Artwork master Inspection and touch up Production phototools Inspection and touch up. development of outer layers

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1 AutomaticPCBInspectionAlgorithms:ASurvey UniversityofMissouri-Rolla,Rolla,MO65401 MadhavMoganti1 DepartmentofComputerScience FikretErcal2 UniversityofMissouri-Rolla,Rolla,MO65401 DepartmentofEngineeringManagement CihanH.Dagli3 ToshibaCorporation ShouTsunekawa Japan 1 abstract ofthemodernmanufacturingenvironment.inelectronicsmass-productionmanufacturingfacilities,anattemptisoftenmadetoachieve100%qualityassuranceof Inthissurvey,algorithmsandtechniquesfortheautomatedinspectionofprinted allparts,subassemblies,andnishedgoods.avarietyofapproachesforautomated visualinspectionofprintedcircuitshavebeenreportedoverthelasttwodecades. circuitboardsareexamined.aclassicationtreeforthesealgorithmsispresented andthealgorithmsaregroupedaccordingtothisclassication.thissurveyconcen- state-of-the-arttechniques.asummaryofthecommercialpcbinspectionsystems tratesmainlyonimageanalysisandfaultdetectionstrategies,thesealsoincludethe Theimportanceoftheinspectionprocesshasbeenmagniedbytherequirements 2 Introduction isalsopresented. Manyimportantapplicationsofvisionarefoundinthemanufacturinganddefenseindustries. Inparticular,theareasinmanufacturingwherevisionplaysamajorroleareinspection,measurements,andsomeassemblytasks.Theorderamongthesetopicscloselyreectsthemanufacturingneeds.Inmostmass-productionmanufacturingfacilities,anattemptismadeto achieve100%qualityassuranceofallparts,subassemblies,andnishedproducts.oneofthe mostdiculttasksinthisprocessisthatofinspectingforvisualappearance-aninspection thatseekstoidentifybothfunctionalandcosmeticdefects.withtheadvancesincomputers (includinghighspeed,largememoryandlowcost)imageprocessing,patternrecognition,and photomasks,etc.nello[1]givesasummaryofthemachinevisioninspectionapplicationsin articialintelligencehaveresultedinbetterandcheaperequipmentforindustrialimageanalysis.thisdevelopmenthasmadetheelectronicsindustryactiveinapplyingautomatedvisual inspectiontomanufacturing/fabricatingprocessesthatincludeprintedcircuitboards,icchips, electronicsindustry. 01mmoganti@@cs.umr.edu;2ercal@@cs.umr.edu;3c3260@@umrvmb.umr.edu

2 printedcircuitboard(pcb).theysimplyinspecttheworkvisuallyagainstprescribedstandards. Thedecisionsmadebythesehumaninspectorsofteninvolvesubjectivejudgment,inaddition tobeinglaborintensive[2]andthereforecostly,whereasautomaticinspectionsystemsremove Humanoperatorsmonitortheresultsofthemorethan50processstepsrequiredtofabricatea thesubjectiveaspectsandprovidefast,quantitativedimensionalassessments.theseautomatic systemsdonotgettired,donotsuerburnouts,andareconsistentdayinanddayout.applied ateachappropriatestepoftheassemblyprocesstheycanpreventvaluebeingaddedaftera defecthasoccurred,reducereworkcosts,andmakeelectricaltestingmoreecient.allofthis meansbetterqualityatalowercost.overtheyearsmanyresearchers[3,4,5,6]haveemphasized theimportanceofautomaticinspectionsystemsintheelectronicsindustry. boardassembly,andsolderedboardprocess[5,7,8].theincreaseinautomatedproductionline technologyhasrapidlyinitiatedsubstitutesforhumanvisualinspection.thesesystemshave beenproducedwithdistinctandlimitedcapabilitiesforcoveringthefaultspectrumateach ThemajorPCBmanufacturingstagesandprocessstepsinvolvebare-boardfabrication,loaded signicantstageofpcbmanufacture[5].eventodatemachinevisioncommunityconsiders Theproblemsofloaded-boardandsoldered-boardinspectionhavebeenaddressedbuttheresults automaticbarepcbinspectiontobethemostmatureindustrialvisualinspectionapplication. manufacturingenvironment[6,10,11,12,13,14]: criteria,thesophisticationinautomatedvisualinspectionhasbecomeapartofthemodern aretypicallylimitedtodetectionofmorenoticeablediscrepancies[9].duetothefollowing Theyrelievehumaninspectorsofthetediousjobsinvolved. Manualinspectionisslow,costly,leadstoexcessivescraprates,anddoesnotassurehigh Multi-layerboardsarenotsuitableforhumaneyestoinspect. Withtheaidofamagnifyinglens,theaveragefault-ndingrateofahumanbeingisabout quality. 90%.However,onmulti-layeredboards(say6layered),theratedropstoabout50%.Even Productionratesaresohighthatmanualinspectionisnotfeasible. Industryhassetqualitylevelssohighthatsamplinginspectionisnotapplicable. withfaultfreepowerandgroundlayers,theratedoesnotexceed70%[11]. Aspackagingtechnologiesbecomeincreasinglycomplex,substratesbecomemorecostly, Tolerancesaresotightthatmanualvisualinspectionisinadequate. suitableforonespecicapplication.avarietyofapproachesforautomatedopticalinspection Mostvisionsystemsforautomatedindustrialinspectionarecustomdesigned,sotheyareonly hencescrapbeminimized. spectionofbarepcbsisthemostmatureindustrialinspectionapplication,thereisnosingle ofprintedcircuitboards(pcbs)havebeenreportedoverthelasttwodecades.thoughinviewonautomaticvisualinspection[15,16]hasasectiondedicatedtotheinspectionofpcbs. publicationwhichcomprehensivelysurveysthetechniquesstudiedthisfar.themostrecentre- ThisreviewcoversverybrieysomeoftherecentadvancesinPCBinspection,alongwiththe 2

3 madesolelyintheeldofbarepcbvisualinspection.thesignicantimprovementsinthis theprintedwiringboardalgorithms.thissurveyisanattempttoputtogethertheadvances techniquesthatwerepublishedin[17,18,19].sanzandjain[20]presentedagoodreviewof arepresented.oneofthegoalsofthisstudyistocollectmost(ifnotall)ofthearticlesinthis ofpcbsareexamined.thissurveyconcentratesmainlyonimageanalysisandfaultdetection strategies,whichincludestate-of-the-arttechniques.limitationsofcurrentinspectionsystems eldjustifythissurvey.inthissurvey,algorithmsandtechniquesfortheautomatedinspection eldpublishedtodate,toclassifyanddiscussthemaccordingtothemethodologiesemployed. Allofthesewillbediscussedunderaconsistentsetofterminologies(wherevariationswillbe 2.1TypesofInspection mentioned)inthehopethatsuchauniedtreatmentwillbehelpful. PCBawdetectionprocedurescanbebroadlydividedintotwoclasses[12,21]:electrical/contact methodsandnon-electrical/non-contactmethods.electricaltestmethodscanndawssuch asshortsandopens;theothersrequiresomeothermethodsofdetection.eventhoughmany designparameterscanbesuccessfullycheckedbyelectricaltest[22],ithaslimitationsthatcould allowdefectiveproductstopass.potentialdefectssuchaslinewidthorspacingreductionsare inches,thexturesnecessaryfortestingbecomeextremelycomplicatedandexpensive.electrical electricaltestingisverysetup-insensitive.asboardscometobedesignedongridsoflessthan0.1 copperonaninnerlayer,whichmaycausefailureofthenalboard,arealsomissed.further, notdetected,norarecosmeticdefectsorthosecausedbyprocessproblems.defectslikeexcess testing,therefore,augmentsvisualinspectionbutcannotreplaceit.thedouble-linedboxesin Figure1designatestagesatwhichelectricaltestingcaninprinciple,beapplied.Animageofa PCBcanbeacquiredusingvisibleorinvisiblelightandthenanalyzedfordefects.Mostcommon imagingtechnologies.someofthenon-contactautomaticinspectionmethodsthatarecurrently spectrum.thissectionbrieylistssomeofthedierentinspectionsystemsbasedondierent availableare[21,23,24,25,26]: andreliablemethodsreportedintheliteraturehavemadeuseoflightinthevisiblepartofthe AutomaticVisual/Opticalinspection:Automaticopticalinspection(AOI)systemsdetectthesametypeofsurface-relateddefectsasmanualinspection,includingbare-board inspection,solderbridging,lackofsolder,missingcomponents,poorpartorientation,lifted nddefectsotherthanshorts,andopens,suchaslinewidtherrors,padmousebites,and leads,tombstoning,andsolderballs.consideringbare-boarddefectsopticaltesterscan tracemisplacements.thispaperfocusesontheautomaticinspectionofbare-boards.the loaded-boardinspectionsystemscanbefoundinthefollowingreferences[7,27,28].automaticopticalinspectionhasthefollowingcharacteristicsthatcontacttesting(electronic testing)doesnothave[11,29,30]: {Itrecognizespotentialdefectssuchasout-of-specs,linewidths,linespacing,voids, linesburnupoverlongperiodsorunderfairlylargecurrents.also,inhighfrequency circuits,thesedefectsmaycauseleakage,parasitecapacitance,impedanceormutual pinholes,etc.thesearenotalwaystraceablebycontacttestingmethods.narrow atthesystemlevel. inductance.therefore,afterelectronictesting,apcbmaystillnotoperateeectively 3

4 Artwork master Inspection and touch up Production phototools Inspection and touch up Exposure and development of inner layers Inspection and touch up Etching of inner layers Inspection and repair Lamination and drilling Plating through holes Exposure and development of outer layers Inspection and repair Plating tin-lead and Inspection and repair Machine and solder mask Inspection and repair etch {AnAOIsystemisnotconnedbyadesigngridduringinspection-unlikemost electronictestingequipment. Figure1:StagesinMultilayerPCBfabrication {Electricaltestmethodsareexpensivebecauseofthenumberofxturesrequired. {AOIisanon-contactinspection,thusavoidingmechanicaldamage. {AOIcaninspectartworkandprovidesstrictproductcontrolfromtheonsetofproduction. X-rayimaging:X-rayimagingsystems[2,24,30]areusedforrapidandprecisemeasurementofmulti-layeredPCBs.Basedonthemeasurementsofindividualtestpads,the thelayers.x-raysalsorevealminutedefects,suchashairlinecracks,whichescapeother systemsuppliesspecicinformationonlayerregistration,distortionandthetorsionof methodsofinspection.smddefectslikeheelcracking,voids,componentmisalignment, Scanned-BeamLaminography:Laminography[30]providescross-sectionalX-rayimaging leadscanbedetectedusingx-rays. bridging,insucientsolder,excesssolder,solderthreadsandballs,poorwetting,andbent separatedimages.thebasicprincipleoflaminographyistomovethex-raysourceand thex-rayimagedetectoraroundonoppositesidesoftheobject.aslongasthex-ray whichseparatesthetopandbottomsides,oranyotherlayerofthepcb,intocleanly detectorsimultaneously,across-sectionalimageisformedinrealtime.bychangingthe sizeofthex-rayscanningcircle,theeldofviewandmagnicationoftheimagecanbe variedonthey.thisenablesinspectionofne-pitchcomponentsathighmagnication beamalwayspassesthroughthesamepointsintheobjectandthesamepointsinthe UltrasonicImaging:Ultrasonicimagingtechnologybestdetectssolder-jointdefectssuch andofothercomponentsatnormalmagnicationtooptimizethroughput. asinternalvoids,cracks,anddisbands.anultrasonicimagingsystem[31]generatesprecise 4

