Euclidean Distance Based Feature Selection for Fault Detection Prediction Model in Semiconductor Manufacturing Process

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1 Vol.133 (Iformatio Techology ad Computer Sciece 016), pp Euclidea Distace Based Feature Selectio for Fault Detectio Predictio Model i Semicoductor Maufacturig Process Jaekwo Kim 1,1, Yougshi Ha * ad Jogsik Lee 1* 1 Dept. of Computer Sciece ad Iformatio Egieerig, Iha Uiversity, South Korea {Jaekwo Kim, ad Jogsik Lee, jslee@iha.ac.kr Dept. of Computer Egieerig, Sugkyul Uiversity, South Korea {Yougshi Ha, hays@sugkyul.ac.kr Abstract. Predictio of the fault detectio ca reduce the lead time ad cost i follow the classificatio performace. Fault detectio predictio eed the feature selectio method for target class related feature i order to ehace the performace. I this paper, we propose the Euclidea distace based feature selectio for fault detectio predictio model. First, the features of Semicoductor calculates the MAV (Mea Absolute Value) ad SD (Stadard deviatio). Secodly, usig the Euclidea distace to select the most appropriate feature o the classificatio model. Fially, usig the selected feature to the eural etwork as a learig geerates fault predictio model. The proposed method is effective for the predictio of the fault Semicoductor Maufacturig process. Keywords: Semicoductor maufacturig process, Fault detectio predictio, Feature selectio, Euclidea distace 1 Itroductio Semicoductor maufacturig process is orgaizes very complicated process ad is possible to predict trasferece umber amog semicoductor idustry.[1] Amog Semicoductor maufacturig process, some fault ca be detected i wafer maufacturig process ad it is possible to fail to produce fial product. Therefore, pass/fail (regular/irregular) classificatio techique is ecessary i maufacturig process, ad fault detectio predictio before fial productio ca improve quality ad reliability.[] Classificatio predictio model ca classify pass/fail usig semicoductor s various data. To produce such a classificatio predictio model, feature selectio for orgaizig of iput data is very importat.[3] Feature selectio ca icrease accuracy of classify predictio by elimiatig uecessary characteristic ad choosig ecessary characteristic.[4] * Correspodig Author. Yougshi Ha ad Jogsik Lee ISSN: ASTL Copyright 016 SERSC

2 Vol.133 (Iformatio Techology ad Computer Sciece 016) I this paper, we proposed the Euclidea distace based feature selectio for Fault detectio predictio model. EDFS (Euclidea distace based feature selectio) is study for detectig fault i semicoductor maufacturig process, it use Euclidea distace to select feature. First, feature whether it is pass or fail is calculated that MAV (Mea Absolute Value) ad SD (Stadard deviatio). Secod, choose the most appropriate feature for classificatio model usig Euclidea distace. Fially, fault predictio model usig ANN (Artificial Neural Network). Method I this paper, we build a fault detectio predictio model usig SECOM dataset [5]. SECOM dataset is FAB data that is collected by 590 sesor from semicoductor maufacturig process, is cosisted of record of 1,567 ad feature of 590. Amog the record of 1,567, fail is 104 (ecoded as 1), pass is 1463 (ecoded -1). I order to icrease accuracy of fault detectio predictio model, we have to choose feature amog the 590 that are related to fault. As show i figure 1 is suggest EDFS ad Fault detectio predictio model framework. Fig. 1. Framework. The framework is cosist of Three phase. First phase is data preprocessig phase to form SECOME dataset as a classificatio predictio model. It is cosisted of Data Cleaig ad Feature selectio. The secod phase is step of geeratio of predictio model, it regulates the proportio of records to maitai balace of pass ad fail by over samplig. Ad the, it geerates Fault detectio predictio model by usig eural etwork. A third phase is step of evaluatio, it measure precisio, recall, F- measure by usig cofusio matrix. Procedure for geerate of fault predictio model icludig EDFS is as follows. Data cleaig 1) Iput the feature data of 590. Remove i case of sigle value. ) Remove feature i case of more tha 60% of NaN (ot available) ad missig values of record of 1,567. Feature selectio 3) Apply the followig Euclidea distace based feature selectio: 86 Copyright 016 SERSC

