ThestructureandtesttheoryofKLA2132.ppt

,ThemachinestructureandtesttheoryofKLA2132,JAYSUNSemiconductorManufacturingCo.,Ltd.,Diffusion,ProcessDepartment,YanFanbin,Contents,ThestructureofKLA2132,Systemfunctionandopticalsystem,Primaryparameterinthetest,Testprocess,Calculatortheory,NoiseandSuppression,ThestructureofKLA2132,1.Imagecollectunit.2.Imageprocessunit.3.Defectanalyzeunit.,Imagecollectunit,cassette1,cassette2,robot,EMO,UnloadState,LoadState,WaferStage,Imageprocessunit.,VideoDisplay1,VideoDisplay2,Keyboard,EMO,StatesCard,BackupDevice,FloppyDrive,Joystick,Mouse,VideoDisplay1,VideoDisplay2,7,Defectanalyzeunit,Keyboard,EMO,StatesCard,Mouse,VideoDisplay,VideoDisplay,1.WaferMap,VideoDisplay,2.DefectSizeHistogram,VideoDisplay,ClusterSizeHistogram,SystemFunctions,OpticsSystem,ImageAcquisition,DefectFileLocationSizeCluster/Random,ImageSensor(TDI),Wafer,ImageComputer,PostProcessing,ClusteringSamplingClassification,Analog-to-DigitalConversion,DigitalImage,ImageSubtractionImageFilteringDefectPixelExtraction,MergingPixelsintoDefectsDefectSizing,DefectPixels,DefectList,AnalogImage,Opticalsystem,LightLevelisadjustedbyNDF,AutofocusSystem(usesInfraredspectrum),ObjectiveLens,ZoomLens,MagChanger,TDISensor,ColorReviewCamera,LightSource,Beam-splitters,NDF,GreenFilter,Mirror,Wafer,LightLevelSensor,NDF=NeutralDensityFilter(adjustable),Neutraldensityfilter(NDF),TheNDFFiltercontrolstheamountoflightthatilluminatesthewafer.Itismadeupoftwoplatesoflinearlyshadedglassthatmoveoppositefromeachother.Thisconfigurationgivesuniformshadingacrossthewidthofthelightbeam.,Lightleveltrainingduringsetup,ThesystemwilloptimizetheamountoflightintheinspectionlightboxusingtheNDFtoachievethedesiredUpperendpoint.NeitherGainorOffsetarebeingusedatthistime,PrimaryParameterinthetest,1.Pixel2.GrayLevel,Pixel,Defectpicture,Graypicture(Pixel=0.01um),Pixel=0.16um,Pixel=0.39um,Pixel=0.62um,Everyimagecanbeexpressedinpixel.Expressthesameimage,themorepixelsthereare,thesmalleroftheareathatsinglepixelexpressed,thebetteroftheimage.Atthesametime,themorepixelsthereare,themoretimetheycost.,GrayLevel,GrayLevel:wedefineabsoluteblackas0,andabsolutewhiteas255,sothateverypixelofeveryimage,thereisanumbervalues0255todescribeitsgraylevel.,Testprocess,1.Loadwafer2.Recipeselect3.Waferscanning4.Digitizationofimage5.Defectimageprocess6.Postprocess7.Defectimagecheckandanalysis,WaferScanning,AftertheX-stagehascompleteda2048pixelheightswath,theY-stagemovessothattheobjectiveisoverthenextswath.TheX-stagethenscansthenextswathintheoppositedirection.TheResultisaserpentinepatternofmovement,sothatdataiscollectedfromlefttorightandfromrighttoleft(savestimeascomparedtoatypewriter-likemovement).,TDIScanning,Stagemoveswaferundertheobjectiveinacontinuousswath.ImageformedbyopticsmovescontinuouslyacrossstationaryTDIsensor.TimingofTDIsensordatatransferissynchronizedwithstagemovement.Resultiscontinuous“ribbon”ofimageinformation(vs.colorcameratakes“snapshots”).The“ribbon”(swath)is2048pixelstall,andisclockedonepixelatatimeinx.,DigitizationofImage,OpticalImage,TDIPixelGrid,DigitizedGrayScaleImage,Defectimageanalysis,1.Defececlassifyparticlescratchresiduemoundpitstackingfault2.Location3.Size4.Cluster/Random,KLAcapturedmaindefect,Pitonsubstrate(findafterpolyetch),Watermark(findafterSINetchsc-1step),Residue,PatternbridgedpitchorspacingbetweencentersofTDI“eyes”is27mOpticsmagnifyimagefromwafersuchthatonepixelontheTDIcoversthedesiredareaonthewaferTherefore,themoremagnification,thesmallertheareaofthewafercorrespondingtoonepixelCanexpresswaferpixelsizeormagnificationinterchangeably;i.e.saying“.39micron”isthesameassaying“32by2”,25m,MagChanger,Objective,Wafer,.39m,2X,32X,25m,MagChanger,Objective,Wafer,.25m,2X,50X,TDI,TDI,25/2/32=.39,25/2/50=.25,25mm,27mm,RANDOM(dietodie),PixelSizeSens.Obj.1PwrChgr22135/38Rate,1.25m0.5m10 