Use of Neural Networks in Testing Analog to Digital Converters

In the past two decades, the techniques of artificial neural networks are growing mature, as a datadriven method, which provides a totally new perspective to fault diagnosis. 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 sine fit test techniques. 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 sine fit test techniques. In this paper, we introduce two ideas to improve the training phase time. They are separation and indexing of neural network outputs. Finally, we concluded that neural network approach is a robust way for fault diagnosis of ADCs and also other mixed signal circuits. Key-Words: fault, ADC, neural networks, test, histogram, FFT, sine fit, training time, output separation.