TY - GEN
T1 - Adaptive test flow for mixed-signal/RF circuits using learned information from device under test
AU - Yilmaz, Ender
AU - Ozev, Sule
AU - Butler, Kenneth M.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
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U2 - 10.1109/TEST.2010.5699271
DO - 10.1109/TEST.2010.5699271
M3 - Conference contribution
AN - SCOPUS:79951645588
SN - 9781424472055
T3 - Proceedings - International Test Conference
BT - Proceedings - International Test Conference 2010, ITC 2010
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st International Test Conference, ITC 2010
Y2 - 31 October 2010 through 5 November 2010
ER -