad dg tds to b qut hgh, ths ca b wrtt as a tgral f ( ) g( ) d whr g() s th dsty of stats. c Soluto of ths tgral rqurs owldg of th dsty of stats. ortutously, to a good aroxmato th dsty of stats ar th bad dg has a arabolc dstrbuto g( ) d ( c ) 1/2 s th rgy crass byod th bad dg, th dstrbuto wll dvat from th sml arabolc form, but sc th robablty fucto dcrass vry radly, th tgral wll hardly b affctd. h scod obstacl to a sml aalytcal soluto of th tgral s th tractablty of tgratg ovr th rm dstrbuto. Itroducto to Radato tctors ad lctrocs, 30-Mar-99 IX.2.a. Smcoductor vc Prmr - og ad ods Hlmuth Slr LL

4 4 If - s at last svral tms, th rm dstrbuto ca b aroxmatd by a oltzma dstrbuto 1+ ( ) / ( ) / f ( ) ( )/ rm-rac strbuto vs. oltzma roxmato 1 Probablty of Occuacy 0.1 rm-rac strbuto oltzma roxmato rgy Sacg from rm Lvl Uts of t rgs byod 2.3 of th rm lvl th dffrc btw th oltzma aroxmato ad th rm strbuto s <10%, for rgs >4.5 t s lss tha 1%. lyg th aroxmato to th occuacy of hol stats, th robablty of a hol stat bg occud,.. a valc stat bg mty s f h ( ) 1 f ( ) ( )/ )/ Sc th bad ga s of ordr 1 V ad at room tmratur s V, th codtos for th oltzma aroxmato ar fulflld for xctato across th bad ga ( Itroducto to Radato tctors ad lctrocs, 30-Mar-99 IX.2.a. Smcoductor vc Prmr - og ad ods Hlmuth Slr LL

5 5 Wth ths smlfcatos th umbr of lctros th coducto bad thrmal qulbrum s ad th lctro coctrato ( ) 3/ 2 ( c ) / c ( c )/ whr c s th ffctv dsty of stats at th bad dg. Corrsodgly, th hol coctrato v ( )/ v h ffctv dsts of stats S ad G ar Slco: c 2.8 x cm -3 v 1.04 x cm -3 Grmaum: c 1.04 x cm -3 v 6.0 x cm -3 I a ur smcoductor ach lctro th coducto bad corrsods to a hol th valc bad, whr s th carrr coctrato trsc to a dal crystal whr th oly oulato mchasm s xctato across th bad ga. Usg th abov rsults c ( c )/ ( v )/ v whch o ca solv to obta ( ) c v log c / If th bad structur s symmtrcal ( c v ), th trsc rm lvl ls th mddl of th bad ga. v Itroducto to Radato tctors ad lctrocs, 30-Mar-99 IX.2.a. Smcoductor vc Prmr - og ad ods Hlmuth Slr LL

6 6 v rathr substatal dvatos from a symmtrcal bad structur wll ot affct ths rsult sgfcatly, as c / v trs logarthmcally ad s much smallr tha th bad ga. Slco ( g 1.12 V): 1.45 x cm -3 at 300K Grmaum ( g 0.66 V): 2.4 x cm -3 at 300K or comarso: h urst smcoductor matral that has b grow s G wth actv murty lvls of about cm -3. ot that th trsc carrr coctrato oly als to dal crystals, whr th oly sourc of mobl carrrs s thrmal xctato across th bad ga wthout ay addtoal murty atoms or crystal mrfctos that would allow othr oulato mchasms. rmarabl rsult s that th roduct of th lctro ad hol coctratos 2 ( c v )/ g / c v c v dds oly o th bad ga g ad ot o th rm lvl. hs rsult, th law of mass acto, s vry usful smcoductor dvc aalyss. It rqurs oly that th oltzma aroxmato holds, so t s ot lmtd to udod or dal crystals. Qualtatvly, t says that f o carrr ty xcds th qulbrum coctrato, rcombato wll dcras th coctratos of both lctros ad hols to mata 2. Itroducto to Radato tctors ad lctrocs, 30-Mar-99 IX.2.a. Smcoductor vc Prmr - og ad ods Hlmuth Slr LL

7 7 2. Carrr Coctratos od Crystals h qualty h oly holds for ur crystals, whr all of th lctros th coducto bad hav b thrmally xctd from th valc bad. I ractcal smcoductors th rsc of murts ts th balac towards thr th lctros or hols. Imurts ar a uavodabl byroduct of th crystal growth rocss, although scal tchqus ca achv astoudg rsults otably ultraur G whr th t murty coctrato s about cm -3,.. a rlatv coctrato of I smcoductor dvc tchology murts ar troducd ttoally to cotrol th coductvty of th smcoductor. Lt d + b th coctrato of ozd doors ad a - th coctrato of ozd acctors. Ovrall charg utralty s rsrvd, as ach ozd doat troducs a chargd carrr ad a oostly chargd atom, but th t carrr coctrato s ow or + d a ssum that th actvato rgy of th doors ad acctors s suffctly small so that thy ar fully ozd h + ad + +, Itroducto to Radato tctors ad lctrocs, 30-Mar-99 IX.2.a. Smcoductor vc Prmr - og ad ods Hlmuth Slr LL

8 8 2 whch, usg, bcoms If th acctor coctrato >> ad >> +.. th coductvty s domatd by hols. + 1, 2 << Covrsly, f th door coctrato >> ad >> th coductvty s domatd by lctros. If th coductvty s domatd by oly o ty of carrr, th rm lvl s asy to dtrm. or xaml, f >>, th xrsso ca b wrtt + + yldg c ( )/ c c log c If >>, th c - must b small,.. th rm lvl ls clos to th coducto bad dg. I ralty th murty lvls of commo doats ar ot clos ough to th bad dg for th oltzma aroxmato to hold, so th calculato must us th rm dstrbuto ad solv umrcally for. vrthlss, th qualtatv coclusos drvd hr stll aly. Itroducto to Radato tctors ad lctrocs, 30-Mar-99 IX.2.a. Smcoductor vc Prmr - og ad ods Hlmuth Slr LL

9 Itroducto to Radato tctors ad lctrocs, 30-Mar-99 Hlmuth Slr IX.2.a. Smcoductor vc Prmr - og ad ods LL 9 It s oft covt to rfr all of ths quatts to th trsc lvl, as t accouts for both c ad v. h ad th rm lvl th -rgo Varato of rm lvl wth dog ad tmratur, cludg arrowg of th bad ga wth tmratur: (from Sz) c c / ) ( / ) ( v v / ) ( / ) ( log

