The class imbalance problem has made researchers pay intensive attention in recent years. In the modern semiconductor industry, the class imbalance makes fault detection, which aims at constructing a decision tool to maintain high process yields, quite challenging. Marginal Fisher Analysis (MFA) is a popular method which can analyze the discriminant relationship between data points from different classes, and has been applied in fault detection. However, the performance of MFA is poor if it is applied in the data having imbalance distributing between classes. This paper analyzes the challenge of class imbalance and a improved approach of MFA named Sensitive Margin Fisher Analysis (SMFA) was proposed for the problem. The proposed fault detection method has been verified by applying it in the semiconductor wafer fabrication process. The experiment results confirm the new method improves the fault detection performance.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Fault detection based on Sensitive Marginal Fisher Analysis for class imbalance


    Contributors:
    Han, Zhi yan (author) / Wang, Jian (author)


    Publication date :

    2016-08-01


    Size :

    72589 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    An optimization criterion for generalized marginal Fisher analysis on undersampled problems

    Yang, W. Y. / Liu, S. X. / Jin, T. S. et al. | British Library Online Contents | 2011



    Class-Wise Difficulty-Balanced Loss for Solving Class-Imbalance

    Sinha, Saptarshi / Ohashi, Hiroki / Nakamura, Katsuyuki | British Library Conference Proceedings | 2021


    Fault Diagnosis for Rail Vehicle Suspension Systems Based on Fisher Discriminant Analysis

    Wei, Xiukun / Wu, Sheng / Ding, Jianlong et al. | Tema Archive | 2014


    Resampling Methods for Solving Class Imbalance Problem in Traffic Incident Detection

    Li, M. H. / Chen, S. Y. | British Library Conference Proceedings | 2015