Haralampos-G. D. Stratigopoulos

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—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, non-linear, is presented. Unlike previous solutions that build hyper-planes, the proposed classifier is capable of drawing(More)
We present a novel analog checker that adjusts dynamically the error threshold to the magnitude of its input signals. We demonstrate that this property is crucial for accurate concurrent error detection in analog circuits. Dynamic error threshold adjustment is achieved by regulating the bias point of the output stage inverters of the checker, which provide(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)
This paper discusses the generation of information-rich, arbitrarily-large synthetic data sets which can be used to (a) efficiently learn tests that correlate a set of low-cost measurements to a set of device performances and (b) grade such tests with parts per million (PPM) accuracy. This is achieved by sampling a non-parametric estimate of the joint(More)