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1、SemiconductorYieldAnalysisandMulti-ChipPackage(MCP)DiePairingOptimizationusingMachineLearningSemiconductorYieldAnalysisandMulti—ChipPackage(MCP)DiePairingOptimizationusingMachineLearningRandallGoodwin,TechnologyandManufacturingGroup,IntelCorporationRussellMille
2、r,TechnologyandManufacturingGroup,IntelCorporationEugeneTuv,TechnologyandManufacturingGroup,IntelCorporationAlexanderBorisov,TechnologyandManufacturingGroup,IntelCorporationIndexWords:statistics,machinelearning,datamining,optimizationAbstractMachineLearning,Art
3、ificia1Intelligence(AI)andStatisticalLearningarerelatedmathematicalfeldswhichutilizecomputeralgorithmstocreatemodelsforthepumosesofdatadescriptionand/orprediction.Somewellknownexamplesincludebiometricidentificationandauthorizationsystems,speechrecognitionanduse
4、rtargetedinternetadvertising.StatisticalLearning.whichwewilluseinthispaper,alsohasmanyapplicationsinsemiconductormanufacturing.Someofthechallengingcharacteristicsofsemiconductordataincludehighdimensionality,mixturesofcategoricalandnumericdata,non—randomlymissin
5、gdata,non—Gaussianandmultimodaldistributions.nonlinearcomplexrelationships,noise,outliersandtemporaldependencies.Thesechallengesarebecomingparticularlyacuteasthequantityofavailabledataincreasesandtheabilitytotracelots,wafers,die,andpackagesthroughoutthefullfab.
6、wafertest.assemblyandfinaltestmanufacturingflowimproves.Statistical—learningtechniquesareappliedtoaddressthesechallenges.InthispaperwediscusstheadvancementandapplicationsofTreebasedclassificationandregressionmethodstosemiconductordata.WebeginthePaDerwithadescri
7、ptionoftheproblem,followedbyanoverviewofthestatistical—learningtechniquesweuseinourcasestudies.Wethendescribehowthechallengespresentedbysemiconductordatawereaddressedwithoriginalextensionstotree—basedandkernel—basedmethods.Next,wereviewfourcasestudies:homesales
8、priceprediction,signalidentification/separation,finalspeedbinclassificationanddiepairingoptimizationforMulti—ChipPackages(MCP).Resultsfromthecasestudiesdemonstratehowstatist