Image Classifier Based Generative Method for Planar Antenna Design
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Abstract (arXiv)
To extend the antenna design on printed circuit boards (PCBs) for more engineers of interest, we propose a simple method that models PCB antennas with a few basic components. By taking two separate steps to decide their geometric dimensions and positions, antenna prototypes can be facilitated with no experience required. Random sampling statistics relate to the quality of dimensions are used in selecting among dimension candidates. A novel image-based classifier using a convolutional neural network (CNN) is introduced to further determine the positions of these fixed-dimension components. Two examples from wearable products have been chosen to examine the entire workflow. Their final designs are realistic and their performance metrics are not inferior to the ones designed by experienced engineers.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 22 references that are in the map. First posted on arXiv 16 Dec 2023.
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References · 6
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2017Mask R-CNNcited by 7,259
- 2020Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Searchcited by 87
- 2020A Generative Machine Learning-Based Approach for Inverse Design of Multilayer Metasurfacescited by 10
- 2022A Machine Learning Generative Method for Automating Antenna Design and Optimizationcited by 1
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