10 Juctos - jucto s formd at th trfac of a - ad a -ty rgo. (from Kttl) Sc th lctro coctrato th -rgo s gratr tha th -rgo, lctros wll dffus to th -rgo. Corrsodgly, hols wll dffus to th -rgo. s lctros ad hols dffus across th jucto, a sac charg du to th ozd door ad acctor atoms bulds u. h fld du to ths sac charg moss a vlocty comot th oost drcto. h stuato s dyamc: h coctrato gradt causs a cotuous dffuso currt to flow. h fld du to th sac charg drvs a drft currt th oost drcto. Itroducto to Radato tctors ad lctrocs, 30-Mar-99 IX.2.a. Smcoductor vc Prmr - og ad ods Hlmuth Slr LL

11 11 qulbrum s attad wh th two currts ar qual,.. th sum of th dffuso ad drft currts s zro. h t hol currt dsty s whr s th dffuso costat for hols ad s th lctrc fld th -rgo. o solv ths quato w ma us of th followg rlatoshs: h hol coctrato s d J q + q µ dx so ts drvatv d dx ( ) / d dx, d dx Sc th forc o a charg q du to a lctrc fld s qual to th gatv gradt of th ottal rgy, dc dv d q dx dx dx s oly th gradt s of trst ad c, v ad dffr oly by a costat offst, ay of ths thr masurs ca b usd. W ll us th trsc rm lvl sc t als throughout th saml. h rmag grdt s th st rlatosh, whch rlats th moblty to th dffuso costat µ q Itroducto to Radato tctors ad lctrocs, 30-Mar-99 IX.2.a. Smcoductor vc Prmr - og ad ods Hlmuth Slr LL

12 12 Usg ths rlatoshs th t hol currt bcoms d J q µ dx ccordgly, th t lctro currt J d q dx µ d dx Sc, dvdually, th t hol ad lctro currts qulbrum must b zro, th drvatv of th rm lvl d dx d dx 0 thrmal qulbrum th rm lvl must b costat throughout th jucto rgo. or th rm lvl to b flat, th bad structur must adat, sc o th -sd th rm lvl s ar th valc bad, whras o th -sd t s ar th coducto bad. If w assum that th doats ar xclusvly doors o th -sd ad acctors o th -sd, th dffrc th rsctv rm lvls s log hs corrsods to a lctrc ottal 2 V 1 q V b oft rfrrd to as th bult- voltag of th jucto. Itroducto to Radato tctors ad lctrocs, 30-Mar-99 IX.2.a. Smcoductor vc Prmr - og ad ods Hlmuth Slr LL

13 13 s thr or crass rlatv to, th rsctv rm lvl movs closr to th bad dg, crasg th bult- voltag. Wth crasg dog lvls th bult- voltag aroachs th quvalt ottal of th bad-ga g /q. (from Sz) Itroducto to Radato tctors ad lctrocs, 30-Mar-99 IX.2.a. Smcoductor vc Prmr - og ad ods Hlmuth Slr LL

14 14 h hrt ottal dstrbuto th jucto lads to a dlto rgo, whos wdth ca b crasd by alcato of a xtral ottal,.. rvrs basg th jucto. hs was dscussd a rvous lctur. (from Kttl) ow th forward bas mod wll b tratd mor dtal. Comlcato: lyg a xtral bas lads to a codto that dvats from thrmal qulbrum,.. th rm lvl s o logr costat throughout th jucto. Itroducto to Radato tctors ad lctrocs, 30-Mar-99 IX.2.a. Smcoductor vc Prmr - og ad ods Hlmuth Slr LL

Lecture #11. A Note of Caution

Lecture #11. A Note of Caution ctur #11 OUTE uctos rvrs brakdow dal dod aalyss» currt flow (qualtatv)» morty carrr dstrbutos Radg: Chatr 6 Srg 003 EE130 ctur 11, Sld 1 ot of Cauto Tycally, juctos C dvcs ar formd by coutr-dog. Th quatos

More information

Chapter 6. pn-junction diode: I-V characteristics

Chapter 6. pn-junction diode: I-V characteristics Chatr 6. -jucto dod: -V charactrstcs Tocs: stady stat rsos of th jucto dod udr ald d.c. voltag. ucto udr bas qualtatv dscusso dal dod quato Dvatos from th dal dod Charg-cotrol aroach Prof. Yo-S M Elctroc

More information

signal amplification; design of digital logic; memory circuits

signal amplification; design of digital logic; memory circuits hatr Th lctroc dvc that s caabl of currt ad voltag amlfcato, or ga, cojucto wth othr crcut lmts, s th trasstor, whch s a thr-trmal dvc. Th dvlomt of th slco trasstor by Bard, Bratta, ad chockly at Bll

More information

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k.

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k. Modr Smcoductor Dvcs for Itgratd rcuts haptr. lctros ad Hols Smcoductors or a bad ctrd at k=0, th -k rlatoshp ar th mmum s usually parabolc: m = k * m* d / dk d / dk gatv gatv ffctv mass Wdr small d /

More information

Lecture 14. P-N Junction Diodes: Part 3 Quantitative Analysis (Math, math and more math) Reading: Pierret 6.1

Lecture 14. P-N Junction Diodes: Part 3 Quantitative Analysis (Math, math and more math) Reading: Pierret 6.1 Lctur 4 - ucto ods art 3 Quattatv alyss Math, math ad mor math Radg rrt 6. Gorga Tch ECE 3040 - r. la oolttl Quattatv - od Soluto ssumtos stady stat codtos o- dgrat dog 3 o- dmsoal aalyss 4 low- lvl jcto

More information

Chp6. pn Junction Diode: I-V Characteristics II

Chp6. pn Junction Diode: I-V Characteristics II Ch6. Jucto od: -V Charactrstcs 147 6. 1. 3 rvato Pror 163 Hols o th quas utral -sd For covc s sak, df coordat as, - Th, d h d' ' B.C. 164 1 ) ' ( ' / qv L P qv P P P P L q d d q J '/ / 1) ( ' ' 같은방법으로

More information

pn Junction Under Reverse-Bias Conditions 3.3 Physical Operation of Diodes

pn Junction Under Reverse-Bias Conditions 3.3 Physical Operation of Diodes 3.3 Physcal Orato of os Jucto Ur vrs-bas Cotos rft Currt S : ato to th ffuso Currt comot u to majorty carrr ffuso, caus by thrmally grat morty carrrs, thr ar two currt comots lctros mov by rft from to

