Sponsor
Portland State University. Department of Electrical and Computer Engineering
First Advisor
W. Robert Daasch
Date of Publication
Winter 6-10-2013
Document Type
Thesis
Degree Name
Master of Science (M.S.) in Electrical and Computer Engineering
Department
Electrical and Computer Engineering
Language
English
Subjects
Integrated circuits -- Testing, Integrated circuits -- Quality control, Production management
DOI
10.15760/etd.1049
Physical Description
1 online resource (vi, 62 pages)
Abstract
Test cost is a critical component in the overall cost of the product. Test cost varies in direct proportion with test time. This thesis introduces a data driven feed forward adaptive technique for reducing test time at wafer sort while maintaining the product defect level. Test data from first insertion of wafer is statistically analyzed to make a decision about adaptive test flow at subsequent insertions.
The data driven feed forward technique uses a statistical screen to analyze test data from first probe of wafer and provides recommendations for test elimination at second insertions. At the second insertion dies are subjected to only the optimum number of tests for a reduced test flow. This technique is applicable only for the products which are tested at two or more insertions.
The statistical screen identifies the dies for reduced test flow based upon correlation of tests across insertions. The tests which are repeated at both the insertions and are highly correlated are the candidates of elimination at second insertion.
The feed forward technique is applied to a mixed signal analog product and figures of merit are evaluated. Application of technique to the production data shows that there is an average 55% test time reduction when a single site is tested per touchdown and up to 10% when 16 sites are tested in parallel per touchdown.
Rights
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Persistent Identifier
http://archives.pdx.edu/ds/psu/9904
Recommended Citation
Chandorkar, Chaitrali Santosh, "Data Driven Feed Forward Adaptive Testing" (2013). Dissertations and Theses. Paper 1049.
https://doi.org/10.15760/etd.1049