DEEP LEARNING-ASSISTED ELECTROMAGNETIC SIMULATIONS FOR ENHANCED MICROSTRIP CIRCUIT DESIGN USING RECURRENT NEURAL NETWORKS

Microstrip circuits form the backbone of modern high-frequency communication systems, offering compact and efficient solutions for signal processing and transmission. However, the design of these circuits is challenging due to the intricate interplay of electromagnetic (EM) parameters, material properties, and circuit dimensions. Traditional EM simulation methods, while accurate, are computationally intensive and time-consuming, limiting their applicability for rapid prototyping and optimization. To address these challenges, this study integrates deep learning techniques with electromagnetic simulations to enhance microstrip circuit design efficiency. A Recurrent Neural Network (RNN)-based framework is proposed to predict the frequency-dependent behavior of microstrip circuits, leveraging temporal data from iterative EM simulations. The RNN model is trained on a diverse dataset of simulated circuit configurations, capturing the relationships between physical parameters, design constraints, and performance metrics. The proposed approach significantly reduces computational overhead by approximating the results of full-wave EM simulations while maintaining high accuracy. Validation against benchmark EM simulation tools shows that the RNN model achieves over 95% prediction accuracy with a 70% reduction in simulation time. Additionally, this framework enables real-time optimization of circuit designs, accelerating the iterative design process without compromising performance.