Title :
Neural recognition of diagnostic test data transforms
Author_Institution :
JKS Syst. Ltd., Westlake Village, CA, USA
Abstract :
By extending the concept of fault signatures on the primary outputs of the UUT to include the multiple parameters required of mixed signal testing, a fault dictionary approach to mixed signal UUT diagnostics can be developed. Transforms of fault signature ensemble information, as opposed to transforms of the time varying test signals themselves, can then be used as inputs to a neural net, the outputs of which are available to enhance conventional, fault dictionary processing of the original fault signature information
Keywords :
automatic test equipment; fault diagnosis; integrated circuit testing; mixed analogue-digital integrated circuits; neural nets; pattern recognition; transforms; UUT; diagnostic test data transforms; fault dictionary approach; fault dictionary processing; fault signature information; fault signatures; mixed signal UUT diagnostics; mixed signal testing; multiple parameters; neural net; neural recognition; primary outputs; time varying test signals; Automatic programming; Automatic testing; Data structures; Dictionaries; Fault detection; Humans; Manuals; Neural networks; Sequential analysis; Signal processing;
Conference_Titel :
AUTOTESTCON '94. IEEE Systems Readiness Technology Conference. 'Cost Effective Support Into the Next Century', Conference Proceedings.
Conference_Location :
Anaheim, CA
Print_ISBN :
0-7803-1910-9
DOI :
10.1109/AUTEST.1994.381587