Design for testability using behavioral models

Spalding, George R.; VanPeteghem, P.M.; Brooks, Todd L.

doi:10.1109/imtc.1990.65964

Cited by 10 publications

(3 citation statements)

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“…Circuit partitioning into its behavioral blocks has a number of advantages from the points of view of both system design and testing, as discussed, e.g., in [4,18,19]. The lack of efficient methods for parametric diagnosis of analog behavioral blocks has been pointed out in this literature.…”

Section: Ota-c Filtermentioning

confidence: 99%

“…The present work is an attempt to find a solution to this problem. In order to maintain continuity of the discussion originated in [ 18] the same circuit example is used, as shown in Fig. 8.…”

Section: Ota-c Filtermentioning

confidence: 99%

“…8. However, instead of using the frequency-domain measurements for circuit parameter identification [18,20], we will employ time-domain testing in this paper, for the reasons explained in Introduction. Suppose there are 4 unknown parameters of the CUT in this example, namely 0 = (gi, gd, gml, gin2) I.…”

Section: Ota-c Filtermentioning

confidence: 99%

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Parametric testing of mixed-signal circuits by ANN processing of transient responses

Materka

Strzelecki

1996

J Electron Test

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It is postulated that feedforward artificial neural networks can be used for fast and robust parametric testing of mixed-signal circuits when applied to the processing of transient waveforms which are circuit responses to test signals. Numerical and experimental results are presented to verify the validity of the technique using examples of OPAMP and OTA-C filters and of a CMOS inverter. A feedforward artificial neural network in the form of a single-hidden-layer sigmoidal perceptron is trained in this preliminary study to estimate the circuit parameters. Since no iterative calculations are performed to identify parameter values with this technique, it is highly suitable for high-speed parametric testing. It is also more robust in the presence of noise when compared to traditional approaches. Topics for future research are addressed.

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Section: Ota-c Filtermentioning

confidence: 99%

Section: Ota-c Filtermentioning

confidence: 99%