Wafer defect detection is a critical part of the semiconductor wafer manufacturing industry. Human-based manual defect detection is tedious and often prone to misjudgement. Hence, there is a push towards automatic wafer defect detection in the industry. This paper shall investigate the efficacy of a transfer learning pipeline that utilizes different pre-trained VGG models, i.e., VGG16 and VGG19, that act as a feature extractor coupled with a Random Forest (RF) model in classifying wafer defect images. The hyperparameter of the RF classifier is further fine-tuned through the grid-search technique via a five-fold cross-validation method. It was shown in the study that the optimised VGG16+RF pipeline provided a better classification of the wafer defects.


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    Titel :

    The Classification of Semiconductor Wafer Defects by Means of Feature-based Transfer Learning Approach


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Abdul Majeed, Anwar P.P. (Herausgeber:in) / Yap, Eng Hwa (Herausgeber:in) / Liu, Pengcheng (Herausgeber:in) / Huang, Xiaowei (Herausgeber:in) / Nguyen, Anh (Herausgeber:in) / Chen, Wei (Herausgeber:in) / Kim, Ue-Hwan (Herausgeber:in) / Xuen, Lim Shi (Autor:in) / Jizat, Jesnor Arif Mat (Autor:in) / Khairuddin, Ismail Mohd (Autor:in)

    Kongress:

    International Conference on Robot Intelligence Technology and Applications ; 2023 ; Taicang December 06, 2023 - December 08, 2023



    Erscheinungsdatum :

    29.11.2024


    Format / Umfang :

    7 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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