Novel resource optimization approach for yield learning

Ramakrishna Akella

doi:10.1117/12.404887

23 October 2000 Novel resource optimization approach for yield learning

Ramakrishna Akella

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.

Citation Download Citation

Ramakrishna Akella "Novel resource optimization approach for yield learning", Proc. SPIE 4229, Microelectronic Yield, Reliability, and Advanced Packaging, (23 October 2000); https://doi.org/10.1117/12.404887

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