Title :
Fault diagnosis of analog circuits using artificial neural networks as signature analyzers
Author :
Spina, Robert ; Upadhyaya, Shambhu
Author_Institution :
Dept. of Electr. & Comput. Eng., State Univ. of New York, Buffalo, NY, USA
Abstract :
Experimental results using neural networks to provide go/no-go testing and fault diagnosis of analog circuits are presented. The primary focus is on reducing test time and providing a simple mechanism for automatic test pattern generation. Networks of reasonable dimension are shown to be capable of robust diagnosis of analog circuits, including effects due to tolerances and nonlinearities. The concepts are extended to include an approach to built-in test of analog or mixed signal ASICs
Keywords :
analogue circuits; application specific integrated circuits; automatic testing; built-in self test; fault location; integrated circuit testing; linear integrated circuits; mixed analogue-digital integrated circuits; neural nets; analog circuits; artificial neural networks; fault diagnosis; go/no-go testing; signature analyzers; Analog circuits; Artificial neural networks; Automatic test pattern generation; Automatic testing; Circuit faults; Circuit testing; Fault diagnosis; Frequency response; System testing; White noise;
Conference_Titel :
ASIC Conference and Exhibit, 1992., Proceedings of Fifth Annual IEEE International
Conference_Location :
Rochester, NY
Print_ISBN :
0-7803-0768-2
DOI :
10.1109/ASIC.1992.270220
