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Proceedings Paper

Novel resource optimization approach for yield learning
Author(s): Ramakrishna Akella
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Paper 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.

Paper Details

Date Published: 23 October 2000
PDF: 9 pages
Proc. SPIE 4229, Microelectronic Yield, Reliability, and Advanced Packaging, (23 October 2000); doi: 10.1117/12.404887
Show Author Affiliations
Ramakrishna Akella, SUNY/Buffalo (USA) and Stanford Univ. (United States)

Published in SPIE Proceedings Vol. 4229:
Microelectronic Yield, Reliability, and Advanced Packaging
Cher Ming Tan; Yeng-Kaung Peng; Mali Mahalingam; Krishnamachar Prasad, Editor(s)

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