5 imagesbyscanningafocusedpiezoelectrictransducersignalinarasterpatternoverthe dissimilarmaterialsmaketheirpresenceknownbyreecting(echoing)thehigh-frequency pulses.inpractice,thissystemislimitedtosimplesolder-jointgeometries.forextremely producedbythetransducertoreachthesolderjoints.defectsorthejuxtapositionof solderjoints.acouplinguidallowstransmissionofshortpulsesofultrasonicenergy ThermalImaging:Thermalimagingsystems[24]indicatehotspotsonoperatingPCBs ne-pitchsurfacemountapplications,thereectionandrefractioneectsmayscatterthe pulses,makingdetectiondicult. indicatingshortsandoverstressedcomponents.usuallythesesystemsndsuccessin applicationsinwhichautomatedmeasurementofheatisutilizedtounderstandprocess performanceorinwhichtemperaturemeasurementandcontrolarevitaltoprocessyield. ComparedtoopticalandX-rayinspectionsystems,thermalinspectionsystemsareless automated[30].laserscanningsystems[2]belongtothiscategory.theyaresuccessful 2.2StagesinMultilayerPCBfabrication indierentiatingbetweenpcbcopperandsubstrate.usinginfrareddetectorsdistinctive thermalproleofthedefectivesolderjointsovernormaljointscanbedetected. visualinspectioncanbeapplied. TheactualinspectionconditionsthatexistatdierentpointsofthemultilayerPCBmanufacturingprocess[12,32]areasfollows.Figure1depictsthesestagesalongwiththepointswhere Artworkmasters:Thesearesilverhalide1:1scaletransparenciesoftheconductorpattern willdirectlyaectallensuingproductionbatchesofthegivenpcb.therefore,agreat dealofworkisusuallyputintotheincominginspectionandtouch-upofartworkmasters. areproducedinmostcasesbycomputerdrivenphotoplotters.defectsonthemaster onlm.forveryhigh-qualityproducts,glassmastersarealsoinuse.artworkmasters nishedboardshowthatthesmallestdefectonthemasterortoolwhichistransmittedto Investigationsofthepropagationofdefectsfromtheartworkmasterandphototoolstothe thenishedproducthasdimensionsofapproximately1mil.themostcommondefectson Phototools:Thesearesilverhalideordiazotransparenciesobtainedbycontactprinting productasopens,shorts,andpinholesorcoppersplashes. artworkmastersarecausedbyscratchesanddustparticles.theseshowuponthenished Innerandouterlayersafterexposureanddevelopment:Thesearesheetsofcopper-clad inspectionneedsanddefectsofartworkmastersapplyalsotophototools. fromtheartworkmaster.theyareusedfortheactualexposureoftheboards.the and-repeatthephotoresistprocess.defectscausedbydustandotherforeignparticles Inspectionatthisstageisverybenecialbecauseofthepossibilitiestotouch-uporstrip- thedefectsfoundafteretchingwerealreadypresentasdefectsinthephotoresistpattern. laminate,overcoatedwithphotoresist,withtheconductorpatternexposedonit.manyof duringexposurecanbeaddedtothelistofdefectsmentionedforartworkandphototools. Imperfectrinsingofthephotoresistmayleaveexcessmaterialonthepanel,whichcan createopensafteretching. 5

6 Innerlayersafteretchandstrip:Thisisthepointatwhichmostofthevisualinspection isinvestedinthemultilayermanufacturingcycle,becausethisisthelastinspectionstage beforelamination.afterthispoint,adefectiveinnerlayerinthemultilayerboardisnot repairable.defectsmentionedthusfarcanappearatthisstage,inadditiontosubsequent Outerlayersafteretch:Asinthecaseofinspectionoftheinnerlayersafteretch,thisis copper.exactgaugingofconductorandspacingwidthsisusuallynecessaryatthisstage. defectslikeover-andunder-etch,whichleadtonarrowconductorsorspacingsandexcess thelastpointatwhichrepairscanbemadeontheconductorpattern.theaoiproblem Inspectionaftermachiningandsoldermasking:Thisismainlyacosmeticnalinspection. isdierentatthisstagebecauseoftheappearanceofholesandthenecessityofinspecting theirannularringsforwidthandbreakout. 2.3Defects Defectsintheconductorpatterncanhardlybedetectedatthisstagebecausethesolder maskobscurestheconductors. Printedcircuitboardsareinspectedextensivelybeforetheinsertionofcomponentsandthe processingstepsinvolvedinthevericationofartworkdesign.avarietyofdefectscanaict approachesareusedinthevericationofartwork[33,34,35,36],beforebeginningactualetching solderingprocesstoisolatedefects(alsocalledanomaliesorfaults).eventhoughautomated thecopperpatternofpcbs;notallmeanimmediaterejectionoftheboardfromconsideration. processontheboard,bare-boarddefectsstillexist.hall[33]outlinestheprocessingandpost- Thetypesoffaultsrangefromhair-line(e.g.,sizeequalto100microns)breaksandbridges shorts(bridge),incipientshort(newiring),overetching,underetching(abnormalwirewidth), unetchedcopper,open(breakorcut),partialopen(mousebiteornicks),scratchesorcracks, linewidths,topoorlyformedplatedthroughholes.theanomalieslookedat,forexampleare: assmallas1mmbetweenconductorpaths,tounacceptableenlargementsandreductionsin whiskersorsmears),crackingofwallsofholes,violationsofspacingofholes,violationofspacing padsizeviolations,spurious(excessorresidual)metalormetalspecks,spurs(protrusionsor ofconductortraces,etc.awidevarietyofterminologyisusedinnamingthesefaults.the popular/unpopularnamesinparenthesis. abovelistgivesthecommonlyassociatednamesusedinnamingthedefects,followedbyother dierentpatternsareshowninoneexampleimage.figure2.3showsthesameimagepattern PCBpatterns,printedwiringboardpatterns,andsurfacemountPCBpatternsinthesame image.becausemostofthedefectsarecommontoallthreevarietiesofboards,thethree Figure2showsanarticialdefect-freePCBimagepattern.Thisguredepictsthrough-hole asinfigure2withavarietyofdefectsshowninit.thougheachdefectshowninthegure isarepresentativeexampleforthatparticulardefect,theshapeandsizeofthedefectvaries defectsarecausedduetooneormoreofthefollowingerrors[21,37]: serious,morelikelyandhardertodetect.studies[29,37,38]showthatopen/partialopen, short,pinhole,breakout,overetch,underetcharethemostfrequentdefectsthatoccur.these fromoneoccurrencetoanother.smallerandsmallerlinesandspacesmakethesedefectsmore thermalexpansionoftheartworkduringprinting,orbydefectiveetching, 6

7 dirtonboard,airbubblesfromelectrolysis, Figure2:ExampleofGoodPCBPatterns mechanicalmisregistrations, distortionsofthepcbduetowarping,etc. incorrectelectrolysistiming, thefabricationofpcbs.thedimensionalvariationsintheconductorspacingsandwidthsdue faultsrequireatleast0.5milimagingresolution,thereforedust,hair,lint,andngerprints toseasonaltemperatureandhumiditychangesshouldbetakenintoaccount.further,1mil Thibadeauin[39]givesagoodsummaryofsomedefectsandtheircausesthatoccurduring Althoughitispossibletodetectinitialdefectssuchasconductorbreaksandshortcircuits becomeunwantednoisesourcesforfalse-alarms,makingcleanroomconditionsnecessary[5]. throughconductortests,thesetestscannotrevealoveretchedconductors,limitedconductor spacing,andotherdefectsthatcanleadtodeteriorationwithage[32].inadditiontodefect detectioninspectionofbarepcbsdemands[40]: highspeed, alowfalse-alarmrate. highdetectionaccuracy,and highdatarate, 7

8 1. Breakout 2. Pin Hole 3. Open Circuit 4. Underetch 5. Mousebite 6. Missing Conductor 7. Spur 8. Short 9. Wrong Size Hole 10. Conductors too close 11. Spurious copper Figure3:ExampleofDefectivePCBPatterns 12. Excessive Short 13. Missing Hole 14. Overetch 3Thissectionbrieydenesthemostcommonlyusedterminologyinthiseld.Thereaderisnot providedwithanyrigorousandcompletedenitions.interestedreadersareadvisedtoreferto ComponentsandTerminologyInvolved recentpictureprocessingormachinevisiontextbookstogetacompleteunderstandingofthe individualsubjectsinvolved.thissectionalsoidentiesthemajorcomponentsofaninspection boards,andsurfacemountboards,inthispaperthegenerictermprintedcircuitboard(pcb)is system.thoughthereisdistinctionbetweenprintedcircuitboards(multilayer),printedwiring usedtorefertoallofthem.thisisbecausemostofthedefectsandtechniquesofdefectanalysis arecommonforallofthem.alsoitisworthmentioningthatsomeofthesetechniquesareused inothertypesofinspection[8,20,41,42,43,44,45]likeintegratedcircuitinspection,thicklm analysisinvolvestheprocessingoftheimagerytoenhancerelevantfeaturesandthedetection andhybridcircuitinspection. anomalies.theprocessinvolvesdigitizationoftheobjecttobeinspectedforvisualdataandthe ofdefects.oneinspectionprocedureofsuchasystemrstprecompilesadescriptionofeach Atypicalinspectionprocessinvolvesobservingthesametypeofobjectrepeatedlytodetect ofaknownsetofdefectsandthenusesthesemodelstodetectdefectsinanimage.another proceduremodelsthepartbyitsnormal,expectedfeaturesandthenusesthepartmodelto verifyinanimagethatthepartunderinspectionhasalltheexpectedfeatures.fosteretal.[10] general.thefollowingdiscussionoutlinessomeofthemajorcomponentsinvolvedinautomated andchin[46]outlinedthemajorissuesinvolvedinpcbinspection,andindustrialinspectionin visualinspectionsystems: HardwareSystem:IndustrialPCBvisualinspectionideallyrequiresacost-eectiveo- 8

