一种应用于半导体制造业的支持向量机SVM检测方法
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摘要
提出一种支持向量机SVM的检测方法,用于半导体制造过程中特征参数检测。首先对SVM模型进行分析,结果显示2D SVM和模糊SVM模型效果最佳。然后提出一种将2D SVM和模糊SVM这两种模型结合的方法,证明了这种方法更有利于半导体晶圆厂检测产品的各种特征参数。
In this paper, a detection method of support vector machine(SVM) is proposed for feature parameter detection in semiconductor manufacturing process. Firstly, the SVM model is analyzed, and the results show that the two-dimensional SVM and the fuzzy SVM model are the best. Then a method combining the two models of 2 D SVM and Fuzzy SVM is proposed, which proves that this method is more advantageous for semiconductor wafer factory to detect various characteristic parameters of products.
In this paper, a detection method of support vector machine(SVM) is proposed for feature parameter detection in semiconductor manufacturing process. Firstly, the SVM model is analyzed, and the results show that the two-dimensional SVM and the fuzzy SVM model are the best. Then a method combining the two models of 2 D SVM and Fuzzy SVM is proposed, which proves that this method is more advantageous for semiconductor wafer factory to detect various characteristic parameters of products.
引文
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[2]S.Li,T.Tang,D.W.Collins.Minimum inventory variability schedule with applications in semiconductor fabrication[J]IEEE Transactions on Semiconductor Manufacturing,1996,9:145-149.
[3]N.Kupp,Y.Makris.Integrated optimization of semiconductor manufacturing:Amachine learning approach[C].in Test Conference,2012:1-10.
[4]G.Tello,O.Y.Al-Jarrah,P.D.Yoo,Y.Al-Hammadi,S.Muhaidat,U.Lee.Deep-structured Machine Learning Model for the Recognition of Mixed-defect Patterns in Semiconductor Fabrication Processes[J].IEEE Transactions on Semiconductor Manufacturing,2018:1.
[5]C.F.Chien,W.C.Wang,J.C.Cheng.Data mining for yield enhancement in semiconductor manufacturing and an empirical study[J].Expert Systems with Applications,2006,33:192-198.
[6]K.B.Irani,J.Cheng,U.M.Fayyad,Z.Qian.Applying Machine Learning to Semiconductor Manufacturing[J].IEEE Expert,1993,8:41-47.
[7]S.Lekha,M.Suchetha.A Novel 1-D Convolution Neural Network With SVM Architecture for Real-Time Detection Applications[J].IEEESensors Journal,2017,18:724-731.
[8]M.A.Mohandes,T.O.Halawani,S.Rehman,A.A.Hussain.Support vector machines for wind speed prediction[J].Renewable Energy,2004,29:939-947.
[9]C.J.C.Burges.A tutorial on Support Vector Machines for pattern recognition[J].Data Mining and Knowledge Discovery,1998,2:121-167.
[10]C.Cortes,V.Vapnik.Support-vector networks[J].Machine Learning,1995:273-297.
[11]J.Sun,M.Zhang,C.L.Tan.Tree Sequence Kernel for Natural Language[C].in AAAI Conference on Artificial Intelligence,AAAI 2011,San Francisco,California,USA,2011.
[12]W.H.Press,S.A.Teukolsky,W.T.Vetterling,B.P.Flannery.The Art of Scientific Computing[M].Cambridge University Press,2007.
[13]Balli,S.,Sagbas,E.A.The usage of statistical learning methods on wearable devices and a case study:activity recognition on smart watches[M].InTech Press,Rijeka,Croatia,2017.
[14]N.Quader,M.O.Gani,D.Chaki.Performance evaluation of seven machine learning classification techniques for movie box office success prediction[C].in International Conference on Electrical Information and Communication Technology,2017.
[15]C.F.Lin,S.D.Wang.Fuzzy support vector machines[J].IEEE Transactions on Neural Networks,2002,13:464-471.
[16]A.H.Murphy.The Finley affair:a signal event in the history of forecast verification[J].Weather&Forecasting,1996,11:3-20.
[17]C.W.Hsu,C.J.Lin.A Comparison of Methods for Multiclass Support Vector Machines[J].IEEE TRANS,NEURALNETWORKS,2002:415-425.
[18]X.Zhang.Using class-center vectors to build support vector machines[J].Neural Networks for Signal Processing Ix,1999:3-11.
[19]James Dougherty,Ron Kohavi,Mehran Sahami.Supervised and Unsupervised Discretization of Continuous Features[J].Machine Learning Proceedings,1995:194-202.
[20]Hong Dai.Research on SVM improved algorithm for large data classification[C].in IEEE 3rd International Conference on Big Data Analysis(ICBDA),2018.