Abstract
In modern electronic circuits, imperfectness in the technological process can cause errors in reaching the correct values of the functional parameters. In order to solve this problem, a novel approach of analog and mixed-signal circuit testing methodology is used. The presented approach allows the testing complexity to be reduced and the testing time to be decreased. For this paper, selected signal features were designated from the transient output signal response. Using regression models with the extracted signal features, the functional parameters of a circuit were determined. An evolutionary determination of the regression models enabled the efficiency of the identification process to be maximized. The proposed methodology is presented for an exemplary CMOS Dickson charge pump circuit.
Highlights
The rapid developments in electronic circuit design have not been accompanied by the same degree of progress in analog and mixed-signal electronic maintenance [1]
The standard production test approach for analog and mixedsignal circuits is based on measuring the functional parameters that are given on the data sheet and comparing them to the designed specifications in order to make the correct decision [2]–[6]
After identifying the selected functional parameters, a δi range of the correct parameter tolerance was imposed on each of the pi. This process allowed the accuracy of classification for each of the functional parameters of the presented method to be determined
Summary
The rapid developments in electronic circuit design have not been accompanied by the same degree of progress in analog and mixed-signal electronic maintenance [1]. The standard production test approach for analog and mixedsignal circuits is based on measuring the functional parameters that are given on the data sheet and comparing them to the designed specifications in order to make the correct (go/no-go) decision [2]–[6]. A wide range of methods for testing the functional parameters of analog circuits have been proposed in the literature. An optimization formulation approach is a commonly used tool that is based directly on the circuit structure [9]. In addition to neural networks, other artificial intelligence algorithms are commonly used to test the specification parameters: genetic algorithms [13]–[15], fuzzy logic systems [16], dependency models [14], etc
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