On the application of symbolic techniques to the multiple fault location in low testability analog circuits

Fedi, G.; Giomi, R.; Luchetta, Antonio; Manetti, S.; Piccirilli, Maria Cristina

doi:10.1109/82.728851

Cited by 49 publications

(39 citation statements)

References 15 publications

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“…Starting from a set of measurements corresponding to the values of the network function in a selected set of frequencies, it is possible to determine the coefficients of the network function, for example by means of a LSE (Least Squared Error) procedure [21]. At this point a nonlinear system expressing these coefficients as functions of the unknown circuit parameters can be considered.…”

Section: Identificationmentioning

confidence: 99%

Analog system modeling based on a double modified complex valued neural network

Luchetta

Manetti

Piccirilli

2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

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Abstract-The aim of this work is to present a novel technique for the identification of lumped circuit models of general distributed apparatus and devices. It is based on the use of a double modified complex value neural network. The method is not oriented to a unique class of electromagnetic systems, but it gives a procedure for the complete validation of the approximated lumped model and the extraction of the electrical parameter values. The inputs of the system are the geometrical (and/or manufacturing) parameters of the considered structure, while the outputs are the lumped circuit parameters. The method follows the Frequency Response Analysis (FRA) approach for elaborating the data presented to the network. I. INTRODUCTIONURING the analog circuit design process, in many cases the phase of modelization, parameter identification and simulation of distributed circuits is still an arduous challenge. The difficulties are inherent in locating the parameters that can be extracted and in obtaining the requested precision for them. On the other hand, this phase can become essential to solve several problems, as, for example, the study of the transient part of the response, the evaluation of the electromagnetic compatibility, the estimate of the harmonic content, the detection and location of faults, the influence of a single parameter over the output final behavior. In the last few years several soft computing algorithms dealing with this subject have been developed, using artificial neural networks (ANNs) [1][2][3], genetic algorithms (GAs) [4,5], particle swarm optimizers (PSOs) [6,7].In this paper we propose a new neural architecture able to accomplish the identification task. It is constituted by the union of a pair of multi-valued neuron neural networks with complex weights [8]. In this kind of architecture it is possible to use a set of multifrequency measurements or simulations made on the device, taken at different values of geometrical parameters, and train a MultiLayer Multi-Valued Neuron Network (MLMVN), able to estimate the electrical parameters of the lumped model. A single network is not A. Luchetta is with the Dipartimento di Ingegneria dell'Informazione (DINFO) of the University of Florence, Via S.Marta, 3, 50139 Firenze, Italy (phone: +39-055-4796461; fax: +39-055-4796442; e-mail: luchetta@ unifi.it).S. Manetti is with the Dipartimento di Ingegneria dell'Informazione (DINFO) of the University of Florence, Via S.Marta, 3, 50139 Firenze, Italy (e-mail: stefano.manetti@ unifi.it).M.C. Piccirilli is with the Dipartimento di Ingegneria dell'Informazione (DINFO) of the University of Florence, Via S.Marta, 3, 50139 Firenze, Italy (e-mail: mariacristina.piccirilli@ unifi.it). sufficient to achieve the goal, because, in general, the supervised reference of the lumped model is not available. In order to overcome this limitation, another network must be added, having the role of approximating and "inverting" a network function of the chosen lumped model. The output of this layer consists of the numerical estim...

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Section: Identificationmentioning

confidence: 99%

Analog system modeling based on a double modified complex valued neural network

Luchetta

Manetti

Piccirilli

2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

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“…The first approach is named simulation after test (SAT) [3]- [5]: fault isolation is obtained by estimating the circuit parameters from the measured circuit outputs. The identification of the circuit parameters is based on the assumption that enough information is available in the measurements and that measurements are mutually independent.…”

Section: Introductionmentioning

confidence: 99%

SBT soft fault diagnosis in analog electronic circuits: a sensitivity-based approach by randomized algorithms

Alippi

Catelani

Fort

et al. 2002

IEEE Trans. Instrum. Meas.

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Abstract-This paper addresses the fault diagnosis issue based on a simulation before test philosophy in analog electronic circuits. Diagnosis, obtained by comparing signatures measured at the test nodes with those contained in a fault dictionary, allows for sub-systems testing and fault isolation within the circuit. A novel method for constructing the fault dictionary under the single faulty component/unit hypothesis is proposed. The method, based on a harmonic analysis, allows for selecting the most suitable test input stimuli and nodes by means of a global sensitivity approach efficiently carried out by randomized algorithms. Applicability of the method to a wide class of circuits and its integration in diagnosis tools are granted since randomized algorithms assure that the selection problem can be effectively carried out with a poly-time algorithm independently from the fault space, structure, and complexity of the circuit.

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“…Several papers have been focused on the multiple fault diagnosis, e.g. [11,14,16,17,19,20]. Although many achievements in this field have been made, the problem is still open and no fully automatic method is available for analog circuits.…”

Section: Introductionmentioning

confidence: 99%

“…During the last several years, many methods devoted to soft fault diagnosis of analog circuits have been developed [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. Appropriate tools for soft-fault diagnosis are SAT methods.…”

Section: Introductionmentioning

confidence: 99%

Multiple Soft Fault Diagnosis of Nonlinear Circuits Using the Continuation Method

Tadeusiewicz

Hałgas

2012

J Electron Test

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This paper offers a method for multiple soft fault diagnosis of nonlinear static circuits. The method enables us to locate the faulty elements and evaluate their parameters. It exploits a set of n nonlinear algebraic type diagnostic equations in n unknown variables and is oriented on finding multiple solutions of these equations. As a result, the method is capable of finding, in systematic manner, different sets of the parameters which satisfy the diagnostic test, rather than one specific set. For this purpose the continuation (homotopy) approach is applied and an efficient procedure for tracing a homotopy path is developed. The proposed method is especially useful at the pre-production stage, where corrections of the technological process are possible and the diagnostic time is not crucial. To illustrate the proposed approach two numerical examples are given.

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On the application of symbolic techniques to the multiple fault location in low testability analog circuits

Cited by 49 publications

References 15 publications

Analog system modeling based on a double modified complex valued neural network

Analog system modeling based on a double modified complex valued neural network

SBT soft fault diagnosis in analog electronic circuits: a sensitivity-based approach by randomized algorithms

Multiple Soft Fault Diagnosis of Nonlinear Circuits Using the Continuation Method

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