標題: A neural-network approach for semiconductor wafer post-sawing inspection
作者: Su, CT
Yang, T
Ke, CM
工業工程與管理學系
Department of Industrial Engineering and Management
關鍵字: defect;neural network;post-sawing inspection;semiconductor wafer
公開日期: 1-五月-2002
摘要: Semiconductor wafer post-sawing requires full inspection to assure defect-free outgoing dies. A defect problem is usually identified through visual judgment by the aid of a scanning electron microscope. By this means, potential misjudgment may be introduced into the inspection process due to human fatigue. In addition, the full inspection process can incur significant personnel costs. This research proposed a neural-network approach for semiconductor wafer post-sawing inspection. Three types of neural networks: backpropagation, radial basis function network, and learning vector quantization, were proposed and tested. The inspection time by the proposed approach was less than one second per die, which is efficient enough for a practical application purpose. The pros and cons for the proposed methodology in comparison with two other inspection methods, visual inspection and feature extraction inspection, are discussed. Empirical results showed promise for the proposed approach to solve real-world applications. Finally, we proposed a neural-network-based automatic inspection system framework as future research opportunities.
URI: http://dx.doi.org/10.1109/66.999602
http://hdl.handle.net/11536/28819
ISSN: 0894-6507
DOI: 10.1109/66.999602
期刊: IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING
Volume: 15
Issue: 2
起始頁: 260
結束頁: 266
顯示於類別:期刊論文


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  1. 000175398400019.pdf