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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/91596


    Title: 導入製程健康指標於半導體封裝製造成本之管理;Applying process health index for cost management on semiconductor packaging industry
    Authors: 徐千琇;HSU, CHIENHSIU
    Contributors: 工業管理研究所
    Keywords: 健康指標;馬氏-田口系統;製造成本;Health Index;MTS;cost management
    Date: 2023-06-08
    Issue Date: 2023-10-04 14:36:35 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 半導體產業提供高效的電子元件和系統,大幅提升現代科技產品效能和日常生活手機與家用電器的便利。近年來人工智慧、雲端技術在資料中心的應用持續增長,以及電動汽機車的發展,促進人工智慧晶片、感測器和記憶體等各種不同功能晶片需求增長。台灣的半導體因完善的半導體產業供應鏈,其表現優於全球,產值占全球半導體產值之26.2%,位居世界第二。尤其在先進產品的晶圓代工與封測代工業務全球市占率第一,半導體更是台灣經濟成長的主要產業。為維持其競爭力,需在原有優勢的彈性高、速度快、客製化服務、低成本的生產模式不斷的改善與精進。
    本研究應用作業管理中精實作業的架構,藉由減少事務的處理次數與頻率來降低成本。蒐集健康指標相關文獻,並利用馬氏-田口系統求解模組,研究因應AVI作業特性,以及生產過程中機台不同狀況下,建立管理重工作業發生之機率的工具。透過模組設立生產時監控的健康指標,讓產線執行時可以區隔異常狀況,及時請工程師處置,進而減少重工浪費,改善現行AVI作業成本。
    研究結果比較導入前後作業方式,導入前因無相關規則讓產線在有異常狀態時及時暫停,需等產品至最終檢驗時若有不合格,才再安排調整與重新檢驗。導入後將健康指標規則設定於製造執行系統扣留邏輯,在異常觸發時即通知相關單位處理,減少最終檢驗時不合格數量以及重工作業負荷。;The semiconductor industry provides efficient electronic components and systems, significantly enhancing the performance of modern technological products and the convenience of everyday life, such as mobile phones and home appliances. In recent years, the continued growth in the application of artificial intelligence and cloud technology in data centers, along with the development of electric vehicles, has driven the increasing demand for various types of chips, including AI chips, sensors, and memory.
    Semiconductors are also a key industry driving Taiwan′s economic growth. To maintain its competitiveness, continuous improvement and refinement are necessary in the existing production models, which emphasize flexibility, speed, customized services, and low cost, leveraging their inherent advantages.
    This study applies the framework of lean operations in operations management to reduce costs by minimizing the frequency and number of transaction processing. By collecting relevant literature on health indicators and utilizing the Mahalanobis-Taguchi system to solve the module, it investigates the characteristics of AVI operations and establishes a tool for managing the probability of occurrence of heavy rework during the production process under different machine conditions. By establishing health indicators for monitoring during production, abnormal conditions can be identified on the production line, enabling timely intervention by engineers to reduce rework waste and improve the current cost of AVI operations.
    The study found that by setting health indicator rules in the manufacturing execution system′s hold logic, relevant units are notified for processing when abnormalities are detected. This reduces the number of non-conforming units and the burden of rework operations during final inspections.
    Appears in Collections:[Graduate Institute of Industrial Management] Electronic Thesis & Dissertation

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