### Create a StudySoup account

#### Be part of our community, it's free to join!

Already have a StudySoup account? Login here

# PERF GLOBAL CONTEXT THTR 2

UCSB

GPA 3.6

### View Full Document

## 36

## 0

## Popular in Course

## Popular in Theatre

This 27 page Class Notes was uploaded by Kobe Dare on Thursday October 22, 2015. The Class Notes belongs to THTR 2 at University of California Santa Barbara taught by Staff in Fall. Since its upload, it has received 36 views. For similar materials see /class/226857/thtr-2-university-of-california-santa-barbara in Theatre at University of California Santa Barbara.

## Similar to THTR 2 at UCSB

## Reviews for PERF GLOBAL CONTEXT

### What is Karma?

#### Karma is the currency of StudySoup.

#### You can buy or earn more Karma at anytime and redeem it for class notes, study guides, flashcards, and more!

Date Created: 10/22/15

Ammonium Toast mwumium EM Bummiilmmij mall Ellis r EE Outline Automatic Test Pattern Generation Test generation systems Test generation for combinational ckts DAlgorithm PODEM Boolean Satisfiability approach Test compaction Test generation for sequential ckts Timeframe expansion amp Extended DAlgorithm Ninevalued test generation Potential detection Issues of sequential ATPG K T Tim Cheng D icumbitg W n The ATPG Problem Problem definiTion Given a logical faulT model and a circuiT deTermine a small seT of TesT vecTors ThaT deTecT all faulTs in The circuiT Problem complexiTy Under The sTuckaT faulT model The problem is NPcompleTe even for combinaTional circuiTs However commercial TesT generaTors ThaT efficienle generaTe TesTs for gt10MgaTe ckTs are in use Today K T Tim Cheng D icumbitg W n 3 TesT GeneraTion SysTems Ack Bushnell and Agrawal EssenTial of ElecTronic TesTing 2000 K T Tim Cheng D icumbitg W n 4 Basics Path Sensitization Method Atomic operations 1 Activation fault excitation Specify inputs so as to generate the appropriate value at fault site for fault excitation Ie set S to 1 for SstuckatO fault quot Error propagation specify additional signal values to propagate the fault effect from the fault site to the outputsobservation points 0quot Line Justification Specify input values so as to produce the signal values specified in 1 or 2 39gt Value implication unique determination of values at other signals due to value assignments made in 1 2 or 3 K T Tim Cheng D icumbiig vi u A Simple Example 1 Fault activation A B C 1 2 Have a choice of error propagating through 65 or 66 a Propagating through 65 requires 62 1 2 ADO 2 contradiction b Propagating through 66 requires 64 1 2 C1 EO 2 Test ABCDE111xO K T Tim Cheng D icumbiig vi u Line Justification 39 E sa 1 3 E 0 5 C D 1 to propagate through 61 To propagate 64 need 62 63 1 Attempt to line Justify 62 63 1 G31possibeifAF1orBH1 If A F 1 3 inconsistency since C 1 so 62 O 3 Therefore 63 13 B H 1 621needAzOorFO 3 Tests are lBCDEH BCDEFH K T Tim Cheng D icumbiig vi u Completeness of ATPG Algorithms A test generation algorithm is deemed campefe iff it will find a test for a fault if exists or prove that there exists no test given sufficient time Complete algorithms can identify untestable faults Major complete algorithms for comb ckts Dalgorithm Roth 1966 PODEM Goel 1981 FAN Fujiwara 1983 Socrates Schulz 1988 BooleanSAT based ATPG Larrabee 1992 K T Tim Cheng D icumbiig vi u Single Path Sensitization Is NOT Complete d 300 3 AB1 Propagate along 63 66 requires C 1 62 64 65 1 In order for G4 1 either E 0 or 61 0 3 inconsistency 3 E O B 13 G5 0 3 inconsistency Propagate along 64 66 3 E 1amp 62 63 G5 1 62 13 C 1 ABC 2 1113 G3 0 inconsistency 3N0 test K T Tim Cheng D icumbiig vi u 9 But Boolean Difference Method Finds Test ABCD1111 K T Tim Cheng D icumbiig vi u 10 K T Tlm Cheng G Multiple Path Sensitization 21 39 10 B Two paths G3 62 and G4 66 are sensitized ie error is propagated along bafh paths D icumbiig vi u K T Tlm Cheng The DAlgebra Need to be able to deal with multiple quoterrorsquot at the inputs to a gate D represents a signal which has value 1 in normal faultfree good ckt and value 0 in faulty ckt Ie D E 10 Similarly D E 01 2 U 0 0 0 0 Behaves like a Boolean variable D icumbiig vi u Pr39imiTive Dcubes Specifies The minimal inpuT condiTions which musT be applies To a logic elemenT Ein order To produce an error signal aT The oquuT of E Propagation Dcubes The pr opagaTion Dcubes of a logic