Application of Feed-forward Artificial Neural Networks to the Identification of Defective Analog Integrated Circuits

Abstract

This paper presents a new approach for detecting defects in analog integrated circuits using a feedforward neural network trained by the resilient error back-propagation method. A feed-forward neural network has been used for detecting faults in a simple analog CMOS circuit by representing the differences observed in power supply current of fault-free and faulty circuits. The identification of defects was performed in time and frequency domains, followed by a comparison of results achieved in both domains. We show that resilient backpropagation neural networks can be a very efficient and versatile approach for identifying defective analog circuits. Moreover, this approach is not limited to the supply current analysis, because it also offers monitoring of other circuit parameters. The type of defects detected by the resilient backpropagation neural networks, as well as other possible applications of this approach, are discussed.

Time-Frequency Analyses of Tide-Gauge Sensor Data

“…The output layer is the final processing layer that provides the output value. The hidden layers between the input and output layers, of which there may be only one, perform the basic calculations [ 36 , 49 ]. Each connection between the nodes has an associated weight, which is usually chosen randomly at the beginning of the training process.…”