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Analogue electronic circuit diagnosis based on ANNs

Litovski, V, Andrejevic, M and Zwolinski, M, Stojadinovic, N(ed.) (2006) Analogue electronic circuit diagnosis based on ANNs Microelectronics Reliability, 46, (8), pp. 1382-1391.

Record type: Article


Feed-forward artificial neural networks (ANNs) have been applied to the diagnosis of nonlinear dynamic analogue electronic circuits. Using the simulation-before-test (SBT) approach, a fault dictionary was first created containing responses observed at all inputs and outputs of the circuit. The ANN was considered as an approximation algorithm to capture mapping enclosed within the fault dictionary and, in addition, as an algorithm for searching the fault dictionary in the diagnostic phase. In the example given DC and small signal frequency domain measurements were taken as these data are usually given in device’s data-sheets. A reduced set of data per fault (DC output values, the nominal gain and the 3 dB cut-off frequency, measured at one output terminal) was recorded. Soft (parametric) and catastrophic (shorts and opens) defects were introduced and diagnosed simultaneously and successfully. Large representative set of faults was considered, i.e., all possible catastrophic transistor faults and qualified representatives of soft transistor faults were diagnosed in an integrated circuit. The generalization property of the ANNs was exploited to handle noisy measurement signals.

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Published date: August 2006
Organisations: EEE


Local EPrints ID: 263410
ISSN: 0026-2714
PURE UUID: 31cdf34a-f3c2-462b-ad4d-a4375fbc308b
ORCID for M Zwolinski: ORCID iD

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Date deposited: 12 Feb 2007
Last modified: 18 Jul 2017 07:45

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Author: V Litovski
Author: M Andrejevic
Author: M Zwolinski ORCID iD
Editor: N Stojadinovic

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