9 the-shelfsystem.thismeansthatitshouldbedesignedtotakeintoaccountoperation arethematerialandcomponenthandlingsystem,illuminationsystem,imageacquisition speed,reliability,easeofuse,andmodularexibility,inorderthatitcanbeadaptedto dierentinspectiontasks[11].themainhardwarecomponentsoftheinspectionsystem system,andtheprocessor. {MaterialandComponentHandlingSystem:Thissystemcomprisesthemechanism {IlluminationSystem:Suitablelightingandviewingconditionsfacilitateinspection, componentsoftheautomatedvisualinspectionsystem[47]. whichpresentsthepartorassembly,denotedmaterial,indierentorientationstothe pointedouttheimportanceoflightingtechniques[48,49,50].themainparameters avoidingtheneedforcompleximageprocessingalgorithms.manyresearchershave goodqualityare:(a)intensity,(b)uniformity,(c)directionality,and(d)spectral thatcharacterizethesuitabilityofanilluminationsystemtoacquireanimageof prole.therelativeimportanceoftheseparametersandthedegreetowhicheach PCBandtheconstraintsimposedbythecamera.Examplesofdierentsurface onemustbecontrolledarelargelygovernedbythesurfacecharacteristicsofagiven photoresist,(d)redphotoresist,(e)solderbeforereow,(f)solderafterreow,(g) characteristicsinclude[51]:(a)etchedcleancopper,(b)oxidecoatedcopper,(c)blue dierentlightingtechniques.amongthelightingtechniquesmostcommonlyusedare layersthatareopaque,(h)thintransparentlayers,and(i)phototools.mostofthe inspectionsystemsbuilttodateeitherrequiregoodlightingconditionsortheyemploy [37,48,49,50,52,53]:standardlightsources,indirectandbacklighting,uorescent Camera Camera Camera Half-mirror Light source (a) (b) (c) Camera Camera Light Light source 1 source 2 Diffuse Surface Fluorescent filter Figure4:Dierentilluminationtechniques(a)Backlighting(b)Directedlighting(c)Vertical Camera Filter transparent lighting(d)fluorescentlighting(e)bidirectionallightingand(f)diuselighting to ultra-violet rays Dichroic mirror Light source Object Shadow Light Source 9 (e) (f)

10 lighting,reected(vertical)lighting,bidirectionallighting,diuseillumination,beroptic,quartz-halogenlightsources,etc.someoftheselightingtechniquesareshown wasilluminatedwithasuperhigh-pressuremercurylamp,andthereectedlightis anduorescentlightingtechniques.incaseofreectedlightfigure4(c),thepcb experimentation.forexample,haraetal.[54]haveexperimentedwithreected ingure4.anappropriatelightingcongurationisdeterminedbyjudgementand highsensitiveimagetubetypetvcameraisusedtodetectthelightsignals.a detectedbyusingaccdlinearimagesensor.inuorescentlightdetectionfigure 4(d),astheamountofuorescentlightemittedbythebasematerialissmall,a {ImageAcquisitionSystem:Imagesareusuallyacquiredbyuseofacameraora dichroicmirrorisusedtoreectshortwavelengthradiationsalongwithvarioustypes ofltersusedtocompletelyseparatetheexcitationradiationfromthesuperhigh pressuremercurylamp,leavingadequateuorescentlightsignals. camera,etc.aoisystemcorp.developedtheaoi-20systemthatutilizesasmany digitizerthatactsasasensor.thereareaseveraltypesofcamerasavailableand as20ccdcameras[32].themop-5002systemoperateswithoneortwocameras typesaretelevisioncamera(achargedcoupleddevice(ccd)camera),laserscanner thedeterminationoftheappropriatetypeisdictatedbyuse.examplesofdierent whichscanthepcbimagethroughlinearccdsensors,wherehighprecisionlenses guaranteethemaximumpossibleresolutionandawidedepthofeld.everycamera {Processor:Theprocessorsystemusuallyconsistsofahighspeedcomputersystem. hasitsownmicroprocessorsystemjustforcamerafunctionssuchasautomaticfocus, automaticexposure,andautomaticcontrastadjustment.greyscaleprocessorsand realtimedigitizationfacilitiesbreaktheimagedownintoindividualpoints. highspeedparallelprocessingsystem[32].anzaloneetal.[55],implementedtheir inspectionsystemonthesmaemultiprocessorsimd/mimdarchitectureemulator. Mostofthecommerciallyavailablesystemshavespecialprocessorsdesignedsolely forinspectionpurposes.acommerciallyavailableinspectionsystem,aoi-20,usesa Resolution:Anyadequatevisionsystemmusthavesucientresolutiontodetectthe compensatingtechniquesareemployed(multiplecameras,etc.). milsystempixelsize.asmallerpixelsizeusuallymeansasmallereldofvisionifno beatleasttwicethatofthevisionsystem;i.e.,twomilminimumfaultsizerequiresaone potentialfaultsunderinspection.thepixelsizeofthesmallestfaulttobedetectedshould ImageEnhancement:Involvesremovalofnoise,enhancementofedges,enhancementof contrast,etc.thresholding(pointprocessingoperation),convolution(groupprocessing FeatureExtraction:Thedecisionregardingwhatfeaturestobeconsideredisrathersubjectiveanddependsonpracticalsituations.Featuresarelesssensitivewithrespecttothe encounteredvariationsoftheoriginalnoisygray-scaleimagesandprovidedatareduction techniquesusedforenhancementoftheimages[47,56]. operation),andpictureprocessing(processingovertheentireimage)aresomeofthe Model-BasedSystem:Themostcommoninspectiontechniqueisthemodelbasedprocess, whilepreservingtheinformationrequiredfortheinspection.mostoftheproceduresused forfeatureextractionaresimpleedge-detection,linetracing,andobjectshapeproperties. 10

11 Modeling:Modelinginvolvestraining,inwhichtheuserusesamodelparttoteachthe whichperformsinspectionbymatchingthepartunderinspectionwithasetofpredened systemthefeaturestobeexamined,theirrelations,andtheiracceptabletolerances. models. Detection/Verication:Detectionprocessconsistsofmatchingtheextractedfeaturesfrom positionsandorientations.detectionusingrepresentativefeaturesandtheirrelationships verycomplexiftheimagetobeinspectedisnoisyandthefeaturescouldoccuratrandom dureinvolvessimplecomparison,likeimagesubtraction.thedetectionprocessbecomes theimageunderinspectionwiththoseofthepredenedmodel.atypicaldetectionproce- BoundaryAnalysis:Modelsofgoodboundariesarecomparedwiththoseoftheboard fromkeyfeatures.thesemethodsareusuallycomputationallyintensive. provideawaytoinspectapartandlocatedefectsonthebasisofmeasurementstaken Thinning,Contraction,andExpansion:Theseareimage-to-imagetransformationoperations[59,60].Theseoperationsaredenedusingneighborhoodconnectivityrelations.An expansionsetsallbackgroundpixelsinanimagetoforegroundpixelvalue,ifanyoneof result.thinningreducesanentitytoitsskeleton,asimpliedversioncontainedinthe byrstexpandingthecomplementofanimageandthentakingthecomplementofthe originalentitythatretainsthebasicshapeofanentity.unlikeexpansionorcontraction, thinningmaintainstheconnectivity[61]ofanentityandpreservesitsholes(noneare beinginspected[57,58]. theneighboringpixelvaluesisequaltotheforegroundpixelvalue.contractionisrealized Morphology:Thisreferstoabranchofnonlinearimageprocessingandanalysis.The removedoradded).dierentdenitionsandimplementationsoftheseoperationscanbe foundin[62,63,64,65]. completetreatmentofthissubjectcanbefoundinreferences[66,67]. basicideaistoprobeanimagewithastructuringelementandtoquantifythemanner inwhichthestructuringelementts(ordoesnott)withintheimage.theoperations ofdilation,erosion,opening,closing,etc.,areusedinthistypeofimageprocessing.a 4Eduardo[15]hasgroupedtheconventionalvisualinspectiontasksintothreebroadcategories basedonthetypesofdefectstheydetect:(a)dimensionalverication,(b)surfacedetection Algorithms gorithmscouldaswellbeputintothesecategories.sanzandjain[20]classiedtheprinted methods,and(c)inspectionofcompleteness.theconventionalpcbbare-boardinspectional- wiringboardinspectiontechniquesintothefollowingfourdierentcategories:run-length-based orindirectimagerelateddata)ofthealgorithmsuseforfaultidenticationispresentedhere. methods,boundaryanalysistechniques,patterndetectionmethods,andmorphologicaltechniques.aclassicationbasedonthenatureoftheinformation(designspecicationdata/direct AlargenumberofPCBinspectionalgorithmshavebeenproposedintheliteraturetodate, gories:referencecomparison(orreferentialapproaches),non-referentialapproaches,andhybrid Figure5showstheclassicationofthesealgorithms.Ingeneral,theyfallintooneofthreecate- 11

12 Image subtraction PCB inspection Automatic visual inspection X-ray imaging and other technologies Manual inspection Hybrid inspection methods Non-referential inspection Reference based inspection Image comparison techniques Model-based inspection Encoding techniques Template matching Phase-Only method Tree Syntactic Graph matching methods Morphological processing Boundary analysis Run-length encoding Generic method Attributed graph Pattern attributed hypergraph Pattern detection using boundary analysis Figure5:ClassicationofInspectionAlgorithms Radial matching algorithm Learning methods Shape comparison method Circular pattern matching 12

13 approaches-whichinvolveacombinationofmorethanoneofthesemethods.thereferencecomparisonapproachesusecompleteknowledgeofthecircuitundertest,whereasthenon-referential approachesusetheknowledgeofpropertiescommontoacircuitfamilybutnotknowledgeofthe approachesinvolvesomekindofdirectimagecomparison,betweenpixelsinthetestimageand speciccircuitundertest.therearetwotypesofreferencecomparisonmethods:thesimpler inanidealizedreferenceimage.somewhatmoresophisticatedapproachesinvolverecognitionof circuitfeaturesinthetestimagefollowedbyacomparisonagainstasetofreferencefeatures. Thenon-referentialapproacheseitherworkontheassumptionthatfeaturesaresimplegeometricshapesandthedefectsareunexpectedirregularfeaturesorondirectlyverifyingthedesign requireddimensions.thisapproachdoesnotrequireprecisealignment,butmightmisslarge rules.basically,thesemethods,uselocalneighborhoodprocessingtechniquesovertheimageto awsanddistortedfeatures. beinspected.inthesemethods,thetaskistodeterminewhethereachfeaturefallswithinthe 4.1ReferentialModeling Thereferentialmethodsexecutearealpoint-to-point(orfeature-to-feature)comparisonwhereby thereferencedatafromthesurfaceimageofa\good"sampleisstoredinanimagedatabase. Thesemethodsdetecterrorslikemissingtracks,missingtermination,opens,shorts,etc.The drawbackofthismethodisthat,sincedierencesbetweenthepcbunderinspectionanda \goldenboard"orcaddataarecalleddefects,boarddistortions,asaconsequenceofprocessing, maybeidentiedasanomalies[29]. XOR Figure6:ImageSubtraction 13