elemenT E specify minimal inpuT condiTions which are required To pr opagaTe an error signal on an inpuT or inpuTs To The oquuT of ThaT elemenT K T Tim Cheng D icumbiig vi u 13 An Example A H 12 c 3 61 s a 0 E 4 12345678910111213 Imltlve Dicube l l D prlmltlve cubes of Gz 0 X X l X 0 X l X X 0 l l l l 0 prlmltlvecubesofGa l X l X l l 0 0 0 prlmltlve Cubes 0f64 l X l X l l 0 0 0 prlmltlve Cubes OfGS X 0 0 0 X 0 l l l prlmltlve cubes of Ge 0 X 0 X 0 0 l l l K T Tim Cheng D icumbiig vi u 14 The DAlgor39iThm 1 SelecT a primiTive Dcube of The fauIT 2 ImplicaTion and checking for inconsisTency If inconsisTency occurs go To 1 3 Da rive seecTs an elemenT in DfronTier amp aTTemst To propagaTe D or D in iTs inpuTs To iTs oquuT Dfrom ier consisTsof seT of all elemenTs whose oquuT values are unspecified buT inpuTs have some signals wiTh D or D Ddrive is donegy inTersecTing The TesT cube wiTh a propagaTion Dcube of The seecTed elemenT BackTrack ie seecT anoTher propagaTion Dcube if inTersecTion is null 4 Imgica on ofDa rive perform implicaTion for The new TesT cube 5 RepeaT 3 amp 4 unTi fauITy signal propagaTed To an oquuT 6 Line 39usfi ca om ConsisTency check on inpuT condiTions required K T Tim Cheng D icumbitg W n DAlgoriThrn Example D icumbitg W n K T Tim Cheng 12345678910111213 initial test cube ti 1 D propagation Dcube of Ga test cube after Ddrive through Gs tc39 performimplication 1 1 1 1 D 0 D Check implication Dcube of 61 does not imply any other signal Dfrontier 63 Get propagation Dcube for 63 K r Tim Cheng D icumbitg v1 u Now Dfrontier is 65 amp 66 Select 65 and a propagation Dcube of G5 12345678910111213 performimplication1 1 1 1 D 0 D propagation Dcube of Gs TestcubeatterDdrive1 1 1 1 D 0 D 1 D Linejustification1 1 1 1 D 0 D 1 1 D or 1 1 1 1 D 0 D 1 1 D E K T4Tim Cheng Eliicumbitg v1 n Flowchart for Dalgorithm Initialize test cube tc Select a primitive Dcube of that as C Dintersect C with previous test cube tc and perform 39 quot 39 Backtrack to Is there a D point a choice exists none exists Select a gate from Dfrontier and a pro agation Dcube of the se ected ate as C x T Tim Cheng D icumbitg vi u Line Justification Is there any line in tc which are not justifie ES Select an unjustified line and a primitive cube C to 39usti the line Intersect C with previous test cube tc inconsistent Backtrack to the last point a choice exists consistent I none exists x T Tim Cheng D icumbitg vi u A DAlgor39ithm Example K T Tim Oneng D icumbitg v1 u primitive DIube 1 implication 2 Select Dl39rontisir Ga 3 implication 4 implication 5 1 Select Dli39ontier Ga 6 1 cation Line justi cation 7 cation Line justi cation 8 implication 8 inconsistent amp backtrack to 6 Select Dli39ontier GE 9 1 implication 10 Testis found 1 1 11 o o o 0000 UIUIUIUI UI oiooo o 0 0 0 0 1 1 0 1 1 0 0 0 0 0 0 m 0 Cl 0 oo aloo ID ID 1011 0 0 0 0 0 0 0 Potential Problems with DAlgor39ithm Since the assignment of values is allowed to internal lines more than one choice is available at such internal linegate and backtracking could occur at each gate special classes of ckts K T Tim Oneng Example D icumbitg v1 u Could result in inefficiency for large ckts and some An ECAT er r orcor r ectionzSitr39anslation ckt The PODEM AlgoriThm Goel 1981 Only allows assignmenT of values To primary inpuTs The values assigned To primary inpuTs are Then propagaTed Toward inTernal lines by The I39mpI39cafbn Example FirsT a binary value is assigned To an unassigned PI To provide a faulT effecT aT faulT siTe A 1 DeTermine The implicaTions of assigned PIs only forward implicaTion A 1 cause no implicaTion NexT assign B 1 AB12implyHD K T Tim Cheng D icumbitg v1 u 23 PODEM Decision Tree for The Example K T Tim Cheng D icumbitg v1 u 24 PODEM 39 EssenTiaIIy a process of finding a PI Si a binary value for iniTia assignmenT ConTinue assigning PI values checking To see if The error is being propagaTed To oquuTs afTer each PI assignmenT perform forward implicaTion If aT any sTage eiTher The fauIT cannoT be exciTed or The error cannoT be propagaTed furTher backfrack To The mosT recenT PI assignmenT and change H K T Tim Cheng D icumbitg W n FlowcharT of PODEM 551 a mar va ue to a lnunassi yed PI Determine im lication of all P15 5 tilere a D or D on any P0 yes T9 0551ble Wlth addlhona ssi ed P15 7 no 11 maybe Is there an u trlled combmat lolg1 o va uesP 17a551gne D icumbitg W n K T Tim Cheng