Abstract
With the rapid development of microelectronics and semiconductor technology, integrated analog electronic systems become more sophisticated and complex functions. It has become increasingly high reliability requirements, but the corresponding testability positive change it was getting worse. How to use signal processing and artificial intelligence techniques and diagnose faults in the system analog electronic components or subsystems, is currently a hot simulation diagnostics. Fault feature extraction and selection is the key technology in the field of analog electronic system testing, for subsequent fault classification is very important. Current research focuses on the fault feature extraction, feature selection. To solve this problem, a new feature based on fault scalar wavelet coefficients selection method. In this paper, some analog electronic system fault characteristics and difficult to obtain a small number of samples and other issues, study the characteristics of a fault simulation method based on a sample cloud model generation method, and the use of neural network expansion sample sets the newly created training. The results show that the new sample training practiced neural network has better noise robustness.
