Machine learning assisted inverse design of pixelated mmWave patch antennas

Rather, Nadeem Nabi; Claussen, Holger; Ho, Lester

Machine learning assisted inverse design of pixelated mmWave patch antennas

Files

Machine_Learning_Assisted_Inverse_Design_of_Pixelated_mmWave_Patch_Antennas.pdf(853.02 KB)

Accepted Version

Date

2026

Authors

Rather, Nadeem Nabi

Claussen, Holger

Ho, Lester

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 19×23 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 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.

Keywords

Millimetre-wave antenna , Pixelated antenna , Inverse design , Surrogate model , XGBoost , CNN-BiLSTM , Dataset augmentation , 5G , mmWave , [TyndallMicroNano] , [Tyndall] , [ComputerScience]

Citation

Rather, N. N., Claussen, H. and Ho, L. (2026), 'Machine learning assisted inverse design of pixelated mmWave patch antennas', IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Singapore , 1-4 September (6pp).