Neural Network–based Defect Detection in Analog and Mixed Ic Using Digital Signal Preprocessing

The major goal of our work was to develop an efficient defect-oriented parametric test method for analog & mixed-signal integrated circuits based on Artificial Neural Network (ANN) classification of a selected circuit’s parameter using different methods of signal preprocessing. Thus, ANN has been used for detecting catastrophic defects in an experimental mixedsignal CMOS circuits by sensing the abnormalities in the analyzed circuit’s response and by their consequent classification into a proper category, representing either good or defective circuit. To reduce the complexity of neural network, Wavelet Decomposition (WD) is used to perform preprocessing of the analyzed parameter. This brings significant enhancement in the correct classification, and makes the neural network-based test method very efficient and versatile for detecting hard-detectable catastrophic defects. Moreover, investigation of the possibility to utilize this approach also in detection of parametric faults in analog circuits was the subject of our research as well. Therefore, a new methodology for neural network based detection of parametric defects using Principal Component Analysis (PCA) of the analyzed circuit’s response has been proposed. Since the training set selection plays a crucial role in achieving desirable classification results, we also propose a new approach to this selection employing Convex hull (qhull) graphics algorithm. As it is shown in the experiments performed, well trained neural network is not only able to detect the faulty devices but also identify the particular parameter deviation in the respective circuit element.