An intelligent digital twinning approach for complex circuits

Abstract

The digital twinning process is essential for transferring real-world objects to the Metaverse by creating accurate digital versions, known as digital twins. However, complex systems pose challenges in this process. With the increasing utilization of microwave components and circuits in telecommunication systems such as IoT, 5 G, and 6 G, the need for digital twins of these components arises. Nevertheless, high-frequency components exhibit intricate behavior, requiring advanced modeling techniques. Artificial intelligence (AI) provides a powerful tool for enhancing the reliability and accuracy of estimated models in such cases. In this study, a microstrip lowpass filter (LPF) is designed, fabricated, and measured as the physical twin. An intelligent digital twinning approach is employed using a machine learning method based on an adaptive neuro-fuzzy inference system (ANFIS), trained by a subtractive clustering algorithm. The resulting digital twin of the LPF proves valuable for communication networks and IoT applications. Moreover, this research showcases the applicability and accessibility of machine learning in creating digital twins of electromagnetic components for communication cyber-physical systems (CPSs) and the Metaverse. Furthermore, the proposed method exhibits adaptability to various passive and active electrical or electronic circuits. By harnessing the potential of AI and digital twinning, this study presents a robust and accurate approach for modeling and analyzing complex circuits, specifically in the context of communication systems and their integration into the Metaverse. The findings highlight the advantages of an intelligent digital twinning approach and its potential for advancing various domains involving complex circuitry.