WASET
	%0 Journal Article
	%A Sae-Rom Pak and  Seung Hwan Park and  Jeong Ho Cho and  Daewoong An and Cheong-Sool Park and  Jun Seok Kim and  Jun-Geol Baek
	%D 2012
	%J International Journal of Industrial and Manufacturing Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 72, 2012
	%T Yield Prediction Using Support Vectors Based Under-Sampling in Semiconductor Process
	%U https://publications.waset.org/pdf/5620
	%V 72
	%X It is important to predict yield in semiconductor test process in order to increase yield. In this study, yield prediction means finding out defective die, wafer or lot effectively. Semiconductor test process consists of some test steps and each test includes various test items. In other world, test data has a big and complicated characteristic. It also is disproportionably distributed as the number of data belonging to FAIL class is extremely low. For yield prediction, general data mining techniques have a limitation without any data preprocessing due to eigen properties of test data. Therefore, this study proposes an under-sampling method using support vector machine (SVM) to eliminate an imbalanced characteristic. For evaluating a performance, randomly under-sampling method is compared with the proposed method using actual semiconductor test data. As a result, sampling method using SVM is effective in generating robust model for yield prediction.

	%P 2755 - 2759