利用資料探勘方式優化PCB良率改善流程 -以C公司為例;Using data mining methods to optimize the PCB yield improvement process - An empirical study for C. corporation

NCUIR > School of Management at National Central University > Executive Master of Industrial Management > Electronic Thesis & Dissertation > Item 987654321/89095

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/89095

Title:	利用資料探勘方式優化PCB良率改善流程 -以C公司為例;Using data mining methods to optimize the PCB yield improvement process - An empirical study for C. corporation
Authors:	江逸婷;Chiang, I-Ting
Contributors:	工業管理研究所在職專班
Keywords:	印刷電路板;良率;羅吉斯迴歸;類神經網路;決策樹;PCB;Logistic regression;Decision Tree;Neural network;Yield
Date:	2022-07-30
Issue Date:	2022-10-04 10:51:23 (UTC+8)
Publisher:	國立中央大學
Abstract:	2019年新冠疫情爆發，供應鏈中製造、組裝、運輸、倉儲和銷售等成本節節高升，對於低毛利的PCB 產業無疑是極大的挑戰，如何更即時監控產品品質、設備穩定性以及快速地分析產品缺陷進而導入相應的改善對策，是提升公司獲利的重要課題。 PCB檢驗站中光學AOI檢驗的缺點率誤差最大，原因在於光學檢驗有其設備能力的極限，對於細小的缺點難以有效檢出，因此當光學AOI檢驗時缺點數量越多，在相同機率下漏失的點數就越多，漏失的缺點到電性測試時就會成為短路、斷路缺點導致報廢，從AOI檢驗到電性測試缺點板分析完成至少需耗費20-30天，對於關鍵製程的改善顯然缺乏即時性，改善前的這段期間持續產生更多的缺點板，造成製造成本的浪費。因此，若能有效運用AOI檢驗的第一手資訊監控品質建立模型、展開關鍵製程改善行動並以模型預測電性測試良率進行改善確效，可以縮短缺點分析以及改善計畫展開的時程，並漸少不良品的產生數量和WIP (Work In Process)的製造成本。本研究之目的在於建立一準確、即時並可廣泛運用在各式產品的模型，透過資料庫中AOI檢驗基板短路、斷路的缺點數據，對應最終的電性測試短路、斷路資料，找出影響電性測試結果的因子，建立良率預估模型，用以加速品質改善流程。本文將以CRIPS-DM 資料探勘流程為基礎，分析PCB製造流程中AOI檢驗的缺點分類數據與電性測試的結果建立模型，確認兩者之間的相關性高低，並利用預估結果找出影響良率的關鍵因子，與實際結果是否相符合，使用SAS Enterprise Guide進行判別分析作資料前處理，以及SAS Enterprise miner比較羅吉斯迴歸分析、決策樹分析以及類神經網路分析各模型的準確率，挑選出合適的預測模型。;With the outbreak of Covid-19 in 2019, the cost of manufacturing, assembly, transportation, warehousing and sales in the supply chain has been rising, which is undoubtedly a great challenge for the low-margin PCB industry. How to monitor product quality, equipment stability, and Quickly analyzing product defects and then introducing corresponding improvement countermeasures is an important issue to improve the profits of the company. The optical AOI inspection error is the largest in all the PCB inspection station. The reason is the limit of its equipment capabilities, and it is difficult to effectively detect small defects. Therefore, once the amount of defeats increase, more small defects will be missed under the same probability. The missed defeats will become short and open defeats in electrical test, which will lead to scraps. It will take at least 20-30 days from AOI inspection to electrical defect board analysis, and the improvement of key processes is obviously lack of immediacy. More defects are continually generated before corrective action being taken, resulting in waste of manufacturing costs. Therefore, if we can effectively use the first-hand information of AOI inspection to monitor the quality and establish a model to predict the electrical test yield for improvement and validation, we can shorten the time of all improvements, and reduce the number of defective products and the manufacturing cost of WIP (Work In Process). The purpose of this research is to establish an accurate and immediate model that can be widely used in various products. According to the AOI inspection results, corresponding to the final electrical test short-circuit and open-circuit data, find out the factors that affect the electrical test results. Based on the CRIPS-DM process, this paper will use the defect data of the AOI inspection in the PCB manufacturing process and the results of the electrical test to establish a model, corroborate with the actual results, and confirm the correlation between the two inspections. In this research, SAS Enterprise Guide will be used in discriminant analysis for data preprocessing, and SAS Enterprise miner will be used to compare the accuracy of each model of Logistic regression analysis, decision tree analysis, and neural network-like analysis results, and to select a suitable model for prediction.
Appears in Collections:	[Executive Master of Industrial Management] Electronic Thesis & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	56	View/Open

社群 sharing

Loading...