More information

JUNTIONS I. HARNESSING ELECTRICAL CONDUCTIVITY IN SEMICONDUCTOR MATERIALS I.1

JUNTIONS I. HARNESSING ELECTRICAL CONDUCTIVITY IN SEMICONDUCTOR MATERIALS I.1 OS A. La Rosa Physcs Dartmt P JUIOS I. HARSSIG LCRICAL CODUCIVIY I SMICODUCOR MARIALS I. Itrsc coductty ur slco I.2 trsc coductty slco dod wth slctd dffrt atoms II. RGY LVLS DIAGRAM II. II.2 Itrsc matrals

More information

3.4 Properties of the Stress Tensor

3.4 Properties of the Stress Tensor cto.4.4 Proprts of th trss sor.4. trss rasformato Lt th compots of th Cauchy strss tsor a coordat systm wth bas vctors b. h compots a scod coordat systm wth bas vctors j,, ar gv by th tsor trasformato

More information

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis Dpartmt of Mathmatcs ad Statstcs Ida Isttut of Tchology Kapur MSOA/MSO Assgmt 3 Solutos Itroducto To omplx Aalyss Th problms markd (T) d a xplct dscusso th tutoral class. Othr problms ar for hacd practc..

More information

( E) 1 PE ( 1 ) will be the probability to have not an. electron in this state (on this energy level). For the energy level configuration, depicted in

( E) 1 PE ( 1 ) will be the probability to have not an. electron in this state (on this energy level). For the energy level configuration, depicted in hatr. troducto to Sold Stat Physcs... Frm Drac Dstrbuto ad th Dsty of Ergy Stats a Sold Lt b P ( E ) th robablty to ha a lctro th stat charactrsd by th rgy E, th PE ( ) wll b th robablty to ha ot a lctro

More information

1 BIPOLAR TRANSISTOR

1 BIPOLAR TRANSISTOR POLR TRSSTOR blograhy: H. Mathu, «Physqu ds sm-coducturs t ds comosats élctroqus», 4 édto, Masso 998... am, «smcoductor hyscs ad dvcs», McGraw-Hll, c 003. P. Lturcq t G.Ry, «Physqu ds comosats actfs à

More information

ECE606: Solid State Devices Lecture 7

ECE606: Solid State Devices Lecture 7 C606: Sold Stat vcs Lctur 7 Grhard Klmck gkco@purdu.du Rfrc: Vol. 6, Ch. 3 & 4 Prstato Outl Itrsc carrr coctrato Pottal, fld, ad charg -k dagram vs. bad-dagram Basc cocpts of doors ad accptors Law of mass-acto

More information

Reliability of time dependent stress-strength system for various distributions

Reliability of time dependent stress-strength system for various distributions IOS Joural of Mathmatcs (IOS-JM ISSN: 78-578. Volum 3, Issu 6 (Sp-Oct., PP -7 www.osrjourals.org lablty of tm dpdt strss-strgth systm for varous dstrbutos N.Swath, T.S.Uma Mahswar,, Dpartmt of Mathmatcs,

More information

Correlation in tree The (ferromagnetic) Ising model

Correlation in tree The (ferromagnetic) Ising model 5/3/00 :\ jh\slf\nots.oc\7 Chaptr 7 Blf propagato corrlato tr Corrlato tr Th (frromagtc) Isg mol Th Isg mol s a graphcal mol or par ws raom Markov fl cosstg of a urct graph wth varabls assocat wth th vrtcs.

More information

Numerical Method: Finite difference scheme

Numerical Method: Finite difference scheme Numrcal Mthod: Ft dffrc schm Taylor s srs f(x 3 f(x f '(x f ''(x f '''(x...(1! 3! f(x 3 f(x f '(x f ''(x f '''(x...(! 3! whr > 0 from (1, f(x f(x f '(x R Droppg R, f(x f(x f '(x Forward dffrcg O ( x from

More information

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are Itratoal Joural Of Computatoal Egrg Rsarch (crol.com) Vol. Issu. 5 Total Prm Graph M.Rav (a) Ramasubramaa 1, R.Kala 1 Dpt.of Mathmatcs, Sr Shakth Isttut of Egrg & Tchology, Combator 641 06. Dpt. of Mathmatcs,

More information

Second Handout: The Measurement of Income Inequality: Basic Concepts

Second Handout: The Measurement of Income Inequality: Basic Concepts Scod Hadout: Th Masurmt of Icom Iqualty: Basc Cocpts O th ormatv approach to qualty masurmt ad th cocpt of "qually dstrbutd quvalt lvl of com" Suppos that that thr ar oly two dvduals socty, Rachl ad Mart

More information

LECTURE 13 Filling the bands. Occupancy of Available Energy Levels

LECTURE 13 Filling the bands. Occupancy of Available Energy Levels LUR 3 illig th bads Occupacy o Availabl rgy Lvls W hav dtrmid ad a dsity o stats. W also d a way o dtrmiig i a stat is illd or ot at a giv tmpratur. h distributio o th rgis o a larg umbr o particls ad

More information

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f MODEL QUESTION Statstcs (Thory) (Nw Syllabus) GROUP A d θ. ) Wrt dow th rsult of ( ) ) d OR, If M s th mod of a dscrt robablty dstrbuto wth mass fucto f th f().. at M. d d ( θ ) θ θ OR, f() mamum valu

More information

Transparency and stability of low density stellar plasma related to Boltzmann statistics, inverse stimulated bremsstrahlung and to dark matter

Transparency and stability of low density stellar plasma related to Boltzmann statistics, inverse stimulated bremsstrahlung and to dark matter Trasparcy ad stablty of low dsty stllar plasma rlatd to oltzma statstcs, vrs stmulatd brmsstrahlug ad to dark mattr Y. -Aryh Tcho-Isral Isttut of Tchology, Physc Dpartmt, Isral, Hafa, Emal: phr65yb@tcho.physcs.ac.l

More information

Phase diagram and frustration of decoherence in Y-shaped Josephson junction networks. D.Giuliano(Cosenza), P. Sodano(Perugia)

Phase diagram and frustration of decoherence in Y-shaped Josephson junction networks. D.Giuliano(Cosenza), P. Sodano(Perugia) Phas dagram ad frustrato of dcohrc Y-shapd Josphso jucto tworks D.GulaoCosza, P. SodaoPruga Frz, Frz, Octobr Octobr 008 008 Ma da Y-Shapd twork of Josphso jucto chas YJJN wth a magtc frustrato Ft-couplg

More information

Lecture 1: Empirical economic relations

Lecture 1: Empirical economic relations Ecoomcs 53 Lctur : Emprcal coomc rlatos What s coomtrcs? Ecoomtrcs s masurmt of coomc rlatos. W d to kow What s a coomc rlato? How do w masur such a rlato? Dfto: A coomc rlato s a rlato btw coomc varabls.

