Stretchable wideband dipole antenna using machine learning assisted optimization for soft electronics

Abstract

In this paper, we present a machine learning-assisted optimization approach, combining artificial neural networks (ANN) with particle swarm optimization (PSO), for the design of a stretchable wideband dipole antenna tailored for soft electronics. The stretchable antenna is critical for next-generation wearable and flexible devices, where maintaining stable electromagnetic (EM) performance during deformation is challenging. To address the complex nonlinear coupling between EM performance and mechanical behavior, we propose a tri-branch ANN as a surrogate model to decouple the interdependencies between EM and mechanical properties, thereby improving prediction accuracy and reliability. Furthermore, the PSO algorithm is integrated to optimize the geometric parameters of the antenna, ensuring robust mechanical performance and stable EM characteristics. This optimization is achieved through an integrated co-simulation using an EM simulator, a mechanical simulator, and Python. The proposed method not only demonstrates significant improvements in efficiency, automation, and time savings over traditional design techniques but also offers a scalable solution for developing high-performance stretchable electronics.</p