14 4.1.1ImageComparisonTechniques. ImageSubtractionImagesubtractionisthesimplestandmostdirectapproachtothePCB inspectionproblem.thisisoneoftheearliesttechniquesemployedininspection[68].the PCB.Theadvantagesofthismethodisthatitistrivialtoimplementinspecializedhardwareand showsthisdirectsubtractionprocessasalogicalxoroperationonthesubimagepatternsofthe Thesubtractedimage,showingdefects,cansubsequentlybedisplayedandanalyzed.Figure6 boardtobeinspectedisscannedanditsimageiscomparedagainsttheimageofanidealpart. AfairlyhightoleranceofthePCBboardmakesthemethodtoorestrictiveforpracticaluse. problems,includingregistration,colorvariation,reectivityvariation,andlightingsensitivity. oftheoveralldefectsinthegeometryoftheboard.thistechniquesuersfrommanypractical thereforehighpixelratescanbeobtained.anotheradvantageisthatitallowsforverication Oneotherproblemisthatstatisticalanalysismustbeperformedtodetermineifthedierences areduetononconformitiesorduetoalignment. FeatureMatchingFeaturematchingisanimprovedformofimagesubtraction,inwhich theextractedfeaturesfromtheobjectandthosedenedbythemodelarecompared.the timereducesthesensitivitytotheinputdataandenhancestherobustnessofthesystem.this matchingprocessiscalledtemplatematching[69,70].oneofthemajorlimitationsoftemplate advantageofthismatchingisthatitgreatlycompressesthedataforstorage,andatthesame matchingforinspectionisthatanenormousnumberoftemplatesmustoftenbeused,making theprocedurecomputationallyexpensive.thisproblemcanbeeliminatedifthefeaturestobe matchedareinvariantinsize,location,androtation.thedisadvantagesofthismethodarethat forcomparison.itissensitivetoilluminationanddigitizationconditions,andthemethodlacks itrequiresalargedatastoragefortheidealpcbpatterns,andpreciseregistrationisnecessary exibility.haraetal.[54,71,72]developedathitachiadefectdetectionmethodbasedon featureextractionandcomparison.largedefectsaredetectedbyextractionofboundariesusing HKX;HKY;HK45;andHK?45operatortemplates,showninFigure7(a),inthefourdirections (0o;90o;+45o;?45o).Thesetemplatesareusedfordetectionofalldefectsofwidthgreaterthan axedvalueandforisolateddefects.narrowdefects,likenewiringandwhiskers,aredetected bysearchinginfourdirections(0o;90o;+45o;?45o)usinghby;hbx;hb45;andhb?45operator templates,showninfigure7(b).thenalresultofextractionisalogicalandofthefour directionfeaturesextracted.thesizesofthetemplatesharenotxedandcanberegulated alsobighopscanbemadeusinglargertemplatesizesintheuninterestingregions(e.g.,which bysettinglimitsonthelengths,orientationsandwidthsofthepatterns.thesedierentsizes donothavetracepixels),thusreducingunnecessarycomputationtime. arenecessarytopreciselyidentifytheboundaries,asthetracepatternwidthsmaychangeand showsthetwopatternsf(defectivepattern)andg(non-defectivepattern)thatarecompared;(2) showstheboundaryimagesfkandgkobtainedbyapplyingthehkyoperatorinydirection; and(3)showsthene-lineboundaryimagesfbandgbobtainedbyapplyingthehbxoperator Figure7(c),explainstheextractionofpatternfeaturesanddefectrecognitionprocedure:(1) inthexdirection.defectrecognitioninvolvesthecomparisonoffkandgk(orfbandgb) 14

15 a1 a2 ai H Y p1 b1 b2 bi a1 a2 b1 b2 H X ai bi p1 q3 a1 a2 a7 p3 a8 b1 b2 b3 b4 a9 a10 p3 a1 a2 q3 a9 b4 b3 b2 b1 a8 a16 a7 A fine line is, for example, determined when: a1 =... = a16 and either of b1,..., b4!= ai a1 a2 p2 p2 b1 b2 h BY h BX ai bi H K + 45 b1 b2 a1 a2 H K - 45 p1 = 6 p2 = 4 i = 3 or 4 or 5 ai bi q4 a1 a2 b1 b2 p4 a10 a1 a2 q4 p4 b2 b1 q3 = 3 or 5 or 7 q4 = 2 or 4 or 6 For the conductors ( = w): p3 = 2 or 3 or 4 p4 = 1 or 2 or 3 A boundary is, for example, determined when: a1 =... = ai, b1 =... = bi, for all i and ai!= bi (a) Boundary Extraction Operators h B45 h B-45 (b) Fine-line Extraction Operators For the substrate ( = B) p3 = 4 or 5 or 6 p4 = 2 or 3 or 4 Pattern f and its processed image Pattern g and its processed image Feature Extraction Operators Result of comparison of F and G (1) Detected patterns f, g f g (2) Extracted boundry lined F K, G K in the Y direction F K G K (3) Extracted fine line pattern F, G in the B B direction X F Figure7:LocalFeatureMatchingMethod B G B (c) Extraction of features and comparison of the extracted feature patterns 15

16 images.whenthecorrespondingpointsonthereferencepatternandpcbtestpatternexhibit thesamefeatures,thepatternisfreefromdefects.otherwiseadefectexists.inpart(3)of thecompletesystemcanbeimplementedinhardware.thesystemworkson5:5millinesand 500mil600milboardswithaspeedperformanceof2:5min/panel. Figure7(c),ashort(shownasanarrowline)isdetected.Theadvantageofthismethodisthat Phase-OnlyMethodDavidetal.[73]discussanalternativemethodtostandardtemplate whichhasunitpowerspectraldensityamplitudesothatallinformationiscontainedinthe matchingtechniquewhichisbasedonphase-onlyimaging.aphase-onlyimageisanimage phase.phase-onlyimagecomparisonhasthepropertiesofredundancyremoval(correlation betweendatapointsisremoved)andedgeenhancement.themethodusesfouriertransform, followedbynormalizationoftheresultantimage,tospreadovertheentiregreyscalerange (bydividingeachspectralpointbyitsownmagnitude),andtheninversefouriertransformsan ofdatapointsintheimageareremoved,allperiodiccomponentsoftheimagearesuppressed. Twosimilarimagescanbecomparedbycreatingacompositeimagebyplacingthemside-bysideandapplyingaphase-onlytransformationatonce.Ifthetwoimagesareverysimilar,a imagepairtoproduceamapofsignicantimagedierences.becausethecorrelationsofanypair ofthecompositeimage.bysuppressingthiscomponent,allpointswhichcorrespondtothetwo subimageswillbesuppressed,andonlythedierencesremain.thepaperpresentedexamples strongperiodiccomponentwithperiodequaltothesubimagespacingappearsinthespectrum ofrealandsimulatedimageswithdierentilluminationlevels,lightinggradients,andboard substratecolors,allcomparedwiththesamemasterreference.thismethodhasadvantagesover conventionaltemplatematching/comparisontechniquesbecauseofitslightintensityinvariance, andinvariancetotranslation.themethodsuersfromthedisadvantagethatitrequiresalarge amountofcomputationaltimecomparedtosimpletemplatematchingmethods. insensitivitytoilluminationgradients,tolerancetomisregistrationoftheimagestobecompared, Model-basedmethodsaretechniqueswhichperforminspectionbymatchingthepatternunder inspectionwithasetofpredenedmodels.theselectionofasuitablemodelrepresentationof 4.1.2Model-BasedMethods. thetrainingpatternsstronglyaectstheperformanceofaninspectionsystem.forexample, oneoftheapproachesthatfallsintomodel-basedtechniquesisthesyntacticapproach,also calledstringmatchingtechnique.inthesyntacticapproach[74,75],apcbimageismodeled asanitesetofalphabets/symbols.themethodinvolvestracingtheboundarytoproducean Onemajorlimitationofthissyntacticapproachisthatthechoiceofprimitivesinquantifying orderedlistofboundarypoints,andanalyzingtheshapetoproducesyntacticdescriptionof thebasicshapeinvolvedinthepatternsisadicultproblem.thismakestheapproachnot theshapeusingprimitiveshapesthatbestdescribethepcbpattern.thedetectionofdefects theninvolvesthedetectionoflocaldefectivefeaturesexpressedinniteregularexpressionform. applicableforarealtimeapplicationlikethis. GraphMatchingMethodsThegraphmatchingmethodsarebasedonthestructural,topological,andgeometricpropertiesoftheimage.Theideaisbasedonthetopological/structural comparisonwhichcomparesthestandardgraphobtainedfromtheconductorandinsulatorim- 16

17 informationincorporatesaweightedgraphcomposedofseveraltypesofnodes,edges,connections,andtheirlocation[76].pavlidis[77]presentedatechniqueforconvertingrasterdatainto thisgraphissearchedtoobtainalistoftheboundarypointsoftheregionsandholesinthe image.atechniquebasedonmatchingthelinearadjacencygraphofthetestboardtoamodel alineadjacencygraphdescribingthetransitionbetweentheconductorandthesubstrate.then agepatternsofthereferencepcbwiththoseofinspectionboards.forexample,topological AttributedGraphDarwishandJain[62]proposedamethodthatworksintwomainsteps. graphispresentedasanapplicationtoprintedwiringboardinspection. Intherststep,theimageistransformedintoacollectionofnodesthatdescribesthe2-D jects.spatialrelationsareaddedtothegraphintheformofdirectedattributes,whichdescribes relationalpropertiesbetweenprimitivesbelongingtothesameobjectandbetweendierentob- shapeofthedierentobjectsintheimage.thesenodesareconnectedtogetherdependingon functionisusedtomeasurehowwellthescenegraphmatchesthemodelgraph.experimental andmodelpatternsisthemosttime-consumingstepduringinspection.asimilarityevaluation connectivityandneighborhoodrelationships.thisgraphiscalledanattributedgraph(ag).the secondstepinvolvesamodelvericationprocess.thismatchingprocessbetweentheinspected isovercomebysunandtsai[63]byreducingthelargeamountofunnecessarycomputations joinsthecouplingpermutationateachiterationforeveryattributedrelationship.thisproblem false-alarmrate.butthecomplexityofmatchingagsisverylarge,sinceeverynodeofanag resultsindicated100%detectionofallshorts,cuts,andminimumwidthviolationswithazero PatternAttributedHypergraphSunandTsai[63]presentarepresentationcalledpattern doneinevaluatingscoresbetweenimpossiblecouplesduringtheexhaustivepermutations.the followingsectiondiscussesthismethod. primitivefeaturesconnectedtooneanotherwithinaregion,whichisthebottomlevelofpahg. attributedhypergraph(pahg)andastructuralinspectionalgorithm.theproposedgraph, ThetoplevelofPAHGcontainsregionalfeaturesandthespatialrelationsamongthem.This calledpahg,describesallsegmentedregionsandthespatialrelationshipamongthem.these representationprunesthesearchspacebyperformingonlyselectivematchingoperationsduring segmentedregionsarerepresentedbyaregionalattributedgraph(rag)thatrepresentsasetof theinformationisrepresentedintwodierentlevels,isamajorimprovementovertheattributed thematchingphase,therebyreducingtheinspectiontime.thisnewrepresentation,inwhich thenlabelingtheprunedpattern.figure8(a)isthinnedtoobtainfigure8(b).figure8(c)isthe graphmethod.figures8(a),8(b)and8(c)showallthestepsinvolvedintheconstructionof thinnedimageusingthepruningoperationinordertoeliminatespuriouseectsinthinningand thebottomlevelofpahg.thisstepinvolvesthinningofthebinaryimage,thensmoothingthe labeledgraphobtainedafterpruningthefigure8(b).figure8(d)showstheragconstructed forthesub-patternofthepcbpatterna.figure8(e)showsthepahgforthecompletepcb and(c)verifyingeachragofthescenemodelwiththecorrespondingragofthereference sub-patternshowninfigure8(a).thematchingalgorithmproposedworksby(a)verifying model.anewinspectionalgorithmwasproposedtoutilizethehierarchicalstructureofpahg thetoplevelofpahgonthescenemodelandreferencemodel,(b)ndingthecorresponding pairsofragsbyevaluatingthecondencescoresbetweentwopahgsandthepairofrags, 17

