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In this paper, we apply artificial neural networks (ANNs) to the diagnosis of  a mixed-mode electronic circuit. In order to tackle the circuit complexity  and to reduce the number of test points hierarchical approach to the  diagnosis generation was implemented with two levels of decision: the system  level and the circuit level. For every level, using the  simulation-before-test (SBT) approach, fault dictionary was created first,  containing data relating the fault code and the circuit response for a given  input signal. Also, hypercomputing was implemented, i.e. we used parallel  simulation of large number of replicas of the original circuit with faults  inserted to achieve fast creation of the fault dictionary. ANNs were used to  model the fault dictionaries. At the topmost level, the fault dictionary was  split into parts simplifying the implementation of the concept. During the  learning phase, the ANNs were considered as an approximation algorithm to  capture the mapping enclosed within the fault dictionary. Later on, in the  diagnostic phase, the ANNs were used as an algorithm for searching the fault  dictionary. A voting system was created at the topmost level in order to  distinguish which ANN output is to be accepted as the final diagnostic  statement. The approach was tested on an example of an analog-to-digital  converter

Hierarchical approach to diagnosis of electronic circuits using ANNs