Yield Learning Through Physically Aware Diagnosis of IC-Failure Populations
A variety of yield-learning techniques are essential since no single approach can effectively find every manufacturing perturbation that can lead to yield loss. Test structures, for example, can range from being simple in nature (combs and serpentine structures for measuring defect-density and size...
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Veröffentlicht in: | IEEE design & test of computers 2012-01, Vol.29 (1), p.36-47 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | A variety of yield-learning techniques are essential since no single approach can effectively find every manufacturing perturbation that can lead to yield loss. Test structures, for example, can range from being simple in nature (combs and serpentine structures for measuring defect-density and size distributions) to more complex, active structures that include transistors, ring oscillators, and SRAMs. Test structures are designed to provide seamless access to a given failure type: its size, its location, and possibly other pertinent characteristics. |
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ISSN: | 0740-7475 2168-2356 1558-1918 2168-2364 |
DOI: | 10.1109/MDT.2011.2178587 |