More information

Unbalanced Panel Data Models

Unbalanced Panel Data Models Ubalacd Pal Data odls Chaptr 9 from Baltag: Ecoomtrc Aalyss of Pal Data 5 by Adrás alascs 4448 troducto balacd or complt pals: a pal data st whr data/obsrvatos ar avalabl for all crosssctoal uts th tr

More information

Course 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source:

Course 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source: Cour 0 Shadg Cour 0 Shadg. Bac Coct: Lght Sourc: adac: th lght rg radatd from a ut ara of lght ourc or urfac a ut old agl. Sold agl: $ # r f lght ourc a ot ourc th ut ara omttd abov dfto. llumato: lght

More information

Weights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( )

Weights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( ) Sprg Ch 35: Statstcal chacs ad Chcal Ktcs Wghts... 9 Itrprtg W ad lw... 3 What s?... 33 Lt s loo at... 34 So Edots... 35 Chaptr 3: Fudatal Prcpls of Stat ch fro a spl odl (drvato of oltza dstrbuto, also

More information

The probability of Riemann's hypothesis being true is. equal to 1. Yuyang Zhu 1

The probability of Riemann's hypothesis being true is. equal to 1. Yuyang Zhu 1 Th robablty of Ra's hyothss bg tru s ual to Yuyag Zhu Abstract Lt P b th st of all r ubrs P b th -th ( ) lt of P ascdg ordr of sz b ostv tgrs ad s a rutato of wth Th followg rsults ar gv ths ar: () Th

More information

How many neutrino species?

How many neutrino species? ow may utrio scis? Two mthods for dtrmii it lium abudac i uivrs At a collidr umbr of utrio scis Exasio of th uivrs is ovrd by th Fridma quatio R R 8G tot Kc R Whr: :ubblcostat G :Gravitatioal costat 6.

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D { 2 2... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data

More information

Independent Domination in Line Graphs

Independent Domination in Line Graphs Itratoal Joural of Sctfc & Egrg Rsarch Volum 3 Issu 6 Ju-1 1 ISSN 9-5518 Iddt Domato L Grahs M H Muddbhal ad D Basavarajaa Abstract - For ay grah G th l grah L G H s th trscto grah Thus th vrtcs of LG

More information

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty 3 Bary Choc LPM logt logstc rgrso probt Multpl Choc Multomal Logt (c Pogsa Porchawssul,

More information

Pion Production via Proton Synchrotron Radiation in Strong Magnetic Fields in Relativistic Quantum Approach

Pion Production via Proton Synchrotron Radiation in Strong Magnetic Fields in Relativistic Quantum Approach Po Producto va Proto Sychrotro Radato Strog Magtc Flds Rlatvstc Quatum Approach Partcl Productos TV Ergy Rgo Collaborators Toshtaka Kajo Myog-K Chou Grad. J. MATHEWS Tomoyuk Maruyama BRS. Nho Uvrsty NaO,

More information

Aotomorphic Functions And Fermat s Last Theorem(4)

Aotomorphic Functions And Fermat s Last Theorem(4) otomorphc Fuctos d Frmat s Last Thorm(4) Chu-Xua Jag P. O. Box 94 Bg 00854 P. R. Cha agchuxua@sohu.com bsract 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral

More information

I-V and Switching Characteristics of Back Illuminated OPFET using Finite Difference Methods

I-V and Switching Characteristics of Back Illuminated OPFET using Finite Difference Methods tratoal Joural of Coutr Alcatos (975 8887 Volu 3 No.4, Octobr -V ad Swtchg Charactrstcs of Back lluatd OPFET usg Ft Dffrc Mthods Rajsh B. oha Dt of Elctrocs ad Tlcoucato Goa Collg of Egrg Faragud, Poda,

More information

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space.

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space. Rpatd Trals: As w hav lood at t, th thory of probablty dals wth outcoms of sgl xprmts. I th applcatos o s usually trstd two or mor xprmts or rpatd prformac or th sam xprmt. I ordr to aalyz such problms

More information

' 1.00, has the form of a rhomb with

' 1.00, has the form of a rhomb with Problm I Rflcto ad rfracto of lght A A trstg prsm Th ma scto of a glass prsm stuatd ar ' has th form of a rhomb wth A th yllow bam of moochromatc lght propagatg towards th prsm paralll wth th dagoal AC

More information

Washington State University

Washington State University he 3 Ktics ad Ractor Dsig Sprg, 00 Washgto Stat Uivrsity Dpartmt of hmical Egrg Richard L. Zollars Exam # You will hav o hour (60 muts) to complt this xam which cosists of four (4) problms. You may us

More information

8. Queueing systems. Contents. Simple teletraffic model. Pure queueing system

8. Queueing systems. Contents. Simple teletraffic model. Pure queueing system 8. Quug sysms Cos 8. Quug sysms Rfrshr: Sml lraffc modl Quug dscl M/M/ srvr wag lacs Alcao o ack lvl modllg of daa raffc M/M/ srvrs wag lacs lc8. S-38.45 Iroduco o Tlraffc Thory Srg 5 8. Quug sysms 8.

More information

Chapter 4 NUMERICAL METHODS FOR SOLVING BOUNDARY-VALUE PROBLEMS

Chapter 4 NUMERICAL METHODS FOR SOLVING BOUNDARY-VALUE PROBLEMS Chaptr 4 NUMERICL METHODS FOR SOLVING BOUNDRY-VLUE PROBLEMS 00 4. Varatoal formulato two-msoal magtostatcs Lt th followg magtostatc bouar-valu problm b cosr ( ) J (4..) 0 alog ΓD (4..) 0 alog ΓN (4..)

More information

They must have different numbers of electrons orbiting their nuclei. They must have the same number of neutrons in their nuclei.