STeps in PODEM 1 DeTermine an 397397 39a abj39ec ve If The faulT effecT has noT appeared aT faulT siTe The iniTial objecTive is direcTed Toward providing The faulT effecT on The faulTy line 2 Given The iniTial objecTive a PI Si a logic value are chosen ThaT have a good likelihood of meeTing The objecT Done using The backfrace procedure x T Tim Cheng D icumbitg v1 u Flowchart of Backfrace i OR NAND amp vl objectlve value V AND NOR amp vo and type of gate driving 0 39 tive Next obj line is the in ut of G OR NAND amp V0 Whlc ls atx A D NOR amp v Next obj line is the 1n ut of G Whlc ls atx a ls easlest to control an ls e hardest to control Next obj value ls the same as the current of the clirrent objectlve value objectlve value D icumbitg v1 u Next obj value is the x T Tim Cheng A PODEM Example Ini rial objecfive O 62 Backfr ace To PI X2 1 Ini rial objecfive 0 62 i Back rr ace X3 1 X2 lt Implica rion 62 D x47 Dfr on rier is 65 66 AHempT To propagafe Through 65 Require X1 1 Implica rion 61 O 64 1 65 D AHempT To propagafe D Through 68 K T m Chang D icumbitg W n 29 A PODEM Example Con39r39d Ini rial objecfive 1 66 Backfr ace To sef X4 O Implica139ion r3 1 67 0 amp 68 1 failed in p paga ring er39r39or39 Backfr ack To mos r recenf PI assignmen r 2 reassign X4 1 Implica139ion r3 O 66 D 68 D 2 Tes r is generafed A fl39 T amp con IC fest IS found backfmck K T m Chang D icumbitg W n 30 605139 of ATPG A How long Time complexiTy B How much RAM Space complexiTy C How many vecTors generaTed TesT applicaTion Time TheoreTical resulT Ibarra Si Sahni 1975 GeneraTing a TesT for comb ckT is NPcompleTe WorsTcase Time oc consTanTG G of gaTes Emperical resulT averageTime behavior ToTal ATPG Time oc consTanT 62 TesT lengTh cc 6 K T Tim Cheng D icumbitg W n 31 AcceleraTing Comb ATPG Basic goals Reduce number of backTracks Reduce processing beTween backTracking Basic Tools Topological analysis MuITipIe backTrace Learning K T Tim Cheng D icumbitg W n 32 Socrates Static Learning Preprocessing the ckt 1 Assign a logic value to a certain signal of the ckt 2 Perform all implications from that assignment 3 Learn from the results of implications Using law of contraposition A 2 B ltgt IB 2 IA Pre processi ng Learned formation During ATPG K T Tlm Cheng D icumbitg W n 33 Boolean Satisfiability Approach Given a fault it consists of two steps Step 1 Construct a formula expressing the Boolean Difference of a circuit with respect to the fault Step 2 Apply a Boolean Satisfiability SAT solver to the resulting formula K T Tlm Cheng D icumbitg W n 34 Tep 1 ExTracTing The formula 3elemenT conjuncTive normal form or 3CNF The formula is True iff The values assigned are consisTenT wiTh The TruTh for The logic elemenT EAE BcA CTCBE AB cAEK K T Tim Cheng D icumbitg W n 39 Each node of The ckT is Tagged wiTh The logic formula in Tep 1 ExTracTing The formula ConT39d a ConsTrucT The formula of The good circuiT oquuT X b ConsTrucT ormula of faulTy circuiT oquuT X39 for faulT D 1 sTuck aT O no need To repeaT The parT idenTical To 0 K T Tim Cheng D icumbitg W n Step 1 Extracting the formula Cont39d c Construct the formula of the Boolean Difference XOR of a Si b and the output of XOR should be 1 x I Xl gt BDXX39XX39V1V21 From a XBgtltE7DE5A5BDWECEE From b X39Egtlt39EYD39ED39 From c v1Yg1gtlt39V1XYv2xvzYN2Ygtlt39EgtvoEgtvz BDV1V2BD note BDgtltgtlt39gtlt39gtltYv1vz K T Tim Cheng D icumbitg W n Step 2 Satisfying the Formula Boolean Satisfiability Given a suitable representation for a Boolean function X Find an assignment Xsuch that X 1 Or prove that such an assignment does not exist ie X O for all possible assignments In the quotclassicalquot SAT problem X is represented in productof sums POS or conjunctive normal form CNF Many decision yesno problems can be formulated either directly or indirectly in terms of Boolean Satisfiability K T Tim Cheng D icumbitg W n Conjunctive Normal Form CNF wa 1c quotSource 39 S 1 39 A SakanahY m mum K T Tim Cheng Eliicumbiig W n Li l39eral amp Clause Classification guu u 3 quotSource 39 quot y 1 quot J39 quot Sakallah quot innnquot CAV2000 tutorial K T Tim Cheng Diicumbiig vi u Basic Backfr acking Search 39 39 5 639 I ID 4 a 7 K v 2 IL IL IL IL quotSource 39 U 1 39 A sakanahyu J I KT Tun Cheng gimmbitg w n Uni r Clause Rule Implications An unresolved clause is um fif if has exac rly one unassigned Ii rer al P 0 b 6X If A uni r clause has