Visual probe mark inspection, using hardware implementation of artificial neural networks, in VLSI production
As a result of their adaptability, artificial neural networks present good solutions for a permanently increasing range of industrials problems. So, if their usefulness has already been confirmed, very few papers deal with real applications of this kind of technology. Our goal is to present a neural based solution that we have developed for visual inspection in VLSI production for the IBM Essonnes plant. The main characteristics of such systems are real-time control and high reliability in detection and classification tasks. The presented system is based on a ZISC©, an IBM hardware implementation of the Restricted Coulomb Energy algorithm and of the K-Nearest Neighbor algorithm. The goal of the developed application is to inspect vias for probe damage during wafer tests: each via is analyzed and classified (good impact, bad impact or absence of impact). First results are really encouraging and show the efficiency of this system in manufacturing environment.
Keywordsartificial neural networks visual inspection fault detection realtime adaptivity
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