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

使用人工智能/机器学习算法设计和优化超表面材料

• 批准号: 2751285
• 金额: --
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2751285
• 关键词: Design; optimisation; metasurface; materials; using

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.

现代技术的进步增加了对各种多功能组件的需求。在射频 (RF) 领域，无线通信需要高效、可调谐、廉价且尺寸较小的封装天线，以便可以放入越来越小的设备中。此外，随着我​​们进入下一代无线通信技术，此类天线的设计变得更加复杂。由于复杂性增加，需要使用人工智能和机器学习工具等自动化技术来处理这些即将推出的新技术所涉及的计算量和复杂性。由于天线的复杂性和某些应用，天线设计者必须在尺寸、功能和制造成本之间做出妥协。偏铁氧体材料已成为解决大多数此类问题的有吸引力的选择，并且已成为从天线到吸收器以及复杂空间或频域设备的构建等各种应用的非常有用的工具[1]。超材料用于天线设计的主要优点是它们有可能实现广角扫描设计和出色的波束性能、电子控制指向和极化、低功耗，并且可以是扁平的、轻量的和小尺寸的[2]，解决了新技术设备需求所面临的许多主要问题。这些超材料是工程材料，也称为左手 (LH) 材料或后向波 (BW) 介质或负折射率材料 (NIM) 或双负 (DNG) 介质，[2] 表现出天然材料中未见的有趣特性。由于超材料设计复杂性的增加，这些材料的设计者使用机器学习技术并不罕见。超材料由夹在两个金属层之间的基板组成，其中一层通常用晶胞阵列进行图案化。这是机器学习技术主要关注设计和优化的地方。该研究的意义在于，它将开发一种机器学习辅助方法，有助于快速制造测试样品。此外，还设想在材料特性的几何优化方面改进以前的超材料设计的性能。所提出的方法非常新颖，因为它寻求与最终用户应用程序整体开发一个工具平台。研究目的和目标：研究机器学习技术在超材料天线设计功能中的应用。研究史莱姆模算法 (SMA) 在超材料设计和优化中的使用，因为据研究人员所知，这是一项相对较新的技术，目前对其超材料优化的使用研究很少。为了改进由 Yihan Ma 小组的前任博士构建的先前神经网络框架，通过实施混合启发式算法将超材料设计提升到更大的规模。旨在推动知识的发展，以生产用于超材料设计的自动化整体工具，同时考虑多个客观参数。