Fault diagnosis of analog circuits based on machine learning

Authors:
Ke Huang;Haralampos-G. Stratigopoulos;Salvador Mir
Affiliations:
TIMA Laboratory (CNRS-Grenoble INP-UJF), Grenoble, France;TIMA Laboratory (CNRS-Grenoble INP-UJF), Grenoble, France;TIMA Laboratory (CNRS-Grenoble INP-UJF), Grenoble, France
Venue:
Proceedings of the Conference on Design, Automation and Test in Europe
Year:
2010

Citing 7
Cited 4

An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Test generation based diagnosis of device parameters for analog circuits

Proceedings of the conference on Design, automation and test in Europe
Analog Circuit Fault Diagnosis Based on Sensitivity Computation and Functional Testing

IEEE Design & Test
Non-RF to RF Test Correlation Using Learning Machines: A Case Study

VTS '07 Proceedings of the 25th IEEE VLSI Test Symmposium
Defect Filter for Alternate RF Test

ETS '09 Proceedings of the 2009 European Test Symposium
Fault diagnosis of electronic systems using intelligent techniques: a review

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Error Moderation in Low-Cost Machine-Learning-Based Analog/RF Testing

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Embedded RF Circuit Diagnostic Technique with Multi-Tone Dither Scheme

Journal of Electronic Testing: Theory and Applications
A New Analog Circuit Fault Diagnosis Method Based on Improved Mahalanobis Distance

Journal of Electronic Testing: Theory and Applications
Prognostics of Analog Filters Based on Particle Filters Using Frequency Features

Journal of Electronic Testing: Theory and Applications
An Approximate Calculation of Ratio of Normal Variables and Its Application in Analog Circuit Fault Diagnosis

Journal of Electronic Testing: Theory and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

We discuss a fault diagnosis scheme for analog integrated circuits. Our approach is based on an assemblage of learning machines that are trained beforehand to guide us through diagnosis decisions. The central learning machine is a defect filter that distinguishes failing devices due to gross defects (hard faults) from failing devices due to excessive parametric deviations (soft faults). Thus, the defect filter is key in developing a unified hard/soft fault diagnosis approach. Two types of diagnosis can be carried out according to the decision of the defect filter: hard faults are diagnosed using a multi-class classifier, whereas soft faults are diagnosed using inverse regression functions. We show how this approach can be used to single out diagnostic scenarios in an RF low noise amplifier (LNA).