Using a self-organizing neural network for wafer defect inspection

Wafer defect inspection is an important process before die packaging. The defective regions are usually identified through visual judgment with the aid of a scanning electron microscope. Five to ten people visually check wafers and hand-mark their defective regions. By this means, potential misjudgm...

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Bibliographic Details
Main Authors: CHANG, Chuan-Yu, CHANE, Jia-Wei, JENG, Muder
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Circuit testing
Computer science
control theory
systems
Control theory. Systems
Costs
Electronics packaging
Exact sciences and technology
Fatigue
Inspection
Marine technology
Microscopy
Neural networks
Oceans
Sawing
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Summary:Wafer defect inspection is an important process before die packaging. The defective regions are usually identified through visual judgment with the aid of a scanning electron microscope. Five to ten people visually check wafers and hand-mark their defective regions. By this means, potential misjudgment may be introduced due to human fatigue. In addition, the process can incur significant personnel costs. Self-organizing neural networks (SONN) have been proven to have the capabilities of auto-clustering. Automated wafer inspection based on a self-organizing neural network is proposed to replace the traditional electrical testing and the human inspection process. Based on real world data, experimental results show that the proposed method successfully identifies the defective regions on wafers with good performances.
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2004.1401209