Haq Tanveerul, Koziel Slawomir, Pietrenko-Dabrowska Anna
Engineering Optimization & Modeling Center, Reykjavik University, 101, Reykjavik, Iceland.
Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, 80-233, Poland.
Sci Rep. 2025 May 23;15(1):17986. doi: 10.1038/s41598-025-03056-x.
In this study, we introduce a technique for unsupervised design and design automation of resonator-based microstrip sensors for dielectric material characterization. Our approach utilizes fundamental building blocks such as circular and square resonators, stubs, and slots, which can be adjusted in size and combined into intricate geometries using appropriate Boolean transformations. The sensor's topology, including its constituent components and their dimensions, is governed by artificial intelligence (AI) techniques, specifically evolutionary algorithms, in conjunction with gradient-based optimizers. This enables not only the explicit enhancement of the circuit's sensitivity but also ensures the attainment of the desired operating frequency. The design process is entirely driven by specifications and does not necessitate any interaction from the designer. We extensively validate our design framework by designing a range of high-performance sensors. Selected devices are experimentally validated, calibrated using inverse modeling techniques, and utilized for characterizing dielectric samples across a wide spectrum of permittivity and thickness. Moreover, comprehensive benchmarking demonstrates the superiority of AI-generated sensors over state-of-the-art designs reported in the literature.
在本研究中，我们介绍了一种用于基于谐振器的微带传感器的无监督设计及设计自动化技术，该传感器用于介电材料表征。我们的方法利用圆形和方形谐振器、短截线和狭缝等基本构建块，这些构建块的尺寸可以调整，并可使用适当的布尔变换组合成复杂的几何形状。传感器的拓扑结构，包括其组成部件及其尺寸，由人工智能（AI）技术，特别是进化算法与基于梯度的优化器共同控制。这不仅能显著提高电路的灵敏度，还能确保达到所需的工作频率。设计过程完全由规格驱动，无需设计者进行任何交互。我们通过设计一系列高性能传感器，广泛验证了我们的设计框架。所选器件经过实验验证，使用逆建模技术进行校准，并用于表征各种介电常数和厚度的介电样品。此外，全面的基准测试表明，人工智能生成的传感器优于文献中报道的现有技术设计。