18 A 1 C 2 B 1 C 1 A 2 C 3 B 2 A 3 B 3 C 4 C 5 A 4 (a) PCB sub-pattern (b) Thinned sub-pattern A 5 (c) Pruned sub-pattern with lables A A A A A Right-Connect Right-Connect Right-Connect Right-Connect Right-Connect Left-Connect Left-Connect Left-Connect Left-Connect Left-Connect NULL NULL NULL NULL NULL NULL (d) Representation of RAG for segment A in Figure (c) NULL B up down right left NULL A up down right left (e) Graphical representation of PAHG for the sub-image pattern (a) C up down right left NULL A 1 A 2 D 2 D 1 C 1 B 1 A D 3 C 3 D C B B 2 (f) Pruned defective PCB D 2 pattern with lables C Figure8:GraphMatchingUsingPatternAttributedHypergraph D B3 B 18 4 (g) PAHG for (f) 4 5

19 toimprovethematchingeciency.thematchingcomplexityofthismethodis1=k3ofthatof theagapproach,thusmakingthemethodmorepractical. 4.2Non-ReferentialInspection Non-Referentialmethodsdonotneedanyreferencepatterntoworkwith,theyworkontheidea thatapatternisdefectiveifitdoesnotconformwiththedesignspecicationstandards.these [21,63,64].Theybasicallyusethedesign-specicationknowledgeinverifyingtheboardtobe methodsarealsocalleddesign-rulevericationmethodsorgenericpropertyvericationmethods thedesigncharacteristicsofapcbasasimplesetofrulesandfeaturedimensionsandtolerances. inspected.applyingthedesign-rulevericationprocessdirectlytotheimagepatternsisatime Featuresspeciedinclude[78]: process/transformtheimageintoaformwhichreducesthevericationtime.themethodsuse consumingprocess,andhencetheresponsetimeofthesystemdecreases.usuallythesemethods Minimumandmaximumtracewidthsforallthedierenttracesused, Minimumandmaximumcircularpaddiameters, Minimumconductorclearance, Minimumandmaximumholediameters, Ejiri[60]developedtheclassicexpansion-contractiontechniquethatassumesdefectsexistina highrst-orderspatial-frequencydomain(viz.,patternsthataresmallrelativetotheacceptablepatterns).expansion-contractionmethodsemploymorphologicaloperationslikeerosion, Minimumannularrings,traceterminationrules,etc. applyingtheseoperatorsdirectlyreectsthediscrepanciesintheimagepatterns,ifanyexist. designedinsuchawaythattheyembedthedesignspecicationsinthemandtheresultof dilation,expansion,contraction,thinning,etc.,inthepre-processingstage.theoperatorsare byextractingthetopologicalfeaturesandimposinglocalizedconstraintssuchasminimumor theimagepatternsandthevericationphaseinvolvesinterpretingthesetransformedpatterns generateimagesthatcouldeasilybeinterpretedfordefects.encodingtechniquesalsotransform Design-specicationinformationisembeddedintheseoperators,suchthatthetransformations thattheyworkwellinidentifyingonlysomekindsofdefects,suchasinthevericationof widthsandspacingviolations.also,anotherdrawbackofthisinspectionisthatitrequiresthe maximumwidthstodetectanomalies.thedisadvantageofthesenon-referentialmethodsis standardizationoftheconductortracetypes[11],forexample: Conductortracesmustbeseparatedbyaminimumpermissiblespacing. Conductortracesmusthaveaminimumpermissiblewidth;and Conductortracesmustendatsolderpads; maymissawsthatdonotviolatetherules,suchasshortsthatareidenticaltoconductors. However,speedismaximizedandcomputerstoragerequirementisminimized. Thesenon-referentialmethodsdependonsophisticatedfeaturerecognitionalgorithmsand 19

20 4.2.1MorphologicalProcessing. MorphologicalprocessingisoneofthewidelyusedtechniquesinPCBinspection.Theinspection involvestheexpansion-contractionprocess,whichdoesnotrequireanypredenedmodelof perfectpatterns.yeanddanielson[61]presentedanalgorithmforverifyingminimumconductor andinsulatortracewidths.themethoditerativelyappliesshrinking(similartocontraction operation)andconnectivitypreservingshrinking(similartothinning)operationsontheimage. Aftersomenumberofiterations,thedierence(logicalAND)betweentheresultsgivesthe defectspresentinthepatterns.themainadvantageofthesemethodsisthatthealignment problemiseliminated.but,theproblemwiththesemethodsisthatdierentpre-processing algorithmsaretobeappliedtocheckdierentviolationsintheboard,whichautomatically decreasestheresponsetimeofthesystem. Board Type Trace Type Intermediate Type (a) PCB sub-image (b) After format filtering (a) Figure9:ExpansionandContractionFiltering lterandthentheconnectivitythroughthecircuittraceischecked.theformattinglter classieseachpixeloftheobservedcircuitboardintooneofthreetypes:tracetype,boardtype, methodgivenbymandeville[64].inthismethod,theimageisrstenhancedbyaformatting Grinetal.[79,80]discussaninspectionalgorithmwhichisavariationoftheshrinking (c) Defective PCB sub-pattern (d) After filtering (c) orindeterminatetype.apixelisclassiedatrace(board)typeifitissurroundedbyacircle thenatthatpointthetrace(board)satisesminimumtrace(board)requirement.pixelswhich oftrace(board)pixelswithaminimumradius.ifthisradiusisequaltoaspeciedminimum, 9(a)showsaPCBsub-imagewhoseoutputpatternwouldlooklikeFigure9(b)afterformat arenotclassiedaseithertracetypeorboardtypeareclassiedasindeterminate.figure 20

21 spacingsandsurfacenonconformitiesonthecircuitboards.figure9(c)showsadefectivepcb outputpatternlookslikefigure9(d)afterformatltering.opens/partialopensareidentiedby sub-image,whichhasamousebite,wrongsizeholeandconductortooclosedefects,andwhose ltering.thisclassicationprovidesameanstocheckforopen/partialopens,minimumtrace checkingforconnectivityalongthetrace,wherefailureofminimumwidthrequirementindicates abreakintheconnectivity.minimumspacingrequirementsarecheckedbyverifyingifthereare anyoftheindeterminatepixelsofonetraceconnectedtoindeterminatepixelsofanothertrace, likescratchesanddustareinspectedafterthealgorithmforwidthandspacingrequirementshas ifanyexist,thentheminimumspacingrequirementsarenotsatised.surfacenonconformities beenperformed.thesenonconformitiesareidentiedtobetheareasofhighintensitypixelsby sourceisatanacuteangletotheboard. subtractingthemetaltracepixelsfromtheimagewhoselightingcongurationissuchthatthe minimumlandwidthrequirement(mlw),violationofminimumconductorspacingrequirement imagetransformationsbasedonmathematicalmorphology.thesystemdetects:violationsof (MCS),andtheviolationofminimumconductortracewidthrequirement(MCTW).Thefundamentaloperationsusedinthetransformationsarehit/misstransformation,erosionoperationitalimagesasshowninFigure10(a):substratepixelswithvalue0,conductingstructurepixel valueswithvalue1,andholeswithvalue2.asegmentationalgorithmwhichseparatesthe systemtoapplydesign-rulecheckingeasilyandthusavoidingfalse-alarms.thefollowingsteps conductorlandssurroundingtheholesfromtheconductortracesisemployed.thisenablesthe depictthealgorithm: Thesystemproposedby[81]makesuseofdefectdetectionalgorithmswhicharederivedusing dilationoperation,andsymmetricalthinning.thepcbimagesaresupposedtobe3-leveldig- 2.theholelocationsareenlarged,asshowninFigure10(c),suchthattheycoverthesurroundinglandsusingdilationoperation. 1.theoriginalimageistransformedusingthefollowingrule0?>0,1?>0and2?>1. Figure10(b)showstheresultantbinaryimage. 3.transformtheoriginalimagebytherule0?>0,1?>1,and2?>0.Figure10(d)shows 4.Imagesobtainedinsteps2and3areANDed.Theresultantimageafterthisoperationon theresultantbinaryimage. 5.Imagesinstep3and4areEXORed,resultingtheconductortraceimage,asshownin Figures10(c)and10(d)isshowninFigure10(e). caneasilybeunderstoodwiththehelpoffigure11,whichdepictseachstepintheprocess: Algorithmforverifyingminimumconductorspacing(MCS)worksasfollows.Thealgorithm Figure10(f). 2.theaboveimageissymmetricallythinnedandprunedtoremovehairlikeprotrusions.The 1.dilatetheoriginalPCBimageFigure11(a)byanisotropicstructuringelement(anelliptic one).theresultantimageisshowninfigure11(b). thisstep. resultantimageisoredwiththeoriginalimage.figure11(c)showstheapplicationof 21

22 0 = Board Type 1 = Trace Type 2 = Hole Type (a) Original PCB sub-pattern (b) After (1 -> 0) and (2 -> 1) operation (c) After dialation operation Figure10:SeparatingConductorSurroundingHolesfromOtherConductorTraces (d) After (2 -> 0) operation (a) (e) After AND oepration (f) After XOR operation (c) and (d) (d) and (e) 22

23 3.theoriginalimageisEXORedwiththeimageobtainedinthepreviousstep,thusobtaining defectivepatternsasshowninfigure11(d). Figure11:VericationofMinimumConductorSpacing andtablelookupoperationsasameanstoimplementmorphologicaloperations.themain advantageofmorphologicaloperationsisthattheyaresimpleandeasytoimplementinhardware algorithmtospeed-upthecompleteprocessispresented,whichmakesuseof2-dconvolution SimilaralgorithmsarepresentedforverifyingMLWandMCTWrequirements.Also,afaster (a) (b) (c) (d) [15,60,64] EncodingTechniques. BoundaryAnalysisTechniquesBoundaryanalysistechniquesstudiedarebasedonthe Westetal.[82,83]implementedaboundaryanalysistechniquetodetectsmallfaultsbyusing representationoftheboundariesinatractableform,followedbyarulevericationprocedure. Freemanchaincoding[84]todescribetheboundaries.Smallfaultsaredenedasthosefeatures thatcaneasilybedistinguishedfromtheconductorpatternsbecauseofthepresenceofcertain characteristicsnotnormallyfoundonboards.freemanchaincodingtranslatestheboundary ofapatternintoapolygonalapproximation.thisapproximationtendstoeliminatesome digitizationandthresholdingnoisefromrepresentationdataatthecostofsomesmallfeatures ofpotentialdefects.eachlinesegmentinthepatternisoneofeightpossiblevectorsofeither 1.0or1.4142resolutiondistanceslongandatincrementalanglesof45degreesasshownin Figure12(c).Themethodworksinthreestages:(i)ItcomparestheEuclideandistanceandthe 23