exactly one op rion for being sa risfied a b gt C Le c mus r be set To 0 quotSource 39 quot quot Sakallah quot iunCAV2000 tutorial K T Tun Cheng Diicumbitg W n Basic Search with Implications quotQ quotr 39 1 M J39 quot Sakallah 39 H in EDAquot KT Tim Chang Eliicumbiigwl n Public Released SAT Solvers by UCSB C SAT Combina rionol Circui rbased SAT Solver F Lu Li C Wang K T Cheng and R Huang A Circui r SAT solver wi rh Signal Correla rion guided learningquot DATE March 2003 F Lu L C Wang K T Cheng J Moondanos and Z Hanna quotA Signal CorrelaTion Guided ATPG Solver and I rs Applica rions for Solving Difficuli Indus rrial Casesquot DAC Jun 2003 Safari amp Sa rori2 Sequen rial Circui rbased SAT Solver M K Iyer 6 ParThasara rhy and K T Cheng SATORI A Fas r SequenTial SAT solver for Circui rsquot ICCAD Nov 2003 F Lu 6 ParThasara rhy M K Iyer L C Wang K T Cheng quotA Efficien r SequenTial SAT Solver W39iTh Improved Search STa regiesquot UCSB Technical ReporT Dec 2003 K T Tim Chang Diicumbilg vi u Zooplankton 2 How are zooplankton adapted to succeed in the pelagic environment Characteristics of the Pelagic Environment A Food dilute rare and small adaptations for feeding Carnivores Factors affecting feeding rates B No cover adaptations to prea ation C 3dimensional environment with higher concentrations of food at the surface aa aptations or buoyancy Raptorial carnivores search chase grab decapods heteropods chaetognaths DECAPOD EXAMPLE Sergestid Shrimp Long antennae chemosensory Raptorial 39 search chase grab Mollusks heteropods visual predators o Raptorial sit and wait grab Chaetognaths most important raptorial predators in the plankton g ee39 Hooks or grasping spines vestibular ridge yv t grasping spines intestinal diverticulum intestine Prey copepods sm crustaceans larvaceans Quick High ration Tentaculate Predators medusae Siphonophores ctenophores sit and wait strategy dominant prey crustaceans and fish larvae Cnidarians use nematocysts to sting prey Cubomedusae box jellies Siphonophore Siphonophores major group with sit and wait strategy Colonial Cnidarians Siphonophores Aggressive Mimicry Ctenophores 8 Comb rows of beating cilia bioluminescent sit and wait predators Factors affecting feeding rates food concentration hunger feeding history size of animal size spectrum of food quottastequot Other temperature etc hdrdaiui o In 4040de ad 15 mad Predator Functional Response Types D Ln I 3 139 Type III Type II I O N x O H 1 I 0 10 20 30 40 50 Prey Density mun mmzmmmn funcuonal spouse Type 1 crltlcal cancentratlan e c Cupcpndfccdmgm um culturc e Slze A hlgger annuals cansune more d slze spectrm of fund 4 ueehemeu in mm m mm FhVEanmJmnn emw cmpnslmm wpzynd a A ex ex mp 511 A mu mun lug rm Hz in mm Lnlumn Imyllcatlans prey fmd refuge n 5129 e taste o food quality 1 Toughnessease of handling Mucus producuon Heavlly calclfled Shape 2 Taste 0 other chemical deterrents a Food qualitynutritional value Essentlal fatty aclds proteln Detutal vs plant vs ammal cN ratlos as 1ndex Znnylallktnn 1 How areznnplallklnn adapted to succeed in Lhepelagic awirnnlnent39 Chamtenmc afthe Pelagc Emmmem A Fa ad Mute rme md small 4 Cz mvares 5 Famrs affechng feedmg mtes 3 Na cava eadapmam tapredanan c 3d1mensmnal mvn39anmentwnh hgm39 cancmtrahans af mu m the sur ce emmnmfm Imam BNo cover Adaptations to predation Vertical migration later lecture Transparency Cyclomorphosis Spines and helmets Countershading Schooling Bioluminescence Transparency Seen in almost all taxa of zooplankton convergent evolution selective pressure is predation visual fish gelatinous zooplankton guild of transparent organisms 3 Cyclomorphosis growth of spines and helmets over successive generations cladocerans and rotifers animals are genetically identical response to predators Animals on left more protected helmet Daphnia 50 I 39 Lon s ines x 39 g p mi 39 Jada l U Bosmina Iongirostris Karatefa testua o 4 5 euphausiids krill fis 6Bioluminescence p st di dist Countershading Schooling h 93 in text artle response splay when urbed School Antarctic krill photo Flip Nicklin ZoolamdonZ How are zooplankton adapted to succeed in the pelagic environment Characteristics of the Pelagic Environment A Food dilute rare and small 4 Carnivores 5 Factors affecting feeding rates B No cover adaptations to predation C3dnnenmonalenvnonnunnxn hlngherconcenaa ons cd mdmdwsm me mkpmamm nbmwmwy

