DocumentCode
1444135
Title
Yield Learning Through Physically Aware Diagnosis of IC-Failure Populations
Author
Blanton, Ronald DeShawn ; Tam, Wing Chiu ; Yu, Xiaochun ; Nelson, Jeffrey E. ; Poku, Osei
Author_Institution
Carnegie Mellon Univ., Pittsburgh, PA, USA
Volume
29
Issue
1
fYear
2012
Firstpage
36
Lastpage
47
Abstract
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.
Keywords
SRAM chips; failure analysis; integrated circuit testing; integrated circuit yield; learning (artificial intelligence); oscillators; transistors; IC-failure populations; SRAM; manufacturing perturbation; physically aware diagnosis; ring oscillators; test structures; transistors; yield learning; yield loss; Accuracy; Arrays; Failure analysis; Integrated circuits; Learning systems; Manufacturing processes; System testing; DFM; Yield; diagnosis; failure analysis; layout; learning; quality; scan; test;
fLanguage
English
Journal_Title
Design & Test of Computers, IEEE
Publisher
ieee
ISSN
0740-7475
Type
jour
DOI
10.1109/MDT.2011.2178587
Filename
6148305
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