24 Chain Code: Curvature code: Changes in dir.: Duration: Extracted corners: (a) (b) (c) (d) A Changes in direction: (e) Duration: (f) Distance from A: (g) (a) Example of a small fault shape (b) Corner extraction on image in Figure (a) (c) Chain code directions C c F b B a b F B a F B b c D E D E (1) Nick/Bump A C E A A a Figure12:FaultDetectionusingChainCodingTechnique (2) Nick/Bump (3)Break/Short (d) Fault shapes detectable by small fault detector C 24

25 codesegmentsapart.itworksontheassumptionthat,foranormalboundary,thedierencewill boundarydistancebetweentwopointsontheboundarythatareaconstantnumberofchain areextractedfromtheboundaries,usinganiterativecornerdetectionalgorithm.thiscorner detectionalgorithmmakesuseofdierentialchaincodes(curvaturecodes),whicheliminatethe detectedasfaultyarepassedtothesecondstageofinspection.inthesecondstagethecorners besmall,butforadefectiveboundarythedierencewillbelarge.theseboundarieswhichare makingthesharpcornersmorevisibleintheprocessedcornerdata.thechangesindirectionare orientationproblems.initiallyadjacentcurvaturecodesthathavethesamesignarecombined indirection.thisgroupingiscontinueduntilnofurthergroupingcanbedone.finallyall istogroupthelikesignedchangesindirectioniftheyareseparatedbyonlyonezerochange denedasthetotalchangeindirectionoveradjacentcurvaturecodes.thedurationisdened zerochangesindirectionareeliminated,asthiscornercombinationissucienttodiscriminate asthenumberofcurvaturecodesoverwhichthechangeindirectionoccurs.thenextstep dierentfaults,likenicks,bumps,etc,usingthesignofthecodes.theseedgecornerson theboundaryareprocessedusingthreedierentcornerfaultmodelsbytraversingalongthe boundariesinaclockwisedirection.lines(a)and(b)infigure12(b)arethefreemanchain codesandcurvaturecodesrespectivelyfortheimagepatterninfigure12(a).thelines(c) through(g)infigure12(b)showtheextractedcornerswiththechangesindirection,duration, 12(a). distanceinformation.thefollowingareexamplecornerfaultmodelsforthefaultinfigure (1)Fourcornerfaultmodel: (4.1)Combination+*-*-*+or-*+*+*-. where(-)isanegativegoingcorner (4.3)E<12.0 (4.2)a<10.0andb<7.0andc<10.0 (*)isanoptionalcornerofanydirection (+)isapositivegoingcorner (2)Threecornerfaultmodel: (4.4)F-E>THRESHOLD. (3.4)F-E<THRESHOLD. (3.3)E<12.0 (3.2)a<15.0andb<15.0 (3.1)Detectionofcorners+*-*+or-*+*-. (3)Twocornerfaultmodel: (2.1)Detectionofcorners+*********+or (2.4)F-E>8.0 (2.3)E>7.0andE<14.0 (2.2)b<14.0 -*********-. nickswith(+--+)andbumpswith(-++-).the\*"indicatesthatacornerofanysensemay Thesignofthecornersisusedtodiscriminatebetweendierentkindsofdefects,forexample, 25

26 distanceandtheboundarydistancebetweentwopointsonthecornermodelsarecalculatedfor appearbetweentwocornersallowingsomeexibilityinthefaultshape.againtheeuclidean obtainedinstagetwoiscalculated.severityisdeterminedbymeasuringtheminimumtrack of1.5isrecommendedasathresholdvalue.(iii)instagethreetheseverityofthefaults ltering,steps(4.4),(3.4)and(2.4).theprocessingcanbestoppedatthisstageandgood widthofafaultandthedepthofthefault.thetrackwidthismeasuredalongnormalstothe resultscanbeobtainedwithathresholdvalueof2.3.ifprocessingiscontinuedavalue ysesbothverticalandhorizontalhistogramsofrun-lengths.themethodcountscontinuousruns Run-LengthEncodingTherun-lengthencodingtechniquedevelopedbyThibadeau[39]anal- boundarybetweentheoutermostcornersofthefaults. histogramreectsveryshorthorizontalrunsalongahorizontaledgeorverticalrunsalonga oftracepixelsalongeachrowandcolumnofthepcbimageandconstructsahistogram.this verticaledge.alsoline-widthoftheconductorsgetsreectedinthehistogramwhichisuseful [85,86]determinesthepositionoftheedgesoftheconductoroneachscanline,whichprovidesa todetectaws.theconductorminimumwidthrequirementisveriedbycheckingifrun-length ofpixelsisshorterthanathresholdvalue.thissystemwasoperationalat10millinepanels withaspeedperformanceof4megapixelspersecond.sterling'srun-lengthencodingmethod convenientwaytolinktheinformationonascanlinetothepreviousscanlines.theinspection minimumandmaximumconductorwidth.thespeedperformanceofthesystem,operatingat1 topologicalfeaturesandthedetectionofanomaliesbyimposinglocalizedconstraintssuchas processinvolvesthetrackingofregionsfromonescanlinetootherscanline,theextractionof tobeimplementedinhardware.althoughthefeaturesextractedneednotchangefordierent boardstyles,itisnecessarytochangetherulesgoverningthefeaturestobeweightedagainst milresolutionandoverboardsof450mil600mil,isabout4minutes.themainadvantage ofthistechniqueisthatiteliminatestheneedforprecisealignmentandenablestheprocess 4.3HybridInspectionMethods oneanotherinordertodecidewhetherarealdefecthasbeendetected. Thehybridaw-detectiontechniquesincreasetheeciencyofthesystembymakinguseofboth referentialanddesign-ruletechniquesexploitingthestrengthsandovercomingtheweaknessesof landwidths,spacingviolations,defectiveannularringwidths,angularerrors,spuriouscopper. eachofthemethods.thesemethodshavetheaddedadvantagethattheycoveralargevariety ofdefectscomparedtoeitherreferentialornon-referencemethodsalone.forexample,most ofthedesign-rulevericationmethodsarelimitedtoverifyingminimumconductortraceand Printedcircuitboarderrorswhichdonotviolatethedesignrulesaredetectedbyreference comparisonmethods.thesemethodscandetectmissingfeaturesorextraneousfeatureslike features;thecomparisonmethodsareequallysensitiverightuptothelargestfeatures.figure13 isolatedblobs,etc.thedesign-ruleprocessdetectsalldefectswithinsmallandmediumsized depictstheperformanceofboththesemethodsbasedonthesizeofthefeatures.hybridsystems otherandthereforeachieve100%errorsensitivity,irrespectiveoffeaturesizesontheprinted makeuseofboththedesign-rulemethodsandcomparisonmethodsastheycomplementeach circuitboards. 26

27 Error sensitivity Reference Comparison Figure13:ComparisonbetweenDesign-RuleandComparisonmethods 4.3.1GenericMethod. Feature size Thegenericmethodisacombinationofreferentialandnon-referentialinspectionalgorithms. AsMandevilleexplainsin[64],itisasynthesisofreference-comparisonandgeneric-property approaches.themethoddoesnotcompareareferenceimageandthetestimagepixel-by-pixel, ofareferenceimagewiththetestimage.instead,themethodcomparesasmalllistofpredicted iteliminatestheneedforthestoragerequirement,generation,registration,andthecomparison featuretypesandlocationswithalistofdetectedfeatures.thismethodisamajorimprovement connections,isolatedblobs,holes,etc.mostofthefalse-alarmsthatcanoccurindesign-rule thatlookslikegoodfeatures.unlikemostdesign-ruleapproaches,thismethodisnotlimitedto verifyingjustminimumconductortracewidthandspacing;italsoveriespads,varioustrace overdesign-ruleapproachesbecauseitcandetectmissingfeaturesandextraneouscircuitization typicalcircuitfeaturescanbeinferredfromthecorrectnessofskeletalversionsofthecircuit expansion,etc.theobservationthatthelocalgeometricandglobaltopologicalcorrectnessof approachesareovercomeinthistechnique. featuresinatestimage,isusedintheanalysisoftheprintedcircuitpatterns.themethod Themethodmakesuseofimage-to-imagetransformoperationslikecontraction,thinning, worksasfollows: transformtheimagetoobtainaskeletalimagefromwhichdefectsandgoodcircuitfeatures comparethedetectedfeaturelistwithadesignfeaturelistgeneratedfromcircuitdesign caneasilybedetected, data,and 27

28 typicaldefects.figure14(a)showsthesejoins:whereann-joinisanonzeroelementwithn Thefactthatthepresenceof0-,1-,T-andblob-joinsissucienttoinfertheexistenceof conictingfeaturesimplydefects. ablob-joinisaskeletalelementwithan8-neighborthatisnotaskeletalelement.inthe Figure14(a),Xisblob-join,sisaskeletalelement(anonzeroelementnecessarytomaintainthe nonzero8-neighbors(0n8);at-joinisa3-joinwhose8-neighborsareskeletalelements; versionoftheimagewiththebasicshape.the4-and8-thinningoperationsremovesthe connectivityofits8-neighbors),andbisaboundaryelement(anonzeroelementwithazero 8-neighbor).Thinningreducesaconnectedsetofonesinanimagetoitsskeleton,asimplied elementsineachiterationwiththefollowingconstraints:(a)theglobalconnectivityofentities ismaintainedandtheholesarepreserved;(b)ateachstep,4-thinningremovesonlyelements withazero4-neighbor,whereas8-thinningremovesallelementswithazero8-neighbor.the verifyingpadposition,area,shape,andtrace-to-padconnections. detectingexcessivetracewidth,verifyingminimumspacinganddetectingshortcircuits,and methodcanbeusedinverifyingminimumconductortracewidthanddetectingopencircuits, Wisthenominaltracewidthandwistheminimumacceptabletracewidth,lessthanW.The algorithmworksonthebinaryversionofthetestimageasfollows: Algorithmforverifyingminimumconductortracewidth(MCTW):Supposethat 8-thin(W2?w2)timestheimageobtainedinpreviousstep.Figure14(d)depictsthe alternately4-and8-thinthebinaryimage(w2)times.figure14(c)depictstheresultof 8-thinnedoutputofFigure14(c). applyingthisoperationontheoriginalpcbsub-patterninfigure14(b). comparethedetectedfeaturesinpreviousstepwithdesignlist: detect1-andblob-joinsinthinnedimageobtainedinpreviousstep. {if1-joinsisnotindesignlist,thisimpliestracewidthviolations.thesquareboxes {if1-andblob-joinsindesignlistarenotindetectedfeatures,thentheimageismissing infigure14(e)are1-joins,whichimpliesthepresenceofdefects(open). Eachofthealgorithmspresentedinthepaperuseadierentthinningprocesssuchthata particularclassinducesaknowncorrespondingclassofskeletalfeaturesthatcaneasilyand thesefeatures. reliablybedetected.thetechniquespresentedhereareamenabletohigh-speedimplementation inpipelinearchitecturesinwhicheachprocessingelementofthepipelineisinchargeofthe executionofamorphologicaloperation.mandevilleclaimsthatbyusing150elementpipeline, theinspectionofapanelof500mil600milcanbeaccomplishedin30secondsataresolution of0:5mil/pixel. TheinspectionsystemproposedbyBenhabibetal.[37]usesahybridaw-detectiontechniquebasedonpattern-detectionandboundary-analysistechniques.Forconductoraws,the 4.3.2PatternDetectionusingBoundaryAnalysis. 28

