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Author Yilmaz, E. ♦ Ozev, S. ♦ Butler, K.M.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2010
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations ♦ Applied physics
Subject Keyword Kernel ♦ Testing ♦ Training ♦ Equations ♦ Estimation ♦ Object recognition ♦ Correlation
Abstract Despite their small size, analog/mixed-signal circuits start with an extensive set of parameters to test for. During production ramp up, most of these tests are dropped using statistical analysis techniques based on the dropout patterns. While effective in reducing the number of tests, this approach treats each device in an identical manner. As the statistical diversity of the devices increases due to increasing process variations, such homogeneous testing approaches may prove to be inefficient. After a number of initial measurements, device-specific information is available, which can provide clues as to where in the process space that device falls. Using this information, the test set for each device can be tailored with respect to its own statistical information. In this paper, we present an adaptive test flow for mixed-signal circuits that aims at optimizing the test set per-device basis so that more test resources can be devoted to marginal devices whereas devices that fall in the middle of the process space are passed with less testing. We also include provisions to identify potentially defective devices and test them more extensively since these devices do not conform to learned collective information. We conduct experiments on an LNA circuit in simulations and apply our techniques to production data of two distinct industrial circuits. Both the simulation results and the results on large-scale production data show that adaptive test provides the best trade-off between test time and test quality as measured in terms of defective parts per million.
Description Author affiliation: Texas Instruments, USA (Butler, K.M.) || Arizona State University, USA (Yilmaz, E.; Ozev, S.)
ISBN 9781424472062
ISSN 10893539
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2010-11-02
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781424472079
Size (in Bytes) 926.11 kB
Page Count 10
Starting Page 1
Ending Page 10


Source: IEEE Xplore Digital Library