Neural network approach to fault diagnosis in CMOS opamps with gate oxide short faults

@inproceedings{Yu1994NeuralNA,
  title={Neural network approach to fault diagnosis in CMOS opamps with gate oxide short faults},
  author={Sunjin Yu and Barrie W. Jervis and Kevin R. Eckersall and Ian M. Bell and A. G. Hall and George E. Taylor},
  year={1994}
}
Faults owing to gate oxide shorts in a CMOS opamp have been diagnosed in simulations using artificial neural networks to identify corresponding variations in supply current. Ramp and sinusoidal signals gave fault diagnostic accuracy of 67 and 83%, respectively. Using both test signals 100% diagnostic accuracy was achieved. 

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