AI Predicts Terahertz Antenna Performance in Microseconds, Accelerating the Road to 6G

Terahertz frequencies promise to carry sixth-generation wireless networks into an era of ultrahigh data rates and near-instant connectivity, but designing antennas that work at these extreme frequencies has always been a slow, computationally punishing affair. A new study published in Results in Optics describes a deep-learning framework that can predict the electromagnetic behavior of a terahertz MIMO antenna in a fraction of a millisecond, replacing full-wave simulations that each take 45 seconds to complete. The work, led by Abdelbasset Azzouz and colleagues, demonstrates how artificial intelligence can compress weeks of simulation time into hours of training and then deliver near-instant predictions on demand.

The research team built their antenna system step by step, starting from a single radiating element and progressively extending it to two ports, then four, and finally a full eight-port MIMO configuration. The single-element antenna measures just 0.4 by 0.4 millimeters and is built on a polyimide substrate with a dielectric constant of 4.3 and a loss tangent of 0.004, chosen for its balance between compactness and acceptable dielectric loss at terahertz frequencies. Copper is used for both the radiating patch and the ground plane because of its excellent conductivity and compatibility with standard fabrication processes.

The design evolved through four successive iterations. The team began with a conventional rectangular microstrip patch, then added two crossed rectangular extensions that lengthened the effective current path and improved impedance matching. A circular metallic element was incorporated along the lower edge of the patch in the third iteration, redistributing current flow and exciting additional resonant modes. Finally, a large circular slot was etched into the ground plane, creating a defected ground structure that suppresses surface-wave transmission and widens the operational bandwidth. The finished single-port antenna resonates at 0.418, 0.555, and 0.718 terahertz, with reflection coefficients of −28, −37.47, and −31.58 decibels respectively.

Parametric studies revealed how sensitive terahertz antenna performance is to small geometric changes. Varying the microstrip feed width from 0.035 to 0.050 millimeters showed that the narrowest feedline produced the best impedance matching across all three bands, while wider feeds degraded the return loss significantly. Substrate thickness was similarly critical, with an optimal range between 0.035 and 0.040 millimeters balancing bandwidth against matching quality. The team also compared polyimide against gallium arsenide and silicon substrates, finding that polyimide delivered wider bandwidths and superior impedance matching across all three resonant frequencies.

Once the single element was optimized, the researchers arranged multiple copies into MIMO configurations. A two-port version was tested in both parallel and orthogonal arrangements, with the parallel layout achieving isolation as deep as −31.5 decibels between ports. The four-port design, occupying a 0.8 by 0.8 millimeter substrate, maintained reflection coefficients between −25.40 and −37.9 decibels across its three bands, with isolation reaching −48.93 decibels in the third band. Each step preserved the multiband character of the original element while adding channel capacity.

The final eight-port antenna sits on a compact 1.6 by 0.8 millimeter substrate and resonates at 0.400, 0.555, and 0.718 terahertz. Reflection coefficients across the eight ports range from −22.5 to −42.13 decibels depending on the band, confirming strong impedance matching at every port. Inter-port isolation varies from −18 to −70 decibels, with the worst-case coupling still deep enough to ensure independent operation of each element. The antenna achieves a maximum gain of 10.04 decibels at 0.555 terahertz and maintains radiation efficiency above 95 percent across the operating range.

Diversity metrics are equally impressive. The envelope correlation coefficient, which measures how independently the antenna elements radiate, remains below 0.0017 across the operating band, far under the typical acceptance threshold. This translates to a diversity gain of essentially 10 decibels, the theoretical maximum, indicating near-perfect spatial diversity. The mean effective gain ratios between ports approach 0 decibels, confirming balanced power reception, and the total active reflection coefficient stays low under simultaneous multiport excitation, validating the design under realistic operating conditions.

The deep-learning component of the study is where the work makes its most distinctive contribution. The researchers generated 130,000 training samples from full-wave HFSS simulations, with 32,500 samples for each of the four antenna configurations. Each sample paired geometric parameters and operating frequency with the real and imaginary components of the reflection coefficient. The dataset was partitioned by complete antenna geometries rather than individual frequency points, ensuring that 65 unseen geometries per configuration were held out entirely from training to prevent data leakage. A deep feed-forward neural network then learned the nonlinear mapping from design space to the complex-valued scattering parameter.

The team compared three optimizers during training: stochastic gradient descent, RMSProp, and Adam. Adam delivered the best performance for the two-port and eight-port models, achieving a coefficient of determination of 0.983296 for the two-port case. For the single-port configuration, RMSProp reached an R-squared of 0.963030. Prediction accuracy declined for the four- and eight-port models, with R-squared values around 0.46 to 0.47, reflecting the increased complexity of modeling more ports. Against conventional regression baselines including random forest, gradient boosting, and support vector regression, the neural network proved competitive, though ensemble methods occasionally edged it out on individual configurations.

The computational savings are dramatic. Generating the full HFSS dataset required approximately 405 hours of simulation time, but once trained, the neural network produces a prediction in 0.051 to 0.106 milliseconds, a speed advantage of roughly 424,000 to 874,000 times over individual full-wave evaluations. A break-even analysis showed that after the initial dataset is generated, the surrogate model pays for its training cost after only 16 to 26 repeated evaluations. The researchers note that the present study is based entirely on numerical simulations, and experimental fabrication and on-wafer measurement remain the immediate next steps. They also caution that the framework has been demonstrated only for this specific antenna topology in the 0.3 to 0.8 terahertz range, and extending it to other geometries, reconfigurable designs using graphene, and full multi-port scattering matrix prediction constitutes future work. Still, the approach offers a practical path toward taming the design complexity of the dense antenna arrays that 6G and next-generation Internet of Things applications will demand.

Subject of Research: Deep learning prediction of S-parameters for a progressively extended eight-port terahertz MIMO antenna designed for 6G and IoT applications

Article Title: Deep learning–based S-parameter prediction of a progressively extended eight-port terahertz MIMO antenna for 6G and next-generation IoT

Article References: Azzouz, A., Alqurashi, A., Zafar, E. Y., Zakaria, Z., & Al-Gburi, A. (2026). Deep learning–based S-parameter prediction of a progressively extended eight-port terahertz MIMO antenna for 6G and next-generation IoT. Results in Optics, Article 101171. https://doi.org/10.1016/j.rio.2026.101171

Image Credits: AI Generated

DOI: 10.1016/j.rio.2026.101171

Keywords: terahertz, MIMO antenna, deep learning, 6G, S-parameters, neural network, polyimide substrate, envelope correlation coefficient, diversity gain, IoT, HFSS simulation, surrogate modeling