Application of Data Mining Based Scan Diagnosis Yield Analysis in a Foundry and Fabless Working Environment

For advanced technology nodes, the ability to improve the yield of a product in a short time dictates the success for both fabless and foundry. Identification of failure mechanisms in digital logic is frequently done using scan diagnosis driven yield analysis (DDYA) [1][2][3]. Recent works on root c...

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Bibliographic Details
Published inProceedings - Asian Test Symposium p. 128
Main Authors Shen, Hao, Shen, Lance, Xu, Pierce, Yang, Wu, Zhong, Junna
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2016
Subjects
Data mining
Deconvolution
diagnosis
Failure analysis
Foundries
Graphics
Manufacturing
pfa
RCD
root cause
yield analysis
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ISSN2377-5386
DOI10.1109/ATS.2016.66

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Summary:For advanced technology nodes, the ability to improve the yield of a product in a short time dictates the success for both fabless and foundry. Identification of failure mechanisms in digital logic is frequently done using scan diagnosis driven yield analysis (DDYA) [1][2][3]. Recent works on root cause deconvolution (RCD) [4][5] discussed a Bayes net model based diagnosis data learning algorithm. Combining RCD and diagnosis suspect location analysis address key considerations in applying DDYA in a production environment.
ISSN:2377-5386
DOI:10.1109/ATS.2016.66