They must have different numbers of electrons orbiting their nuclei. They must have the same number of neutrons in their nuclei. 37 1 How may utros ar i a uclus of th uclid l? 20 37 54 2 crtai lmt has svral isotops. Which statmt about ths isotops is corrct? Thy must hav diffrt umbrs of lctros orbitig thir ucli. Thy must hav th sam

More information

The E vs k diagrams are in general a function of the k -space direction in a crystal

The E vs k diagrams are in general a function of the k -space direction in a crystal vs dagram p m m he parameter s called the crystal mometum ad s a parameter that results from applyg Schrödger wave equato to a sgle-crystal lattce. lectros travelg dfferet drectos ecouter dfferet potetal

More information

Control Systems. Lecture 8 Root Locus. Root Locus. Plant. Controller. Sensor

Control Systems. Lecture 8 Root Locus. Root Locus. Plant. Controller. Sensor Cotol Syt ctu 8 Root ocu Clacal Cotol Pof. Eugo Schut hgh Uvty Root ocu Cotoll Plat R E C U Y - H C D So Y C C R C H Wtg th loo ga a w a ttd tackg th clod-loo ol a ga va Clacal Cotol Pof. Eugo Schut hgh

More information

On Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data

On Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data saqartvlos mcrbata rovul akadms moamb, t 9, #2, 2015 BULLETIN OF THE GEORGIAN NATIONAL ACADEMY OF SCIENCES, vol 9, o 2, 2015 Mathmatcs O Estmato of Ukow Paramtrs of Epotal- Logarthmc Dstrbuto by Csord

More information

Periodic Table of Elements. EE105 - Spring 2007 Microelectronic Devices and Circuits. The Diamond Structure. Electronic Properties of Silicon

Periodic Table of Elements. EE105 - Spring 2007 Microelectronic Devices and Circuits. The Diamond Structure. Electronic Properties of Silicon EE105 - Srg 007 Mcroelectroc Devces ad Crcuts Perodc Table of Elemets Lecture Semcoductor Bascs Electroc Proertes of Slco Slco s Grou IV (atomc umber 14) Atom electroc structure: 1s s 6 3s 3 Crystal electroc

More information

Session : Plasmas in Equilibrium

Session : Plasmas in Equilibrium Sssio : Plasmas i Equilibrium Ioizatio ad Coductio i a High-prssur Plasma A ormal gas at T < 3000 K is a good lctrical isulator, bcaus thr ar almost o fr lctros i it. For prssurs > 0.1 atm, collisio amog

More information

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES TRANSFORMATION OF FUNCTION OF A RANDOM VARIABLE UNIVARIATE TRANSFORMATIONS TRANSFORMATION OF RANDOM VARIABLES If s a rv wth cdf F th Y=g s also a rv. If w wrt

More information

Solid State Device Fundamentals

Solid State Device Fundamentals 8 Biasd - Juctio Solid Stat Dvic Fudamtals 8. Biasd - Juctio ENS 345 Lctur Cours by Aladr M. Zaitsv aladr.zaitsv@csi.cuy.du Tl: 718 98 81 4N101b Dartmt of Egirig Scic ad Physics Biasig uiolar smicoductor

More information

Lecture #13. Diode Current due to Generation

Lecture #13. Diode Current due to Generation Lecture #13 Juctos OUTLINE reverse bas curret devatos from deal behavor small-sgal model Readg: Chaters 6. & 7 EE13 Lecture 13, Slde 1 Dode Curret due to Geerato If a electro-hole ar s geerated (e.g. by

More information

Section 5.1/5.2: Areas and Distances the Definite Integral

Section 5.1/5.2: Areas and Distances the Definite Integral Scto./.: Ars d Dstcs th Dt Itgrl Sgm Notto Prctc HW rom Stwrt Ttook ot to hd p. #,, 9 p. 6 #,, 9- odd, - odd Th sum o trms,,, s wrtt s, whr th d o summto Empl : Fd th sum. Soluto: Th Dt Itgrl Suppos w

More information

Statistical Thermodynamics Essential Concepts. (Boltzmann Population, Partition Functions, Entropy, Enthalpy, Free Energy) - lecture 5 -

Statistical Thermodynamics Essential Concepts. (Boltzmann Population, Partition Functions, Entropy, Enthalpy, Free Energy) - lecture 5 - Statstcal Thrmodyamcs sstal Cocpts (Boltzma Populato, Partto Fuctos, tropy, thalpy, Fr rgy) - lctur 5 - uatum mchacs of atoms ad molculs STATISTICAL MCHANICS ulbrum Proprts: Thrmodyamcs MACROSCOPIC Proprts

More information

and one unit cell contains 8 silicon atoms. The atomic density of silicon is

and one unit cell contains 8 silicon atoms. The atomic density of silicon is Chaptr Vsualzato o th Slo Crystal (a) Plas rr to Fgur - Th 8 orr atoms ar shar by 8 ut lls a thror otrbut atom Smlarly, th 6 a atoms ar ah shar by ut lls a otrbut atoms A, 4 atoms ar loat s th ut ll H,

More information

ME 501A Seminar in Engineering Analysis Page 1

ME 501A Seminar in Engineering Analysis Page 1 St Ssts o Ordar Drtal Equatos Novbr 7 St Ssts o Ordar Drtal Equatos Larr Cartto Mcacal Er 5A Sar Er Aalss Novbr 7 Outl Mr Rsults Rvw last class Stablt o urcal solutos Stp sz varato or rror cotrol Multstp

More information

Estimating the Variance in a Simulation Study of Balanced Two Stage Predictors of Realized Random Cluster Means Ed Stanek

Estimating the Variance in a Simulation Study of Balanced Two Stage Predictors of Realized Random Cluster Means Ed Stanek Etatg th Varac a Sulato Study of Balacd Two Stag Prdctor of Ralzd Rado Clutr Ma Ed Stak Itroducto W dcrb a pla to tat th varac copot a ulato tudy N ( µ µ W df th varac of th clutr paratr a ug th N ulatd

More information

Semiconductor Device Physics

Semiconductor Device Physics 1 Semcoductor evce Physcs Lecture 7 htt://ztomul.wordress.com 0 1 3 Semcoductor evce Physcs Chater 6 Jucto odes: I-V Characterstcs 3 Chater 6 Jucto odes: I-V Characterstcs Qualtatve ervato Majorty carrers

More information

Consistency of the Maximum Likelihood Estimator in Logistic Regression Model: A Different Approach

Consistency of the Maximum Likelihood Estimator in Logistic Regression Model: A Different Approach ISSN 168-8 Joural of Statstcs Volum 16, 9,. 1-11 Cosstcy of th Mamum Lklhood Estmator Logstc Rgrsso Modl: A Dffrt Aroach Abstract Mamuur Rashd 1 ad Nama Shfa hs artcl vstgats th cosstcy of mamum lklhood

More information

A Study of Fundamental Law of Thermal Radiation and Thermal Equilibrium Process

A Study of Fundamental Law of Thermal Radiation and Thermal Equilibrium Process Itratoal Joural of Hgh Ergy Physcs 5; (3): 38-46 Publshd ol May 6, 5 (http://www.sccpublshggroup.com/j/jhp) do:.648/j.jhp.53. ISSN: 376-745 (Prt); ISSN: 376-7448 (Ol) A Study of Fudamtal Law of Thrmal

More information

Chapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University

Chapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University Chatr 5 Scal Dscrt Dstrbutos W-Guy Tzg Comutr Scc Dartmt Natoal Chao Uvrsty Why study scal radom varabls Thy aar frqutly thory, alcatos, statstcs, scc, grg, fac, tc. For aml, Th umbr of customrs a rod

More information

Supplementary Information to: "Low electronpolar. of high carrier mobility in methylammonium

Supplementary Information to: Low electronpolar. of high carrier mobility in methylammonium Elctroc Supplmtary Matral ES or Physcal Chmstry Chmcal Physcs. Ths joural s th Owr Socts 6 Supplmtary ormato to: "Low lctropolar optcal phoo scattrg at th udamt o hgh carrr moblty mthylammoum lad-odd provsts."

