On improving training time of neural networks in mixed signal circuit fault diagnosis applications

Testing issues are becoming more and more important with the quick development of both digital and analog circuit industry. Analog-to-digital converters (ADCs) are becoming more and more widespread owing to their fundamental capacity of interfacing analog physical world to digital processing systems. In this paper, we study the use of neural networks in fault diagnosis of ADCs and compare the results with other ADC testing approaches such as histogram, FFT and sinewave curve fit test techniques. In this paper, we introduced the idea of separation of neural network’s output matrix to improve the training phase time, called ‘index-separation’ approach. Finally, we concluded that training time in this method is about 0.25 times as much as that in the normal training method. We also concluded that this approach does not affect network’s decision strength. Besides, we concluded that if the complexity of the circuit increases, this method will still be effective. Therefore, this method is a robust way for fault diagnosis of mixed signal circuits.