x2x0.5sec/cm20.62m0.3m20 x2x1.8sec/cm20.39m0.2m32x2x5.0sec/cm20.25m0.15m50 x2x10sec/cm21.25m1.25m10 x2x0.5sec/cm20.62m0.62m20 x2x1.8sec/cm20.39m0.39m32x2x5.0sec/cm20.25m0.25m50 x2x10sec/cm2,Sensitivityspecificationsfor90%defectcapturerateusingKLA-TencorDefectStandardWafer(DSW)andastatisticallyvalidnumberofruns.,PixelSizevs.Sensitivity,ARRAY(celltocell),ImageProcessingTechnology,DigitizedimageMagsettingof21xxdeterminestheresolution.Eachpixelassignedvaluefrom0-255(8bits).0=black,255=whiteHistogramAchartofpixelvaluesvs.numberofoccurrencesinanimage.,0,255,30,BasicImageSubtractionTheory,125-0=125,0-125=-125,Values0-255,Values0-255,Values-255-+255,0125,-125255,GrayscaleValue,RandomMode-ImageSubtractionExample,ImageSubtractiondefectsaredetectedbysubtractinga“candidate”imagefroma“reference”imageimagesarecollatedasarraysofdatafromTDIcalledframeseachframeis512highby1024wide,ReferenceImage,CandidateImage,DifferenceImage,MergeAlgorithm,2,2,t=t1,MergeRegion,t=t2,MergeRegion,MergeResultatt=t1,t=t3,FinalMergeResult,2,2,2,2,DefectivePixel,MergeValue=2pixels,MergeRegion,Mergeisspecifiedinpixels.,EffectsofChangingMerge,DefectivePixel,Merge=14DefectsReported,Merge=22DefectsReported,Merge=41DefectReported,Verysmallchangesinmergecanhavelargeimpactonnumberofreporteddefects.,EffectofStackParameter,Iftwodefectsliewithinthestackdistance(inmm)ofeachother,theyarecountedasonedoublycaughtdefect.,CellSize,DoublyCaught,Defect,VerySmallDefect,SinglyCaught,ImageSubtraction,Images,DifferenceImages,Clustering,WequantifytheBrightnessandRoughnessforapixelbylookingatthepixelsinitsneighborhood.Brightness(2choices)Usepixelvalue(1x1Brightness)(SWv5.2+only)Oruseaverageofpixelvaluesina3x3neighborhood(3x3Brightness)RoughnessUsethedifferencebetweenthebrightestanddarkestpixelsina3x3neighborhood(3x3Roughness)Examples:,QuantifyingBrightnessandRoughness,64,64,64,64,32,32,32,32,96,128,128,128,128,224,224,223,192,64,64,64,64,64,64,64,64,224,222,222,223,224,225,225,225,225,222,223,224,223,226,223,223,1x1Brightness=PixelValue,3x3Brightness=,3x3Neighborhood,(PixelValue),19,3x3Roughness=Max(PixelValue)-Min(PixelValue),3x3Neighborhood,3x3Neighborhood,S,64640,224124192,2242244,64,64,64,64,64,64,64,64,64,64,64,64,192,96,32,SegmentationAppliedtoaDieImage,TheDenseandArraysLogic,OpenAreas,andGrainyMetalareisolatedinseparatesegments.Intermediatepixelsgointothegraysegment.,Arbitration,Whenwecalculatedifferences,twopixelsareinvolved.ThesepixelscanbeassignedtoconflictingsegmentnumbersThresholdsareappliedtodifferences,sotheseconflictsmustberesolved.Arbitrationresolvestheseconflicts.Pixeldifferencesareassignedtothelargernumberedsegmentofthetwopixelsinvolvedinthecomparison.,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,32,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,160,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,96,Candidate,Reference,Candidate,Reference,Candidate,Reference,Candidate,Reference,1x1Brightness3x3Roughness,320,9664,320,224192,32192,2240,2240,16064,100,1,0,SegmentationScheme,1,0,Segments,ArbitratedSegment,0,1,1,1,1,0,1,0,1,0,X-Size=9pixels*,Y-Size4pixels*,*Example,ifweassumea0.4mmPixelSizex-Size=3.6umY-Size=1.6um,DefectSizingMethod1:XandYSizing(1D),DefectSizingMethod2:EstimatedArea(2D),When:X-sizecellordiesizedefect5X5pixelsATorSATdefect5X5pixels,fixedthresholddefect