Simulation and Fault Diagnosis in Post-Manufacturing Mixed Signal Circuits

Pawlowski, Kyle; Chkravarty, Sumit; Joginipelly, Arjun Kumar

doi:10.23919/panpacific48324.2020.9059414

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some other studies on diagnosing analog circuit faults used neural networks and fuzzy logic, as shown by Bo et al [17], who used a negative feedback amplifier as the CUT. Simulation and deep learning were also used by Pawlowski et al [18] for identifying circuit faults in post-market circuit boards. In other studies, Li et al [19] used a radial basis function (RFB) neural network and a back propagation algorithm for fault detection in a differential amplifier circuit.…”

Section: Introductionmentioning

confidence: 99%

Application of a Pattern-Recognition Neural Network for Detecting Analog Electronic Circuit Faults

Dieste-Velasco

2021

Mathematics

View full text Add to dashboard Cite

In this study, machine learning techniques based on the development of a pattern–recognition neural network were used for fault diagnosis in an analog electronic circuit to detect the individual hard faults (open circuits and short circuits) that may arise in a circuit. The ability to determine faults in the circuit was analyzed through the availability of a small number of measurements in the circuit, as test points are generally not accessible for verifying the behavior of all the components of an electronic circuit. It was shown that, despite the existence of a small number of measurements in the circuit that characterize the existing faults, the network based on pattern-recognition functioned adequately for the detection and classification of the hard faults. In addition, once the neural network has been trained, it can be used to analyze the behavior of the circuit versus variations in its components, with a wider range than that used to develop the neural network, in order to analyze the ability of the ANN to predict situations different from those used to train the ANN and to extract valuable information that may explain the behavior of the circuit.

show abstract

Section: Introductionmentioning

confidence: 99%

Application of a Pattern-Recognition Neural Network for Detecting Analog Electronic Circuit Faults

Dieste-Velasco

2021

Mathematics

View full text Add to dashboard Cite

show abstract

Simulation and Fault Diagnosis in Post-Manufacturing Mixed Signal Circuits

Cited by 1 publication

References 9 publications

Application of a Pattern-Recognition Neural Network for Detecting Analog Electronic Circuit Faults

Application of a Pattern-Recognition Neural Network for Detecting Analog Electronic Circuit Faults

Contact Info

Product

Resources

About