Machine Learning Assisted Inverse Design of Pixelated mmWave Patch Antennas

Abstract

In this paper, 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 $19 \times 23$ 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 structuredrandom pixel patterns, of which only around 40% achieved a resonance with $\left|S_{11}\right| \leq-10 \mathrm{~dB}$ 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 $S_{11}$ 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 $S_{11}$ specification. The results show good agreement between the surrogate-predicted and CST-simulated $\left|S_{11}\right|$ responses for the generated designs and demonstrate the feasibility of automatically designing and reconfiguring antenna structures.