29 Fox X = X X X X X X: 0-join X: 1-join X: 2-join X: 3-join X: 4-join Fox X = X X X s s s X b b b b b b b X: T-joins X: blob-join s: skeletal element b: boundary element (a) n-joins; T-joins and blob-joins a a a a c c c c b (b) Defective image Figure14:VericationofMinimumTraceWidth b b (c) After contracting n-times (d) After thinning m-times (e) With joins identified b 29

30 non-standardedges,whichareanalyzedbyapattern-detectionsystemtomeasureconductor boundary-analysisalgorithmlocatesareasthatcouldhavepotentialaws,thesearemarkedas widths.thusthistechniquesignicantlyincreasesthespeedofthepattern-detectionalgorithm byisolatingtheconductormeasurementsonlytothoselocationsthatcouldbeaws.similarly,a pattern-detectionalgorithmmeasuresland-widthsforholeaws,afterlocatingtheholecenters usinganimagesubtractiontechnique.flawanalysisforconductorsinvolves: Edgedetection:wherefouredge-pixeltemplates,showninFigure15(a),areusedtodeterminewhetherthepixelsinawindowbelongtoanedgeofaconductorintheimagedardornon-standardbasedonasetofhorizontal,verticalanddiagonaledge-templates, Non-standardedgepixeldetermination:whereedge-pixelsareclassiedaseitherstan- Edge-normaldetermination:wherethreedierentoperators(T;Y;I),showninFigure asshowninfigure15(b).anedge-pixelthatdoesnotmatchanyofthetemplatesis 15(c),areusedtodeterminetheedge-normalsofnon-standardconductoredge-pixels.First consideredtobeapotentialawlocation,hencemarkedasnon-standard. thentheedge-normalisinthedirectionindicatedbytheoperatorbase.whenthisoperator thet-operatorisappliedandifeachpixelunderthisoperatorisclassiedassubstrate, Flawdetectioninvolves:(i)thenon-standardedge-pixelanditscounterpartontheoppositeedgeoftheconductorareexaminedtodeterminewhethertheybelongtoaland determineifthereexistsaaw,(iii)thepin-holesizeiscompared,asapercentagewitha speciedmaximumvaluetodetermineifthereexistsaaw,(iv)theinterconductorspacing oraconductor,(ii)theconductorwidthiscomparedwithaspeciedminimumvalueto theselocations.whenbothoperatorsfail,thei-operatorisapplied. fails,usuallyatinternalsquarecornersofconductors,they-operatorisnextappliedat bytracingfromthecurrentnon-standardedge-pixeltotheoppositeedge-pixelalongthe edge-pixelofthenextconductorislocated.thisiscomparedwithaminimumspecied ismeasuredbycountingsubstratepixelsintheopposite-normaldirectionuntiltherst edgeoftheconductor.ifthetracesucceedswithinaspeciednumberofedge-pixels,there existsaconductorbreak. valuetoverifytheexistenceofaaw,and(v)aconductor-break-detectionisperformed Shiaw-ShianYu,et.al.[11]proposedandimplementedatechniquebasedonradialencoding intohardwareforhighspeedinspectionofartworkandbare-boards.thebasicprincipleisthat 4.3.3Circularpatternmatching. tobottom.atemplatecomparatorisusedtoperformtheencoding,whilethedefectdetection logicisusedtoverifythecodestojudgeifthecodesarecontradicting.adefectlocationrecorder animagewindowofsize3232ismovedoverthepcbimagefromlefttorightandfromtop recordsthedefectlocationsandtotalnumberofdefectsinthatarea.thedefectdetectionlogic worksasfollows.todetectifatracewidthisassmallaswidthd,acirclewithdisdrawn, withthecenterofthecircleonthetraceedge.ifthecircleisdividedintotwoareas,thenitisa normaltrace;otherwise,thetracewidthissmallerthandorcertainotherdefectsexists.figure 16(a)showsatracewidthsmallerthand.Figure16(b)-(d)showtheabilityofthismethodto 30

31 (a) Edge pixel templates Horizontal Edge templates Vertical Edge templates Diagnol Edge templates (b) Edge templates n n n T Operator Figure15:TemplatesUsedinthePatternDetection Y Operator I Operator (c) Edge normal determination operators 31

32 d d d (a) (c) (b) diagnoseotherdefects.thismethodisnotsensitivetotracedirection,butdefectslikefigure d 16(e)-(f)cannotbediscovered. Figure16:DefectdetectionusingTemplateT2 d d (d) (e) (f) Sincethelinesmustendatthesolderpads,whicharegreaterinsizethanthelinewidth,thelines Todiagnoseopencircuitsorshortcircuits,theextensibilityofthelineorsubstrateisinspected. Figure17:16Extensiondirections inlocalareas,mustmaintaingoodextensibility.figure17showsthedirectionsofextension, altogether16,eachdividedbyanangleof22.5degrees.thesmallconcentriccircleinthemiddle isusedtopreventinspectingthelineedgesandmakingincorrectextractions.theextensibility criteriaisthatallpixelswithinadistancer,fromthecenterofthecircleinthatdirection,have thesamecolor.figure18(a)-(c)showlinesthatsatisfyextensibility.figure18(d)isanopen circuit,figure18(e)isashortcircuit,bothofwhichdonotsatisfyextensibility.itshouldbe notedthatthemethodreliesonthejudiciousselectionofthevaluer. pixelsoncircleperimetersandlinesegments.themethodassumesthatthestandardlinewidth is10pixels.forexamplet1,t2,t3,t4andt5templatesareusedasfollows: TemplatesT1throughT5showninFigure19,areusedasbasictemplatesforanalyzingthe T1isusedtodecideweatherthepresentwindowscenterisatapointontheedge.The 32

33 (a) (b) (c) Figure18:ExtensibilitytestonaConductortraceorSubstrate (d) (e) T1 T2 T3 Figure19:Templatesusedinthepatternmatching T5 T4 33

34 Whenthewindowcenterislocatedontheedge,T2isusedtocheckforasituationwhere regions. edgepointmeansitisonconductoranditsneighboring8pixelsaredividedintotwo T3isusedtocheckifthecolorsofpixelsinthewindowarethesame.T3alongwith thelinewidthissmallerthan6pixelsandthelinespacingissmallerthan5pixels.itis alsousedtodiagnosepinholes,copperspecks,mousebites,extrusionsandotherdefects. AfterT3hasbeenusedtocheckthelinewidthsandlinespacingstobesmallerthan10 canalsodiagnosepinholesandcopperspecks. templatet2isusedtoinspectthelinesorspacingsofwidthsmallerthan10pixels.this T5isusedtomeasureextensibility.Tosatisfyextensibilityinacertaindirection,allthe pixelswithinacertaindistance,fromthecenterofthetemplatet5,arecheckedforpixels pixels,t4isusedtomeasuretheactuallinewidthorspacing. ofthesamecolor.thesmallcircleinthemiddleofthetemplatet5isusedtoprevent patterns.itperformsataspeedof4106pixels/secondat0.5milresolution. Thesystemdetectsopenandshortcircuits,pinholes,overetchandunderetchoftheconductor inspectingthelineedgesandmakingincorrectextensions Learningmethods. RadialmatchingalgorithmTheFVIS-110systemdevelopedbyFujitsu[15,32,87]usesa radialcodeself-learningmethod.inthismethodtheusersinputlearningsamplepatternsfrom radialcodes.radialcodesassumethreeconcentriccirclesaroundthecenterofthepcbpatterns; goodpcbboards,andthesystemconvertsthesepatternstocharacteristicfeaturesknownas codesforthelocationsoftheedgesofeachsub-patterndependonwhichcirculardomainitfalls infourdirections,45degreesapart.eachmeasuringlinehasapairofsensorsthatmeasurethe withineachofeightdirectionsasshowninfigure20.thepatternlengthsaremeasuredradially distancefromthecentertothepatternedges.theoutputofeachsensorpairisthencheckedfor themaximumwidth,andlengthofthemeasuringsensor.thesystemassignscodesconsisting exceedthepredeterminednumber.thelengthisdividedintofourareasbytheminimumwidth, equivalentlengths.thepatterncenterisdenedwherethenumberofequivalentsensorpairs ofs(shorter),c(correct),l(longer)andov(over)foreachpointontheline.ovindicates thedirectionofthepatternline.thepatternwidthisperpendiculartothedirection.whenthe patternisnormal,thewidthiswithinthecarea.forexample,forthelinepatterninfigure sensorpairshaveanequivalentlengthfromthecenter. 20(a),the0-degreemeasurementisC,45-degreemeasurementisL,90-degreemeasurementis OV,and135-degreemeasurementagainisL.Theradialcodeis(C,L,OV,L),andallofthe codeis(c,l,ov,l).butifthereisashortasshowninfigure20(b),the0-degreelengthis Figure20showsthedetectionofshortinaPCBsub-pattern.Foranormalpatterntheradial 34

35 OV L C S Minimum width Maximum width OV,andthecodechangesto(OV,L,OV,L).Thesedefectivecodeswillbedetectedatmany Length of sensor points,becauseinspectionisperformedateverycenterpixel.thesystemcountsandmemorizes Figure20:Radialmatchingalgorithm(a)Perfectpattern(b)Shortdetection thefrequencywithwhicheachcodeoccurs.becausethesystemassumesthatdefectstendto algorithm (b) detection occurlessfrequentlythancorrectpoints,itjudgestheitemsthatoccurinfrequentlyasdefects. Therefore,ifthefrequencyofoccurrenceexceedsasetvalue,theproductisgood;ifthefrequency isgreaterthanoneandlessthanthesetvalue,thesystemdisplaysthecomponentscorresponding tothecodeaspotentiallydefectivepoints,askingtheoperatortodeterminewhetherornotthe itemsaredefective.figure21showshowapartialopenisdetectedbythismethod.thesystem detectsopenandshortcircuits,spur,narrowtraces,andpoorspacingbetweenconductors.it performsataspeedof40106pixels/secondat0:2milresolution. ShapecomparisonmethodIntheAi-1029system[32],Nikonemployedapatterncomparisonmethodbasedonautomaticlearningprocedures.Figure22showsthestepsinvolvedinthe andthesystembreakstheinputimageintosmallsegments.thenitstoressmallpatternsfrom trainingandtestingofthesystemforfaultidentication.firsttheuserinputsalearningsample eachsegmentinareferencele.next,thesystemrepeatsthisprocesswiththesubjectboard, productasdefective.inthissystem,theallowanceforpatternvariationarethekeystoaccurate dividingtheinputimageintosmallpatternswiththoseinthereferenceles.ifthesubject evaluation. boarddoesnotexhibitpatternsthatmatchthoseinthereferenceles,thesystemjudgesthe 5Inordertomakethissurveycomplete,thissectionbrieysurveyssomeofthecurrentlyavailable commercialpcbinspectionsystems.manyfactorsmustbeconsideredindesigningacommercial CommercialSystems referstothenumberofdierentinspectionsthesystemcanperform.someofthecommercial inspectionsystem:hardware,software,systemthroughput,versatility,andreliability.versatility 35