3 Vol.133 (Iformatio Techology ad Computer Sciece 016) 3.1) Divide data ito two class (pass class ad fail class) 3.) Calculate the each attribute values usig MAV (Mea Absolute Value) ad SD (Stadard Deviatio). MAV ad SD is defied as MAV feature(n) 1 x k k 1 (1) SD feature( Νe σ (x k m) / ( x k /) m k 1 k 1 () 3.3) calculate the each feature of membership value of MAV. The membership value of MAV is defied as A 1 /((max(fea ture(n)) ( mi(featur e(n))) (3) B 1 ( (max(featu re(n)) * A) MAV p () A* MAV feature(n) 3.5) Compare value differet i every feature of the fail class ad pass class. Usig the MAV ad SD calculate the Euclidea distace. Euclidea distace is defied as EuclideaD istace B (MAV Pass MAV Fail ) (SD Pass SD Fail ) 3.4) Determie the feature that is higher tha average by usig descriptive statistics each value calculated with Euclidea distace (cofidece iterval: 95%) Data Miig ad evaluatio 4) Do oversamplig the record with 50:50 to maitai balace of pass ad fail. 5) Build a fault predictio model with artificial eural etwork (Back propagatio). 6) Usig the cofusio matrix compares the precisio, recall (sesitivity) ad F- measure. Cofusio matrix as show figure. (TP: True Positive, FP: False Positive, FN: False Negative, TN: True Negative) (4) Fig.. Cofusio matrix 3 Experimetal We use JAVA jdk 1.8 ad SPSS Clemetie 11.1 for experimet. To measure the performace of the experimet, compare to CFS (Correlatio-based Feature Subset Copyright 016 SERSC 87

4 Vol.133 (Iformatio Techology ad Computer Sciece 016) Selectio), IG (Iformatio Gai), PCA (Priciple Compoet Aalysis) ad EDFS. Preprocessig result for Fault detectio predictio model is as follow figure 3. Fig. 3. Preprocessig result First, data cleaig is fially usig 309 feature by removig 71 feature that are more tha 60% of NaN (ot available) ad missig values amog 590 feature. Secod, feature selectio determie ultimately 50 feature amog 309 by usig EDFS. (Cofidece iterval: 95%). For geeratio of predictio model ad experimet, we separates traiig set 70% (total 1,099 record; pass 1,06, fail 73) ad testig set 30% (total 468 record; pass 437 fail 31). Also, the umber of traiig set s fail is set to 1,07 usig over samplig. Ad fially, Fault detectio predictio model is created usig ANN. Fault detectio predictio model s cofusio matrix is listed i table 1. Ad each model s performace measure is like figure 4. Table 1. Cofusio matrix Model TP FP FN TN ANN without feature selectio ANN with CFS ANN with IG ANN with PCA ANN with EDFS (propose) Fig. 4. Performace measure 88 Copyright 016 SERSC

5 Vol.133 (Iformatio Techology ad Computer Sciece 016) EDFS is highest i accuracy. I other words, EDFS is useful i feature selectio of Semicoductor Maufacturig process. PCA is highest i Specificity as 96.9%. But the purpose of this study is to predictio of fail, sesitive must be higher tha ay other models. The reaso of all model s sesitive is lower tha 0%, the distributio of pass/fail is imbalace. Therefore, solutio of data ubalaced problem is eeded. Ad sesitive of EDFS is highest, it meas EDFS is useful i fault detectio. 4 Coclusio Semicoductor maufacturig process costs a lot of cost ad lead time depedig o test or ot. To higher of accuracy of fault predictio model, we eed feature selectio related with fail. I this study, we proposed the Euclidea distace based feature selectio for Fault detectio predictio model. Proposed EDFS calculates the MAV ad SD of each feature ad extract the feature usig Euclidea distace. Ad it geerated predictio model usig ANN. Ad the way EDFS is reported better performace tha ay other feature selectio techique. Ackowledgmet. This work was fuded by the Miistry of Sciece, ICT ad Future Plaig (NRF-015R1C1AA ). Refereces 1. G.S. May ad C.J. Spaos: Fudametals of Semicoductor Maufacturig ad Process Cotrol. Joh Wiley & Sos. (006). Puromo, M. R. A., & Dewi, I. H. S.: A maufacturig quality assessmet model based-o two stages iterval type- fuzzy logic. I IOP Coferece Series: Materials Sciece ad Egieerig, vol. 105, o. 1, pp IOP Publishig. (016) 3. Kerdprasop, Kittisak, ad Nittaya Kerdprasop: Feature selectio ad boostig techiques to improve fault detectio accuracy i the semicoductor maufacturig process. Proc. of Iter. MultiCoferece of Egieers ad Computer Scietists. vol. 1. (011) 4. Arif, F., Suryaa, N., & Hussi, B.: Cascade Quality Predictio Method Usig Multiple PCA+ ID3 for Multi-Stage Maufacturig System. IERI Procedia, vol. 4, pp (013) 5. SEmi COductor Maufacturig. (010), Copyright 016 SERSC 89