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.