scholarly journals Testability analysis considerations of digital circuits.

1982 ◽  
Author(s):  
Steven Menzel
Keyword(s):  
Digital Circuits ◽  
Testability Analysis

ALL-OPTICAL DIGITAL CIRCUITS

1988 ◽  
Vol 49 (C2) ◽  
pp. C2-459-C2-462 ◽  
Author(s):  
F. A.P. TOOLEY ◽  
B. S. WHERRETT ◽  
N. C. CRAFT ◽  
M. R. TAGHIZADEH ◽  
J. F. SNOWDON ◽  
...  
Keyword(s):  
Digital Circuits ◽  
All Optical

Towards High Accuracy Fault Diagnosis of Digital Circuits

2004 ◽  
Author(s):  
Camelia Hora ◽  
Stefan Eichenberger
Keyword(s):  
Fault Diagnosis ◽  
Fault Location ◽  
Digital Circuits ◽  
High Accuracy ◽  
Manufacturing Processes ◽  
Diagnosis Method ◽  
Technology Trend

Abstract Due to the development of smaller and denser manufacturing processes most of the hardware localization techniques cannot keep up satisfactorily with the technology trend. There is an increased need in precise and accurate software based diagnosis tools to help identify the fault location. This paper describes the software based fault diagnosis method used within Philips, focusing on the features developed to increase its accuracy.

scholarly journals A Neural Network Classifier with Multi-Valued Neurons for Analog Circuit Fault Diagnosis

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 349
Author(s):  
Igor Aizenberg ◽  
Riccardo Belardi ◽  
Marco Bindi ◽  
Francesco Grasso ◽  
Stefano Manetti ◽  
...  
Keyword(s):  
Analog Circuits ◽  
Analog Circuit ◽  
Machine Learning Techniques ◽  
Rigorous Approach ◽  
Testability Analysis ◽  
Learning Techniques ◽  
Fabrication Tolerances ◽  
Circuit Fault Diagnosis ◽  
Faulty Component ◽  
Ambiguity Groups

In this paper, we present a new method designed to recognize single parametric faults in analog circuits. The technique follows a rigorous approach constituted by three sequential steps: calculating the testability and extracting the ambiguity groups of the circuit under test (CUT); localizing the failure and putting it in the correct fault class (FC) via multi-frequency measurements or simulations; and (optional) estimating the value of the faulty component. The fabrication tolerances of the healthy components are taken into account in every step of the procedure. The work combines machine learning techniques, used for classification and approximation, with testability analysis procedures for analog circuits.

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