Using Supervised Artificial Neural Networks for Die Defect Detection

• Main Authors: Ke, Chir-mour, 柯岐謀
• Other Authors: Su, Chao-Ton
• Format: Others
• Language: zh-TW
• Published: 2001
• Online Access: http://ndltd.ncl.edu.tw/handle/01118440805673560946

碩士 === 國立交通大學 === 工業工程與管理系 === 89 === Defects commonly occur on the surface of a wafer during production owing to carelessness of the operator, poor quality of equipment and inadequate environment. Such defects negatively impact subsequent operations. To maintain the die quality, factories normally visually inspect dies after wafer sawing, subsequently leading to a significant amount of manpower, material resources and operation area and ultimately making it extremely difficult to maintain the inspection quality. This research presents an effective procedure capable of detecting die defects by using supervised neural networks. A case study involving the wafer production demonstrates the effectiveness and practicability of the proposed approach.