### BOOM! Enjoy Your Free Notes!

We've added these Notes to your profile, click here to view them now.

### You're already Subscribed!

Looks like you've already subscribed to StudySoup, you won't need to purchase another subscription to get this material. To access this material simply click 'View Full Document'

## Why people love StudySoup

#### "There's no way I would have passed my Organic Chemistry class this semester without the notes and study guides I got from StudySoup."

#### "I made $350 in just two days after posting my first study guide."

#### "I was shooting for a perfect 4.0 GPA this semester. Having StudySoup as a study aid was critical to helping me achieve my goal...and I nailed it!"

#### "It's a great way for students to improve their educational experience and it seemed like a product that everybody wants, so all the people participating are winning."

### Refund Policy

#### STUDYSOUP CANCELLATION POLICY

All subscriptions to StudySoup are paid in full at the time of subscribing. To change your credit card information or to cancel your subscription, go to "Edit Settings". All credit card information will be available there. If you should decide to cancel your subscription, it will continue to be valid until the next payment period, as all payments for the current period were made in advance. For special circumstances, please email support@studysoup.com

#### STUDYSOUP REFUND POLICY

StudySoup has more than 1 million course-specific study resources to help students study smarter. If you’re having trouble finding what you’re looking for, our customer support team can help you find what you need! Feel free to contact them here: support@studysoup.com

Recurring Subscriptions: If you have canceled your recurring subscription on the day of renewal and have not downloaded any documents, you may request a refund by submitting an email to support@studysoup.com

Satisfaction Guarantee: If you’re not satisfied with your subscription, you can contact us for further help. Contact must be made within 3 business days of your subscription purchase and your refund request will be subject for review.

Please Note: Refunds can never be provided more than 30 days after the initial purchase date regardless of your activity on the site.