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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/93558

    Title: 基於深度學習之自動化光學檢驗後人工辨識輔助
    Authors: 陳鈺云;CHEN, YU-YUN
    Contributors: 資訊管理學系
    Keywords: CoAtNet;Adversarial Autoencoder;EGAN;深度學習;AOI;CoAtNet;Adversarial Autoencoder;EGAN;Deep Learning;AOI
    Date: 2024-01-23
    Issue Date: 2024-03-05 17:48:32 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 當今晶圓製造業中,自動光學檢查(Automatic optical inspection, AOI)已經成
    高昂的,也會導致成本的上升。因此,透過使用人工智慧(Artificial Intelligence,
    在標記資料平衡的情況下,我們採用 合作式注意力網絡(Convolution and
    Attention Network, CoAtNet)方法進行 AI 圖片分類。而在標記資料不平衡的情況
    下,我們使用對抗自編碼器(Adversarial Autoencoder, AAE)與基於 GAN 的高效異
    常檢測(Efficient GAN-Based Anomaly Detection, EGAN)方法生成模型。接著,我
    們搭配閥值的調整進行 AI 圖片異常判斷,實現異常檢測。
    生成 Golden Sample 的方法可以顯著提高自動光學檢查檢驗後人工檢驗的準確率,
    ;In today′s semiconductor wafer manufacturing industry, Automatic Optical
    Inspection (AOI) has become a key technology. However, due to the size of wafers and
    the constraints of equipment capabilities, even though AOI technology has made
    significant progress in image inspection, it still cannot fully meet customer
    requirements. This results in a high dependency on manual inspection to ensure
    accuracy. This manual inspection process is both time-consuming and costly, leading
    to increased production costs. Hence, automating the inspection process to reduce costs
    using Artificial Intelligence (AI) technology is the main direction of this research.
    This paper discusses an optimization method for post-AOI manual identification
    assistance based on deep learning. The aim is to meet customer needs, reduce manual
    inspections, improve inspection accuracy, and achieve rapid deployment and inspection.
    To address this challenge, we first labeled data through an established manual
    inspection platform. With balanced labeled data, we adopted the Convolution and
    Attention Network (CoAtNet) approach for AI image classification. In cases of
    unbalanced labeled data, we employed the Adversarial Autoencoder (AAE) and
    Efficient GAN-Based Anomaly Detection methods for model generation. We then
    paired this with threshold adjustments for AI image anomaly determination, realizing
    anomaly detection.
    Through a series of experiments, we demonstrated that combining the cooperative
    attention network with the adversarial autoencoder to generate Golden Samples can
    significantly enhance the accuracy of post-AOI manual inspections, reduce the number
    of manual inspections, and meet customer needs. This research provides an effective
    and practical solution for image inspection and has a broad application prospect.
    Appears in Collections:[資訊管理研究所] 博碩士論文

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