Design and optimisation of metasurface materials using AI/machine learning algorithms

Modern advances in technology have increased the demand for multifunctional components across the spectrum. In the Radio frequency (RF) world, wireless communications require efficient, tunable, inexpensive and smaller package antenna that can be put into smaller and smaller devices. Additionally, as we move into the next generation of wireless communication technology the designs of such antenna are becoming more complex. Due to this increased complexity automated techniques such as, AI and machine learning tools need to be used in order to deal with the amount of computation and complexity involved in these new upcoming technologies. Due to the complexity and certain applications designers of antennas have to compromise either size, functionality and fabrication cost. Metaferrite materials have become an attractive option for addressing most of these issues and have become a very useful tool for various applications from antennas to absorbers and the construction of complex spatial or frequency domain devices [1]. The main benefits of a metamaterial for antenna design is that they potentially enable the design of wide angle scanning and excellent beam performance, electronically controlled pointing and polarisation, low power consumption and can be flat, lightweight and small in size[2], answering many of the main issues that are facing the demand for new technological devices.
These metamaterials are engineered materials also called left-handed (LH) materials or backward wave (BW) media or negative index materials (NIM) or double negative (DNG) media, [2] that exhibit interesting properties not otherwise seen in naturally occurring materials.
Due to the increase in complexity of metamaterial designs it is not uncommon for machine learning techniques to be used by designers of these materials. Metamaterials consists of a substrate sandwiched between two metal layers where one is usually patterned with a unit cell array. This is where the machine learning techniques are mainly focused on designing and optimising.
The significance of the research is that it will develop a machine learning assisted method that will assist with the rapid fabrication of samples for testing. Additionally, improving upon the performance of previous metamaterial designs in the geometric optimisation to material properties is envisaged. The proposed approach is highly novel as it seeks to holistically develop a platform of tools with end user applications.
Research Aims and Objectives:
To investigate the use of machine learning techniques in their functionality for metamaterial antenna design.
To investigate the use of Slime mould algorithm (SMA) for metamaterial design and optimisation as this is a relatively new technique and has very little research currently into its use of metamaterial optimisation, to the researchers knowledge.
To improve upon previous Neural Network frameworks built by a previous PhD in the group Yihan Ma, by implementing a mix of metaheuristic algorithms to upscale metamaterial designs to a much larger scale.
Aim to push knowledge forward for the production of an automated holistic tool for metamaterial design, taking into account multiple objective parameters.