More information

Phase-Field Modeling for Dynamic Recrystallization

Phase-Field Modeling for Dynamic Recrystallization 0 (0000) 0 0 Plas lav ths spac mpty Phas-Fld Modlg for Dyamc Rcrystallzato T. Takak *, A. Yamaaka, Y. Tomta 3 Faculty of Martm Sccs, Kob Uvrsty, 5--, Fukamam, Hgashada, Kob, 658-00, Japa (Emal : takak@martm.kob-u.ac.p)

More information

Comparisons of the Variance of Predictors with PPS sampling (update of c04ed26.doc) Ed Stanek

Comparisons of the Variance of Predictors with PPS sampling (update of c04ed26.doc) Ed Stanek Coparo o th Varac o Prdctor wth PPS aplg (updat o c04d6doc Ed Sta troducto W copar prdctor o a PSU a or total bad o PPS aplg Th tratgy to ollow that o Sta ad Sgr (JASA, 004 whr w xpr th prdctor a a lar

More information

Counting the compositions of a positive integer n using Generating Functions Start with, 1. x = 3 ), the number of compositions of 4.

Counting the compositions of a positive integer n using Generating Functions Start with, 1. x = 3 ), the number of compositions of 4. Coutg th compostos of a postv tgr usg Gratg Fuctos Start wth,... - Whr, for ampl, th co-ff of s, for o summad composto of aml,. To obta umbr of compostos of, w d th co-ff of (...) ( ) ( ) Hr for stac w

More information

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1)

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1) Math Trcks r! Combato - umbr o was to group r o objcts, ordr ot mportat r! r! ar 0 a r a s costat, 0 < r < k k! k 0 EX E[XX-] + EX Basc Probablt 0 or d Pr[X > ] - Pr[X ] Pr[ X ] Pr[X ] - Pr[X ] Proprts

More information

International Journal of Mathematical Archive-6(5), 2015, Available online through ISSN

International Journal of Mathematical Archive-6(5), 2015, Available online through  ISSN Itratoal Joural of Mathmatal Arhv-6), 0, 07- Avalabl ol through wwwjmafo ISSN 9 06 ON THE LINE-CUT TRANSFORMATION RAPHS B BASAVANAOUD*, VEENA R DESAI Dartmt of Mathmats, Karatak Uvrsty, Dharwad - 80 003,

More information

S. KAR T. ROY M. MAITI 1. INTRODUCTION

S. KAR T. ROY M. MAITI 1. INTRODUCTION Yugoslav Joural o Oratos Rsarch Numbr 9- MULTI-ITEM INENTORY MODEL WITH PROBABILISTI PRIE DEPENDENT DEMAND AND IMPREISE GOAL AND ONSTRAINTS S KAR Halda Isttut o Tchology Halda Wst Bgal T ROY Bgal Egrg

More information

J. Liu, Z. L. Zhang and C. Thaulow, A dynamic void growth model, Proceedings of the 11 th Int. Congress on Fracture (ICF11), Turin, Italy, March

J. Liu, Z. L. Zhang and C. Thaulow, A dynamic void growth model, Proceedings of the 11 th Int. Congress on Fracture (ICF11), Turin, Italy, March J. Lu, Z. L. Zhag ad C. Thaulow, A dyamc vod growth modl, Procdgs of th th It. Cogrss o Fractur (ICF), Tur, Italy, March -5, 5 A YNAMIC OI GROWTH MOEL J. Lu, Z. L. Zhag ad C. Thaulow INTEF Matrals ad Chmstry

More information

Nuclear Chemistry -- ANSWERS

Nuclear Chemistry -- ANSWERS Hoor Chstry Mr. Motro 5-6 Probl St Nuclar Chstry -- ANSWERS Clarly wrt aswrs o sparat shts. Show all work ad uts.. Wrt all th uclar quatos or th radoactv dcay srs o Urau-38 all th way to Lad-6. Th dcay

More information

The translational oscillations of a cylindrical bubble in a bounded volume of a liquid with free deformable interface

The translational oscillations of a cylindrical bubble in a bounded volume of a liquid with free deformable interface Joural of Physcs: Cofrc Srs PAPER OPEN ACCESS Th traslatoal oscllatos of a cyldrcal bubbl a boudd volum of a lqud wth fr dformabl trfac To ct ths artcl: A A Alabuzhv ad M I Kaysa 6 J. Phys.: Cof. Sr. 68

More information

Machine Learning. Principle Component Analysis. Prof. Dr. Volker Sperschneider

Machine Learning. Principle Component Analysis. Prof. Dr. Volker Sperschneider Mach Larg Prcpl Compot Aalyss Prof. Dr. Volkr Sprschdr AG Maschlls Lr ud Natürlchsprachlch Systm Isttut für Iformatk chsch Fakultät Albrt-Ludgs-Uvrstät Frburg sprschdr@formatk.u-frburg.d I. Archtctur II.

