Mohamed Baker Alawieh

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In this paper, we propose a novel analog circuit optimization methodology for achieving high parametric yield. We solve the statistical worst-case optimization problem by a sequence of linear programings where performance metrics are fitted using sparse regression to take into account a large number of device-level parameters modeling process variations. In(More)
Today's automotive industry is making a bold move to equip vehicles with intelligent driver assistance features. A modern automobile is now equipped with a powerful computing platform to run multiple machine learning algorithms for environment perception (e.g., pedestrian detection) and motion control (e.g., vehicle stabilization). These machine learning(More)
In this paper, we propose a novel methodology for detecting systematic spatial failure patterns at wafer level for yield learning. Our proposed methodology takes the testing results (i.e., pass or fail) of a number of dies over different wafers, cluster all these wafers according to their failures, and eventually identify the underlying spatial failure(More)
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