23 October 2000 Novel resource optimization approach for yield learning
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Proceedings Volume 4229, Microelectronic Yield, Reliability, and Advanced Packaging; (2000) https://doi.org/10.1117/12.404887
Event: International Symposium on Microelectronics and Assembly, 2000, Singapore, Singapore
Abstract
In this paper, we describe a new integrated framework for yield learning, based on linking traditional inspection sampling, and current ADC classification procedures. The elements of a yield learning cycle, and the drivers, are identified. We then review results concerning integrated inspection-classification/review procedures that reduce yield loss detection; these incorporate new optimized control charts that incorporate killer and non-killer defect types, with classification errors, as well as integrated queuing-hypothesis testing approaches combining resource management and excursion detection. We briefly touch upon tactical approaches for achieving source isolation and prioritizing source isolation and root cause analysis.
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Ramakrishna Akella, "Novel resource optimization approach for yield learning", Proc. SPIE 4229, Microelectronic Yield, Reliability, and Advanced Packaging, (23 October 2000); doi: 10.1117/12.404887; https://doi.org/10.1117/12.404887
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