More information

Solid State Device Fundamentals

Solid State Device Fundamentals Sold State Devce Fudametals 9 polar jucto trasstor Sold State Devce Fudametals 9. polar Jucto Trasstor NS 345 Lecture ourse by Alexader M. Zatsev alexader.zatsev@cs.cuy.edu Tel: 718 98 81 4N101b Departmet

More information

1. Can not explain why certain spectral lines are more intense. 2. many spectral lines actually consist of several separate lines

1. Can not explain why certain spectral lines are more intense. 2. many spectral lines actually consist of several separate lines Chatr 5 Quatu Mchacs ltato of Bohr thory:. Ca ot la why crta sctral ls ar or ts tha othrs.. ay sctral ls actually cosst of svral sarat ls whos λ dffr slghtly. 3. a udrstadg of how dvdual atos tract wth

More information

7THE DIFFUSION OF PRODUCT INNOVATIONS AND MARKET STRUCTURE

7THE DIFFUSION OF PRODUCT INNOVATIONS AND MARKET STRUCTURE 7THE DIFFUSION OF PRODUCT INNOVATIONS AND MARKET STRUCTURE Isttut of Ecoomc Forcastg Roxaa IDU Abstract I ths papr I aalyz th dffuso of a product ovato that was rctly mad avalabl for lcsd purchas wth a

More information

IS 709/809: Computational Methods in IS Research. Simple Markovian Queueing Model

IS 709/809: Computational Methods in IS Research. Simple Markovian Queueing Model IS 79/89: Comutatoal Methods IS Research Smle Marova Queueg Model Nrmalya Roy Deartmet of Iformato Systems Uversty of Marylad Baltmore Couty www.umbc.edu Queueg Theory Software QtsPlus software The software

More information

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES DEFINITION OF A COMPLEX NUMBER: A umbr of th form, whr = (, ad & ar ral umbrs s calld a compl umbr Th ral umbr, s calld ral part of whl s calld

More information

Lecture 7 Diffusion. Our fluid equations that we developed before are: v t v mn t

Lecture 7 Diffusion. Our fluid equations that we developed before are: v t v mn t Cla ot fo EE6318/Phy 6383 Spg 001 Th doumt fo tutoal u oly ad may ot b opd o dtbutd outd of EE6318/Phy 6383 tu 7 Dffuo Ou flud quato that w dvlopd bfo a: f ( )+ v v m + v v M m v f P+ q E+ v B 13 1 4 34

More information

In 1991 Fermat s Last Theorem Has Been Proved

In 1991 Fermat s Last Theorem Has Been Proved I 99 Frmat s Last Thorm Has B Provd Chu-Xua Jag P.O.Box 94Bg 00854Cha Jcxua00@s.com;cxxxx@6.com bstract I 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral

More information

APPENDIX: STATISTICAL TOOLS

APPENDIX: STATISTICAL TOOLS I. Nots o radom samplig Why do you d to sampl radomly? APPENDI: STATISTICAL TOOLS I ordr to masur som valu o a populatio of orgaisms, you usually caot masur all orgaisms, so you sampl a subst of th populatio.

More information

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

School of Aerospace Engineering Origins of Quantum Theory. Measurements of emission of light (EM radiation) from (H) atoms found discrete lines

School of Aerospace Engineering Origins of Quantum Theory. Measurements of emission of light (EM radiation) from (H) atoms found discrete lines Ogs of Quatu Thoy Masuts of sso of lght (EM adato) fo (H) atos foud dsct ls 5 4 Abl to ft to followg ss psso ν R λ c λwavlgth, νfqucy, cspd lght RRydbg Costat (~09,7677.58c - ),,, +, +,..g.,,.6, 0.6, (Lya

More information

Heisenberg Model. Sayed Mohammad Mahdi Sadrnezhaad. Supervisor: Prof. Abdollah Langari

Heisenberg Model. Sayed Mohammad Mahdi Sadrnezhaad. Supervisor: Prof. Abdollah Langari snbrg Modl Sad Mohammad Mahd Sadrnhaad Survsor: Prof. bdollah Langar bstract: n ths rsarch w tr to calculat analtcall gnvalus and gnvctors of fnt chan wth ½-sn artcls snbrg modl. W drov gnfuctons for closd

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D {... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data pots

More information

Physics of Very High Frequency (VHF) Capacitively Coupled Plasma Discharges

Physics of Very High Frequency (VHF) Capacitively Coupled Plasma Discharges Physcs of Vry Hgh Frquncy (VHF) Capactvly Coupld Plasma Dschargs Shahd Rauf, Kallol Bra, Stv Shannon, and Kn Collns Appld Matrals, Inc., Sunnyval, CA AVS 54 th Intrnatonal Symposum Sattl, WA Octobr 15-19,

More information

Gauge Theories. Elementary Particle Physics Strong Interaction Fenomenology. Diego Bettoni Academic year

Gauge Theories. Elementary Particle Physics Strong Interaction Fenomenology. Diego Bettoni Academic year Gau Thors Elmary Parcl Physcs Sro Iraco Fomoloy o Bo cadmc yar - Gau Ivarac Gau Ivarac Whr do Laraas or Hamloas com from? How do w kow ha a cra raco should dscrb a acual hyscal sysm? Why s h lcromac raco

More information

Chapter 2 Infinite Series Page 1 of 11. Chapter 2 : Infinite Series

Chapter 2 Infinite Series Page 1 of 11. Chapter 2 : Infinite Series Chatr Ifiit Sris Pag of Sctio F Itgral Tst Chatr : Ifiit Sris By th d of this sctio you will b abl to valuat imror itgrals tst a sris for covrgc by alyig th itgral tst aly th itgral tst to rov th -sris

More information

6.012 Electronic Devices and Circuits Formula Sheet for Final Exam, Fall q = 1.6x10 19 Coul III IV V = x10 14 o. = 3.

6.012 Electronic Devices and Circuits Formula Sheet for Final Exam, Fall q = 1.6x10 19 Coul III IV V = x10 14 o. = 3. 6.0 Elctc Dvcs ad Ccuts ula Sht f al Exa, all 003 Paat Valus: Pdc Tabl: q.6x0 9 Cul III IV V 8.854 x0 4 /c,,s.7,,so 3.9 B C N 0 S /c, SO 3.5 x0 3 /c Al S P [S@R.T] 0 0 c 3 Ga G As /q 0.05 V ; ( /q) l0

More information

Narayana IIT Academy

Narayana IIT Academy INDIA Sc: LT-IIT-SPARK Dat: 9--8 6_P Max.Mars: 86 KEY SHEET PHYSIS A 5 D 6 7 A,B 8 B,D 9 A,B A,,D A,B, A,B B, A,B 5 A 6 D 7 8 A HEMISTRY 9 A B D B B 5 A,B,,D 6 A,,D 7 B,,D 8 A,B,,D 9 A,B, A,B, A,B,,D A,B,

More information

3. Carriers. 3.1 Carriers in semiconductors. 3.2 Equilibrium Carriers. 3.3 Excess Carriers

3. Carriers. 3.1 Carriers in semiconductors. 3.2 Equilibrium Carriers. 3.3 Excess Carriers 3. Carrers v.18.aug 3.1 Carrers semcoductors tyes (electros, holes), roertes (charge, mass, m eergy, coducto) trsc / extrsc morty / majorty 3. qulbrum Carrers ad grahcal / aalytcal solutos mass acto law

