Fault Detection in Analog/Digtal VLSI Circuits based on Artificial Intelligence

Abstract

Due to the explosive demand and rapid development of the electronics industry, the integration and complexity of electronic devices have significantly grown. Fault detection has played a vital role in identification of faults in electronic circuits, ensuring normal operation and reliability of systems. Given traditional methods of fault detection are often inaccurate and time-consuming, artificial intelligence techniques are a growing interest for researchers of this field. In this paper, various artificial intelligence-based fault detection techniques in analog and digital VLSI circuits are described. In addition, two distinct models for fault detection of digital circuits based on deep learning are proposed. The primary goal of the first approach is utilization of a stacked sparse autoencoder to avoid the search space explosion problem. The second proposed method utilizes an optimization method for detecting the best model configuration. The proposed models deliver maximum validation accuracy of 97.7% and 95.7% respectively, implemented on digital circuit from ISCAS’85.