36 Coding character patterns Counting frequencies Checking correct answers for the minimum frequency Completing normal learning pattern Pattern edge Central line OV L C S Figure21:Fujitsuradialcodeself-learningmethod Radial coding Characteristic pattern code Frequency Learning Samples Pattern (shape) elements constituting learning samples Image reading Storage Learning process Reference file Printed circuit be tested board to Image reading Figure22:Nikon'sshapecomparisonmethod 36 Test process "Defect": no corresponding pattern is found in the reference file

37 bare-boards.somecanmakeexactmeasurementofboardfeaturesorperforminspectioninlinewiththeproductionprocess.formanufacturing,themostcomplete(andmostexpensive) systemscanexecuteallthesefunctions.thefollowingisalistofcapabilitiesandfeaturesa typicalcommercialpcbinspectionsystemisexpectedtohave: Systemcapability: {Minimumawthatcanberepeatedlydetectedatthestatedescaperate:-2.0mil. {Scanrate:-4.0ft2/min. systemsrunthegamutfrominspectingholesandmeasuringdimensionstoinspectingcomplete {Typicalpixelsize:-1.0mil. {Panelthrough-put:-inspectbothsidesof1824inchpanel(85%active)including {Falsealarmrate(failgoodproduct):-lessthan2.0perft2. setup,loading,scanning,andunloadingatarateof40panels/hour. {Escaperate(passbadproduct):-lessthan1.0per100ft2(dependsondefectcriteria) Typicaldimensionsofpanelstobeinspected: {Gagingcapability(wherespecied):-measurefeaturesizeto1.0mil.. {Scanarea:-18"24". {Paneldimension:-20"26". {Nominalconductorwidth:-4mil. {Nominalconductorspacing:-4mil. Typesofpaneltobeinspected: {Padsize:-roundorrectangularpadsofdimensionbetween3and10mil. {Conductorlayout:-allpossiblelineorientationsandpower/groundlayers. {Conductorviaholediametersize:-5milorlarger. {Photoprintedboards:-allcommercialphotoresisttypes. {Innerlayermetalization:-drilledandundrilledPCBsincoppertechnology. {Artwork:-mostformsincludingsilver-halideanddiazoonbothmylarandglasssubstrate. {Finishedboards:-withoutsolderandpriortosoldermask {Substrates:-FR4,polymideandothercommonsubstratematerial Typesofdefectstobeinspected: {Shorts:-Anyshortwithawidthinaccessof2milatanypoint {Voids:-anyvoidinaconductorthatexposesbaresubstratematerialandexceeds5% {Opens:-Anyconductoropenexceeding2milsinwidth ofthedesignwidth. 37

38 {Spacing:-Anymetalizationthatreducesthespacebetweenconductorbymorethan5 {Artwork:-Anydefectviolatingtheaboverulesforvoids,spacing,orextraneousmetal; {Extraneousmetal:-Anyisolatedspotwhoseareaexceeds2mil2 %ofdesignspacing throughputsinthetableareestimatedandmayvaryuponvariousinspectionfactors.itcan Table1presentsalistofcommercialPCBinspectionsystemscurrentlyavailable.Thequoted aswellasanypinholeinexcessof3mil beobservedthatmostmachinesusethehybridinspectiontechniques-design-rulechecking speedscontinuetoaccelerateandsomemakersnowadoptmultiprocessingsystems.aoisystem andcomparisonmethodsjointly.thesesystemscaninspectmoreitemswithgreateraccuracy Corp.developedtheAOI-20product,whichemploysasmanyas20CCDcamerasandperforms reectedlight,transmittedlightanduorescentlightfrommultiplelightsources.processing thanbefore.toimproveimagequality,makersapplieddierentkindsofilluminationfrom anastonishingspeedof10ns=pixel.withprogressindiminishingpatternthickness,developers parallelprocessing.evenaslowsystemwitha1milresolutionattainsaprocessingspeedof6.00 ft2=min;fastsystemsreach33.33ft2=min.convertingthisvaluetoapre-pixelspeedreveals improvedresolution.the1milresolutionoftheearlydaysnowhasreached0.2mil.inaddition incorporatecorrectionmachinesandaccommodatescomputer-integratedmanufacturing(cim). tohandlinginspectionprocesses,thesesystemsnowdisplaydefectivelocations,components, 6Withtheadvancesmadeoverthelastdecade,machinevisionmayanswerthemanufacturing industry'sneedtoimproveproductqualityandincreaseproductivity.thisstudypresenteda Summary surveyofalgorithmsforvisualinspectionofprintedcircuitboards.aclassicationtreeofthealgorithmsispresented.theclassicationdividesthetechniquesintothreebasicclasses:reference comparisoninwhichproductionboardsarecomparedwithadatabaseorgoldenboardpatterns, designrulecheckingprovidesformakingmeasurementsthatarecheckedagainstpredetermined qualityrules,hybridtechniquescombinebothinselectivelyperformingpatternmatchesaswell asdesign-rulemeasurements. rithmicallycouldreducethecostofthesesystemsdrastically.however,theyremainasabetter aspecialhardwareplatforminordertoachievethedesiredreal-timespeeds,whichmakethe systemsextremelyexpensive.anyimprovementsinspeedingupthecomputationprocessalgo- Themajorlimitationofalltheexistinginspectionsystemsisthatallthealgorithmsneed optionwhendecidingbetweenincreasinglyerrorproneandslowmanualinspectionandhigher productivity.anothermajorprobleminautomatedinspectionsystemdesignisthedevelopment frontinthechallengesconfrontingtheautomatedvisualinspectionresearchisthedevelopment animperfectionisaobjectivedecisionandwillvaryamongdierentmanufacturers.also,fore- ofalgorithmsthatwillprovidethesensitivityneededtondfaultswhileignoring`noise'caused ofgenericinspectionequipment,hardwareandsoftware,capableofhandlingawidevarietyof byacceptableimperfections.thisproblembecomesmoredicultbecausetheacceptabilityof inspectiontasks.manyeortsareunderwaytoimproveexibilityintheeldofvisualinspection systems.systemsinthefuturewillbeeasiertooperatethanthosenowavailable. 38

39 TableI.CommerciallyAvailableBarePCBInspectionSystems System Inspecion Methods Image System Resolution Scan Rate Features/Benefits AOI System AOI-20 Design Rule Checking (8 kinds of detection sensors) and Comparison method 20 CCD Cameras Reflection/Transmission lighting 1 mil 6.00 sq. ft/min Continuous operation is possible through the use of conveyor system Mania MOP-5002 Simultaneous use of Design Rule Checking and Image Comparison Two CCD Cameras Halogen Lamp Lighting 1 mil 6.00 sq. ft/min Menu driven user friendly software for easy and fast setup. Fast unit under test change- over using patented vacuum adaptor system Dai-Nippon Screen OPI-5220 Design Rule Checking and Comparison method LED light, CCD line sensor Reflection/Transmission lighting 1 mil sq. ft/min Complete Comparison Inspection Inspection function of product with special shape Shin-Nippon Steel PT-2130 Design Rule Checking and Comparison method Halogen Lamp, Multi-Directional illumination Speedy CCD Camera 1 mil sq. ft/min Continuous Variable Resolution (0.2 to 1mil) Fastest Speed Orbotech PC-1411 Design Rule Checking and Comparison method (Golden Board or CAD download) Reflective and Diffusive Omni lighting Fixed resolution 0.5 mil 18 x 24 panels 45 sides / hour Low cost startup, On-line verification Orbotech PC-1450 Design Rule Checking and Comparison method (Golden Board or CAD download) Reflective and Diffusive Omni lighting Variable resolution mil 18 x 24 panels sides / hour 3-10 mil line width technology Orbotech PC-1490 Design Rule Checking and Comparison method (Golden Board or CAD download) Reflective and Diffusive Omni lighting Variable resolution mil 18 x 24 panels sides / hour 3-6 mil line width technology for high volume PCB shops Orbotech V-309i/x Design Rule Checking and Comparison method (Golden Board or CAD download) Fluorescent technology (Blue Laser) Variable resolution mil 18 x 24 panels sides / hour 3-10 mil line width technology for high volume PCB shops Orbotech Vision Blaser Design Rule Checking and Comparison method (Golden Board or CAD download) Fluorescent technology (Blue Laser) Variable resolution mil 12 x 12 panels 2-4 mil line width sides / hour technology for high volume PCB shops

40 reviewersfortheirvaluablesuggestionsinimprovingthequalityofthispaper.theauthors andmr.w.grihill,maniatesterion,forprovidingnecessarytechnicalinformationabout wouldliketothankmrs.dyanmacdonald,orbotechinc.,mr.vijaypatel,viewengineering, Acknowledgments-TheauthorswouldliketoacknowledgeDr.BruceMcMillinandthe commercialinspectionsystems.theauthorsthanktheintelligentsystemscenter,umrfor References thesupportincarryingoutthiswork. [2]WalterH.Schwartz,\VisionSystemsforPCBoardInspection",AssemblyEngineering, [1]NelloZuech,\IntroductoryThoughtsonMachineVision/AOIApplicationsintheElectronic Industry"ProceedingsofNEPCON'92,Vol.2,pp ,1992. [3]StephenT.Barnard,\AutomaticVisualInspectionofPrintedCircuitBoards",Advanced Vol.29,No.8,pp.18-21,1986. [4]RyanHendricks,\On-LineInspectionEnables6SigmaQuality",CircuitsAssembly,Vol. SystemsforManufacturing:ConferenceonProductionResearchandTechnology,pp.423-1,No.3,pp.24-27,December ,1985. [5]FrankJ.Langley,\ImagingSystemsforPCBInspection",CircuitsManufacturing,Vol.25, [6]ShinMukai,\PCBContinuousLineSystemProceedsfromManufacturingtoInspection", No.1,pp.50-54,1985. [7]MichaelBeck,andDavidClark,\SMTInspectionStrategies:MaximizingCostEectiveness",ProceedingsoftheTechnicalProgram:NEPCONWest'91,pp ,1991. JournalofElectronicEngineering,Vol.29,No.305,pp.34-39,May1992. [9]Charles-HenriMangin,\WhereQualityisLostonSMTBoards",CircuitsAssembly,pp. [8]BrentR.Taylor,\AutomaticInspectioninElectronicsManufacturing",SPIE-Automatic 63-64,February1991. OpticalInspection,Vol.654,pp ,1986. [11]Shiaw-ShianYu,Wen-ChinCheng,andS.C.Chiang,\PrintedCircuitBoardInspection [10]JosephW.FosterIII,PaulM.Grin,SherriL.Messimer,andJ.ReneVillalobos,\AutomatedVisualInspection:ATutorial",ComputersinIndustrialEngineering,Vol.18,No. 4,pp ,1990. SystemPI/1",SPIEAutomatedInspectionandHighSpeedVisionArchitecturesII,Vol. [12]EmanuelBin-Nun,\AutomaticOpticalInspectionFocusesOnDefects",ElectronicPackaging&Production,pp.82-87,April ,pp ,1988. [14]HowardW.Markstein,\AutomaticOpticalInspectionImprovesMultilayerYields",ElectronicPackaging&Production,pp.60-64,September,1983. [13]JohnRagland,\AutomatingInnerlayerInspection",CircuitsManufacturing,pp.91-94, February

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