NEURAL-NETWORK APPROACH TO FAULT-DIAGNOSIS IN CMOS OPAMPS WITH GATE OXIDE SHORT FAULTS

Authors
YU S JERVIS BW ECKERSALL KR BELL IM HALL AG TAYLOR GE
Citation
S. Yu et al., NEURAL-NETWORK APPROACH TO FAULT-DIAGNOSIS IN CMOS OPAMPS WITH GATE OXIDE SHORT FAULTS, Electronics Letters, 30(9), 1994, pp. 695-696
Citations number
4
Categorie Soggetti
Engineering, Eletrical & Electronic
Journal title
Electronics Letters → ACNP
ISSN journal
00135194
Volume
30
Issue
9
Year of publication
1994
Pages
695 - 696
Database
ISI
SICI code
0013-5194(1994)30:9<695:NATFIC>2.0.ZU;2-I
Abstract
Faults owing to gate oxide shorts in a CMOS opamp have been diagnosed in simulations using artificial neural networks to identify correspond ing variations in supply current. Ramp and sinusoidal signals gave fau lt diagnostic accuracy of 67 and 83%, respectively. Using both test si gnals 100% diagnostic accuracy was achieved.