More information

Radial Distribution Function. Long-Range Corrections (1) Temperature. 3. Calculation of Equilibrium Properties. Thermodynamics Properties

Radial Distribution Function. Long-Range Corrections (1) Temperature. 3. Calculation of Equilibrium Properties. Thermodynamics Properties . Calculato o qulbrum Prorts. hrmodamc Prorts mratur, Itral rg ad Prssur Fr rg ad tro. Calculato o Damc Prorts Duso Coct hrmal Coductvt Shar scost Irard Absorto Coct k k k mratur m v Rmmbr hrmodamcs or

More information

Grand Canonical Ensemble

Grand Canonical Ensemble Th nsmbl of systms mmrsd n a partcl-hat rsrvor at constant tmpratur T, prssur P, and chmcal potntal. Consdr an nsmbl of M dntcal systms (M =,, 3,...M).. Thy ar mutually sharng th total numbr of partcls

More information

Multi-fluid magnetohydrodynamics in the solar atmosphere

Multi-fluid magnetohydrodynamics in the solar atmosphere Mul-flud magohydrodyams h solar amoshr Tmuraz Zaqarashvl თეიმურაზ ზაქარაშვილი Sa Rsarh Isu of Ausra Aadmy of Ss Graz Ausra ISSI-orksho Parally ozd lasmas asrohyss 6 Jauary- Fbruary 04 ISSI-orksho Parally

More information

CHAPTER 7. X and 2 = X

CHAPTER 7. X and 2 = X CHATR 7 Sco 7-7-. d r usd smors o. Th vrcs r d ; comr h S vrc hs cs / / S S Θ Θ Sc oh smors r usd mo o h vrcs would coclud h s h r smor wh h smllr vrc. 7-. [ ] Θ 7 7 7 7 7 7 [ ] Θ ] [ 7 6 Boh d r usd sms

More information

Handout 7. Properties of Bloch States and Electron Statistics in Energy Bands

Handout 7. Properties of Bloch States and Electron Statistics in Energy Bands Hdout 7 Popts of Bloch Stts d Elcto Sttstcs Eg Bds I ths lctu ou wll l: Popts of Bloch fuctos Podc boud codtos fo Bloch fuctos Dst of stts -spc Elcto occupto sttstcs g bds ECE 407 Spg 009 Fh R Coll Uvst

More information

Lecture 14. Relic neutrinos Temperature at neutrino decoupling and today Effective degeneracy factor Neutrino mass limits Saha equation

Lecture 14. Relic neutrinos Temperature at neutrino decoupling and today Effective degeneracy factor Neutrino mass limits Saha equation Lctur Rlc nutrnos mpratur at nutrno dcoupln and today Effctv dnracy factor Nutrno mass lmts Saha quaton Physcal Cosmoloy Lnt 005 Rlc Nutrnos Nutrnos ar wakly ntractn partcls (lptons),,,,,,, typcal ractons

More information

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca**

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca** ERDO-MARANDACHE NUMBER b Tbrc* Tt Tbrc** *Trslv Uvrsty of Brsov, Computr cc Dprtmt **Uvrsty of Mchstr, Computr cc Dprtmt Th strtg pot of ths rtcl s rprstd by rct work of Fch []. Bsd o two symptotc rsults

More information

Note: Torque is prop. to current Stationary voltage is prop. to speed

Note: Torque is prop. to current Stationary voltage is prop. to speed DC Mach Cotrol Mathmatcal modl. Armatr ad orq f m m a m m r a a a a a dt d ψ ψ ψ ω Not: orq prop. to crrt Statoary voltag prop. to pd Mathmatcal modl. Fld magtato f f f f d f dt a f ψ m m f f m fλ h torq

More information

The R Package PK for Basic Pharmacokinetics

The R Package PK for Basic Pharmacokinetics Wolfsggr, h R Pacag PK St 6 h R Pacag PK for Basc Pharmacotcs Mart J. Wolfsggr Dpartmt of Bostatstcs, Baxtr AG, Va, Austra Addrss of th author: Mart J. Wolfsggr Dpartmt of Bostatstcs Baxtr AG Wagramr Straß

More information

ASYMPTOTIC AND TOLERANCE 2D-MODELLING IN ELASTODYNAMICS OF CERTAIN THIN-WALLED STRUCTURES

ASYMPTOTIC AND TOLERANCE 2D-MODELLING IN ELASTODYNAMICS OF CERTAIN THIN-WALLED STRUCTURES AYMPTOTIC AD TOLERACE D-MODELLIG I ELATODYAMIC OF CERTAI THI-WALLED TRUCTURE B. MICHALAK Cz. WOŹIAK Dpartmt of tructural Mchacs Lodz Uvrsty of Tchology Al. Poltrchk 6 90-94 Łódź Polad Th objct of aalyss

More information

Chapter 2 Reciprocal Lattice. An important concept for analyzing periodic structures

Chapter 2 Reciprocal Lattice. An important concept for analyzing periodic structures Chpt Rcpocl Lttc A mpott cocpt o lyzg podc stuctus Rsos o toducg cpocl lttc Thoy o cystl dcto o x-ys, utos, d lctos. Wh th dcto mxmum? Wht s th tsty? Abstct study o uctos wth th podcty o Bvs lttc Fou tsomto.

More information

CBSE , ˆj. cos CBSE_2015_SET-1. SECTION A 1. Given that a 2iˆ ˆj. We need to find. 3. Consider the vector equation of the plane.

CBSE , ˆj. cos CBSE_2015_SET-1. SECTION A 1. Given that a 2iˆ ˆj. We need to find. 3. Consider the vector equation of the plane. CBSE CBSE SET- SECTION. Gv tht d W d to fd 7 7 Hc, 7 7 7. Lt,. W ow tht.. Thus,. Cosd th vcto quto of th pl.. z. - + z = - + z = Thus th Cts quto of th pl s - + z = Lt d th dstc tw th pot,, - to th pl.

More information

EFFECT OF PLASMA-WALL RECOMBINATION AND TURBULENT RESISTIVITY ON THE CONDUCTIVITY IN HALL THRUSTERS

EFFECT OF PLASMA-WALL RECOMBINATION AND TURBULENT RESISTIVITY ON THE CONDUCTIVITY IN HALL THRUSTERS EFFEC OF PLASMA-WALL RECOMBINAION AND URBULEN RESISIVIY ON E CONDUCIVIY IN ALL RUSERS A.A. Ivaov, A.A. Ivaov Jr ad M. Bacal Laborator d Physqu t cholog ds Plasmas, Ecol Polytchqu, UMR 7648 du CNRS, 98

More information

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information