This paper discusses using Neural Networks for diagnosing circuit faul
ts.,Ss a circuit is tested, the output signals from a Unit Under Test
can vary as different functions are invoked by the test. When plotted
against time, these signals create a characteristic trace for the test
performed, Sensors in the ATS can be used to monitor the output signa
ls during test execution, Using such an approach, defective components
can be classified using a Neural Network according to the pattern of
variation from that exhibited by a known good card. This provides a me
ans to develop testing strategies for circuits based upon observed per
formance rather than domain expertise. Such capability is particularly
important with systems whose performance. especially under faulty con
ditions, is not well documented or where suitable domain knowledge and
experience does not exist, Thus, neural network solutions may, in som
e application areas, exhibit better performance than either convention
al algorithms or knowledge-based systems. They may also be retrained p
eriodically as a background function, resulting with the network gaini
ng accuracy over time.