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We propose a post-fabrication calibration technique for RF circuits that is performed during production testing with minimum extra cost. Calibration is enabled by equipping the circuit with tuning knobs and sensors. Optimal tuning knob identification is achieved in one-shot based on a single test step that involves measuring the sensor outputs once. For(More)
Machine-learning-based test methods for analog/RF devices have been the subject of intense investigation over the last decade. However, despite the significant cost benefits that these methods promise, they have seen a limited success in replacing the traditional specification testing, mainly due to the incurred test error which, albeit small, cannot meet(More)
AbshrccCWe present a case study that employs production test data from an RF device to assess the effectiveness of four different methods in predicting the padfail labels of fabricated devices based on a subset of performances and, thereby, in decreasing test cost. The device employed is a zero-IF down-converter for cell-phone applications and the four(More)
A neural classifier that learns to separate the nominal from the faulty instances of a circuit in a measurement space is developed. Experimental evidence, which demonstrates that the required separation boundaries are, in general, nonlinear, is presented. Unlike previous solutions that build hyperplanes, the proposed classifier is capable of drawing(More)
We present a method that is capable of handling process variations to evaluate analog/RF test measurements at the design stage. The method can readily be used to estimate test metrics, such as parametric test escape and yield loss, with parts per million accuracy, and to fix test limits that satisfy specific tradeoffs between test metrics of interest.(More)
—A stand-alone built-in self-test architecture mainly consists of three components: a stimulus generator, measurement acquisition sensors, and a measurement processing mechanism to draw out a straightforward Go/No-Go test decision. In this paper, we discuss the design of a neural network circuit to perform the measurement processing step. In essence, the(More)