机器学习辅助的像素化毫米波贴片天线逆设计

中文摘要 AI 辅助

本文提出一种机器学习辅助的框架，用于针对22-30 GHz频段的像素化毫米波贴片天线的逆设计。天线表面在Rogers RT/duroid 5880基底上表示为19×23的二值像素网格，每个像素为金属或空白，设计时确保从馈源到天线的连续电气路径。从结构化随机像素图案中收集了约6000组全波CST仿真的初始数据集，其中仅约40%的图案在该频段内任意位置实现了|S11|≤-10 dB的谐振，形成不平衡数据集。为提高仿真效率，在该数据上训练了XGBoost二分类器，用于在仿真前区分谐振与非谐振图案。使用该分类器作为预仿真过滤器，额外选择并仿真了4000组图案，使10000组样本的组合数据集中谐振设计的总体比例从约40%提升至52%。随后在该增强数据集上训练了混合CNN-BiLSTM正向代理模型，以预测801个频率点上的完整复S11响应，训练时采用物理引导的复合损失函数，明确强调谐振 dip 的精度。最后，开发了一种逆设计模型，在紧凑的64维潜在空间中使用梯度下降进行优化，以生成符合期望S11规格的像素图案。结果表明，代理模型预测的生成设计的|S11|响应与CST仿真结果吻合良好，证明了自动设计和重构天线结构的可行性。

英文摘要

A machine learning-assisted framework for the inverse design of pixelated millimetre-wave patch antennas targeting the 22--30 GHz band is presented. The antenna surface is represented as a 19x23 binary pixel grid on a Rogers RT/duroid 5880 substrate, where each pixel is either metal or empty, with a continuous electrical path from the feed enforced by design. An initial dataset of approximately 6,000 full-wave CST simulations was collected from structured random pixel patterns, of which only around 40% achieved a resonance with |S11| <= -10 dB anywhere in the band, resulting in an imbalanced dataset. To improve simulation efficiency, an XGBoost binary classifier was trained on this data to distinguish resonant from non-resonant patterns before simulation. Using the classifier as a pre-simulation filter, an additional 4,000 patterns were selected and simulated, raising the overall proportion of resonant designs in the combined 10,000-sample dataset from approximately 40% to 52%. A hybrid CNN-BiLSTM forward surrogate was then trained on this augmented dataset to predict the full complex S11 response across 801 frequency points, using a physics-guided composite loss that explicitly emphasises resonance dip accuracy. Finally, an inverse design model was developed that optimises in a compact 64-dimensional latent space using gradient descent to generate pixel patterns matching a desired S11 specification. The results show good agreement between the surrogate-predicted and CST-simulated |S11| responses for the generated designs and demonstrate the feasibility of automatically designing and reconfiguring antenna structures.

发表机构

• Tyndall National Institute（廷德尔国家研究所）
• University College Cork（科克大学学院）
• Trinity College Dublin（都柏林大学圣三一学院）

机构由 AI 辅助整理，请以论文原文为准。