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DEEP LEARNING-BASED FAULT DETECTION IN NANO ELECTRONICS CIRCUITS FOR ROBUSTNESS ENHANCEMENT

Abstract In the rapidly advancing field of nano electronics, ensuring the robustness of circuits is paramount for reliable performance. This research addresses the critical need for effective fault detection in nano electronics circuits using deep learning techniques. The introduction outlines the increasing complexity of nano electronic circuits and the corresponding rise in susceptibility to faults, emphasizing the necessity for advanced fault detection mechanisms. The problem at hand involves the inherent challenges in identifying faults in highly compact and intricate nano electronic circuits, where traditional fault detection methods often fall short. The research gap is highlighted, emphasizing the lack of robust fault detection solutions tailored to the specific challenges of nano electronics. To bridge this gap, our method leverages the power of deep learning, employing neural networks to learn intricate patterns indicative of faults in nano electronic circuits. The approach involves the development of a comprehensive dataset that captures diverse fault scenarios, ensuring the model’s adaptability to real-world conditions. The neural network is trained using this dataset, enabling it to discern subtle variations that signal potential faults. The results showcase the efficacy of the proposed deep learning-based fault detection system, demonstrating a significant improvement in accuracy compared to traditional methods. The system not only identifies known faults with high precision but also exhibits a remarkable ability to detect novel faults, showcasing its adaptability to evolving nano electronic circuit architectures. Authors Arvind Kumar Shukla1, Sachin Vasant Chaudhari2, Narayan Krishan Vyas3, Neerav Nishant4, Mohammed Saleh Al Ansari5 IFTM University, India1, Sanjivani College of Engineering, India2, Government Engineering College, Jhalawar, India3, Babu Banarasi Das University, India4, University of Bahrain, Bahrain5 Keywords Nano Electronics, Fault Detection, Deep Learning, Neural Networks, Robustness Yearly Full Views January February March April May June July August September October November December 59 107 1 1 2 0 0 0 0 0 0 0

Published By : ICTACT Published In : ICTACT Journal on Microelectronics ( Volume: 9 , Issue: 3 , Pages: 1595 - 1600 ) Date of Publication : October 2023 DOI : 10.21917/ijme.2023.0276 Page Views : 1078 Full Text Views : 342