Machine Learning Assisted Inverse Design of Pixelated mmWave Patch Antennas
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 t...
| Authors | Nadeem Rather, Holger Claussen, Lester Ho |
|---|---|
| Primary category | eess.SP |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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