Intelligent Methods for Reconfigurable Devices

Intelligent Computational Methods for Reconfigurable Devices

Research Synopsis

The rapid development of communication, sensing, and navigation systems are driving major changes in the next-generation RF and microwave devices. These devices need to be miniaturized and integrated, capable of working in multiband and multimode, and configured and controlled via software-defined signals or user-centric artificial intelligence (AI), in order for the systems to possess perception, learning, reasoning, and decision-making capabilities. Fabricated by functional materials and engineered metasurfaces, these devices are tunable and reconfigurable through external stimuli such as biasing voltages or currents, electrical/magnetic/optical excitations, temperature variations, and mechanical forces. Unfortunately, while the reconfigurability and controllability provide unprecedented system flexibility and reliability, the design and optimization methods for such RF/microwave devices face great challenges coming from the structural and material complexities, multi-scale and large-scale challenges, multiphysics and nonlinear interactions, and high optimization dimensionalities.

The primary objective of this research is to confront these challenges by developing physics and neural network enabled EM and multiphysics simulation methods. These methods are engineered to provide multi-scale, multiphysics, and nonlinear modeling capabilities, enabling efficient evaluation and optimization of RF and microwave reconfigurable devices.