Predictive Analysis for Wafer Defect Management in Semiconductor Manufacturing

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Adigarla Jayanthi; Palacharla Venkatrao; Pilla Saikumar; Kundeti Durga Sudheer; Bonuboyina Sambasiva rao

Predictive Analysis for Wafer Defect Management in Semiconductor Manufacturing

Authors: Adigarla Jayanthi, Palacharla Venkatrao, Pilla Saikumar, Kundeti Durga Sudheer, Bonuboyina Sambasiva rao

Unique Paper ID: 173907
Volume: 11
Issue: 10
PageNo: 2177-2183

Keywords: Dataset Image Processing Techniques Deep Learning Yolov2 Detection and Convolutional Neural Network.

Abstract:
Predictive analysis for wafer defect management in semiconductor manufacturing play a critical role in ensuring the quality and reliability of semiconductor manufacturing. This work proposes a robust framework leveraging deep learning techniques to enhance detection and classification of wafer defects. We integrate advanced preprocessing techniques, including Contrast Limited Adaptive Histogram Equalization (CLAHE) and local brightness adjustments, to improve image quality and increase Peak Signal-to-Noise Ratio (PSNR). A YOLOv2-based object detection model is employed to localize defects efficiently, while a hybrid Convolutional Neural Network (CNN) is utilized for accurate defect classification. Our framework begins with image pre-processing, where enhancement techniques are applied to improve contrast and noise levels. The YOLOv2 model detects and annotates defects, while the hybrid CNN classifier identifies defect types. By incorporating the improved preprocessing pipeline, the PSNR values of input images are significantly enhanced, ensuring better feature representation for downstream tasks. Experimental results demonstrate the efficacy of the proposed approach, achieving high accuracy in both localization and classification tasks. The proposed system offers a scalable and efficient solution for real-time wafer defect analysis, paving the way for enhanced automation and precision in industrial applications. The framework's modular design ensures adaptability for diverse defect types and manufacturing environments.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 10
PageNo: 2177-2183

Predictive Analysis for Wafer Defect Management in Semiconductor Manufacturing

Available:https://ijirt.org/article?manuscript=173907

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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