Stimulus with Limited Band Optimization for Analogue Circuit Testing

Abstract

The paper presents an analogue circuit testing method that engages the analysis of the time response to a nonperiodic stimulus specialized for the verification of selected specifications. The decision about the current circuit diagnostic state depends on an amplitude spectrum decomposition of the time response measured during the test. A shape of the test excitation spectrum is optimized with the use of a differential evolution algorithm and it allows for achieving maximum fault coverage and the optimal conditions for fault isolation. Genotypes of the evolutionary system encode the amplitude spectrum of candidates for testing stimuli by means of rectangle frequency windows with amplitudes determined evolutionarily.

Analog testing stimulus optimization for generating by means of Sigma-Delta modulation

“…The analog electronic circuits (AECs) testing and diagnosing are important research domains [1][2][3][4][5]. The integrated analog circuit quality as well as the final device reliability depends directly on the control regime applied on the each stage of the product life cycle.…”

“…This fact leads to the motivation of heuristic computational algorithm usage [4][5][6]. The exemplary, published previously concepts engage the neural networks, ant algorithms as well as evolutionary techniques (genetic algorithm, evolutionary strategies, genetic programming, etc.…”

Analog Circuit Specification Testing by Means of Walsh–Hadamard Transform and Multiple Regression Supported by Evolutionary Computations

“…Firstly, before starting the optimization system, all the necessary initial assumptions for variables listed in the first column of Terminal or node mutation probability P MU2 Tree global mutation probability P REM Minimal probability of reproduction assumed TIM END , FREQ END and N from Table 1) are carried out for all the K teaching patterns. The frequency responses calculated in this way are used to the CUT actual performance parameters determinations (e.g., cutoff frequencies and maximal gains in assumed frequency band); however, related to their respective time step responses are transformed to the N points WHT spectra and they create the diagnostic signatures set (7). Now, the K random pairs of the considered CUT parameters set (1) patterns and the respective sequence spectra are ready and the evolutionary creation of the statistical model may be started.…”

“…In [11,19,35], there are the techniques that use support vector machines for faults detection. Heuristic computations and statistical analysis are used in [4,[6][7][8]13,18,20] for catastrophic and global parametric faults detection as well as for optimal test points and testing signal shape searching, respectively. In [34], the entropy parameter is used to analog circuit soft faults detections.…”

An Efficient Method of Group Delay Equalization for Digital IIR Filters

“…They are widely used thanks to their natural and intuitive procedures. Concisely stated, a genetic algorithm is an attempt to copy natural and biological aspects of evolution expressed in a chosen programming language [11], [12].…”