5X5pixelsfixed,ATorSAT,DefectSizingMethod3:KLASize(1D),KLASize=MinSQRT(Area),X-Size,Y-Size,Example:Given:X-Size=10mm,Y-Size=5um,Area=4mm2,Result:KLASize=2mm(squarerootofarea)(Notetheareaisnotx-sizey-size.),NoiseandSuppression,PatternNoise1.Whatispatternnoise?2.Thecharacterofpatternnoise.3.Thesuppressionofpatternnoise.,ProcessNoise1.Whatisprocessnoise?2.Thecharacterofprocessnoise.3.Thesuppressionofprocessnoise.,PatternNoise,1.Patternnoiseisanon-zerodifferencebetweendiesasaresultoftheimagingprocess.2.Itoriginatesfromslightmisalignmentbetweendies,leadingtoincompleteimagesubtraction.3.PatternnoisecanbeminimizedwithImageFiltering.,Thesuppressionofpatternnoise,1.Filtersgiveyoua2-dimensionalweightedgraylevelaverageofa3x3pixelneighborhood.2.YoucanFilteranimagebeforeimagesubtractionwiththetools“In-Filter”oraftersubtractionwiththe“Diff-Filter”.,1-Dexample,2-Dexample,3-DFilteringExample,ProcessNoise,1.Exceptional2xxxalignmentvirtuallyeliminatesPatternNoiselFilterscancompensateforslightmisalignments2.“ProcessNoise”isthelimitingfactorforsensitivity:PhysicalDie-to-Diedifferenceswhichdonotimpactyield.Fixedthresholdsmustbesethigherwhichmaymissmoresubtle“Real”defects.Auto-ThresholdandSATadvancedimageprocessingalgorithmshelpdifferentiate“noise”from“real”signal.,|ImageA-B|,IImageA-BI,IImageB-CI,ImageA,ImageB,ImageC,Moustache,Minorpatternnoise,0,2,4,6,8,10,-255,-192,-128,-64,0,64,128,192,255,GreyLevelDifference,Log(#Pixels),Nonoise,A-B,ColorVariation,ImageA,ImageC,ImageB,0,2,4,6,8,10,-255,-192,-128,-64,0,64,128,192,255,Log(#Pixels),Moustache,Noisefrommetalgrain,GreyLevelDifference,A-B,Log(#Pixels),0,2,4,6,8,10,-255,-192,-128,-64,0,64,128,192,255,GreyLevelDifference,Moustache,Noisefrommetalgrain,B-C,MetalGrain,IImageB-CI,ImageA,ImageC,ImageB,IImageA-BI,A-B,B-C,DifferenceImages,ColorSuppression,Auto-Threshold(AT),Log(#Pixels),0,2,4,6,8,10,12,TargetDensityPoint,ThresholdOffset,CalculatedThreshold,ForeachFrame(1024x512pixels),theImageComputergeneratesaDifferenceHistogramandseparatelycountspixelsfromeachendofthehistogram(+/-255)untilitreachesthefractionofthetotalspecifiedbytheTargetDensity.AThresholdOffsetisthenappliedtoeachoftheseTargetDensityPointstogettheCalculatedThresholds.PixelswhosegrayscaledifferencesarefurtherfromzerothantheCalculatedThresholdontheappropriatesideofthehistogramarereportedasdefective.,CalculatedThreshold,DynamicAuto-ThresholdAlgorithm,0,2,4,6,8,10,-255,-192,-128,-64,0,64,128,192,255,GreyLevelDifference,Log(#Pixels),Verylargedefectismissedbytheauto-threshold:,MAXThreshold,MAXThreshold,CalculatedThreshold,Calculatedthreshold,TheTargetDensitySearchmaynotpassthroughalargedefect.AfteraddingThresholdOffset,theCalculatedThresholdwillmissthisdefect!Solution:InadditiontoTargetDensityandThresholdOffset,thereisaparametercalledMaxThresholdwhichlimitstheappliedthresholdvalue.Oneachsideofthehistogram,thesmaller(closertozero)oftheCalculatedThresholdorMaxThresholdisapplied.Here,theCalculatedThresholdvalueisappliedontheleftsideofthehistogramandMaxThresholdisappliedontherightside.,Auto-ThresholdandLargeDefects,Auto-ThresholdMode(SWv5.0+),The“Auto-Threshold”inspectionmodeusesthefollowingvaluesforitsparameters:ThresholdOffset=Auto-Threshold(onlyvaluesetinMenus)ThresholdMax=Auto-Threshold+64Targetdensity=10-3Auto-Thresholdshouldbeamultipleof4Nonmultiplesof4aretruncatedtothenextlowermultipleof4ThresholdOffset,ThresholdMaxandTargetDensitycanbesetseparatelywitha1-segmentSATinspection2132(SWv4.1+withSAToption)canthereforesimulateSWv5.xAuto-ThresholdBecauseofadifferentFramesize(2048x512)on2132s,Targetden