Abstract
This paper presents the application of an artificial neural network with a genetic algorithm for identifying the selected specification parameters of a voltage-controlled oscillator (VCO). In modern electronics, the complexity of the production process may cause errors in analogue and mixed-signal electronic circuits, and inaccuracies in this technological process have a direct impact on the specification parameters of a VCO. The modern market requires that the production process has to be as quick as possible, and therefore testing systems should be fast and have the highest efficiency of parameter identification. In the following paper, a genetic algorithm is used to optimise the number of output signal measurement points, which allows them to be identified by the specification parameters of the VCO that are selected by an artificial neural network. The proposed method is characterised by shortening the test time of the system while maintaining a high efficiency in the identification of the selected design specification parameters.DOI: http://dx.doi.org/10.5755/j01.eie.24.6.20945
Highlights
Fault diagnosis of analogue circuits is an important and still relevant element for the design validation of electronic devices [1]–[5]
This paper presents the use of an artificial neural network (ANN) to reduce the identification time of Voltage-Controlled Oscillator selected specification parameters
The presented paper discusses the problem of reducing the identification time of the selected specification parameters of a Voltage-Controlled Oscillator in its ring oscillator structure
Summary
Fault diagnosis of analogue circuits is an important and still relevant element for the design validation of electronic devices [1]–[5]. Analogue circuit testing can be divided into two categories: Fault Driven Testing (FDT), which detects faults in elements or Functional Test/Specification Driven Test (FT/SDT), where the functional behaviour of the circuit being tested is measured. While most of the works have been focused on the soft fault diagnosis of analogue circuits when only one parameter is faulty, fewer papers have been directed to multiple faults [8], [10]–[18]. This paper presents the use of an artificial neural network (ANN) to reduce the identification time of Voltage-Controlled Oscillator selected specification parameters. Based on the selected samples, the learned ANN determines the value of the selected specification parameters of the circuit under test (CUT). The last section describes the results of the research in detail
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