Generative surrogate modelling for the efficient design optimization of an RF component

Abstract

Accurate and efficient design of millimeter-wave components is critical for sixth-generation (6 G) wireless systems, particularly at the D-band where low-loss interconnects are required. Vertically aligned carbon-nanotube (VACNT)–based air-filled waveguides (AFWGs) have recently been introduced as a promising alternative to conventional metallic structures, offering reduced propagation losses and wafer-level compatibility. However, their optimization remains dependent on full-wave solvers such as HFSS, where large parametric sweeps are computationally prohibitive. This work introduces a rank-reduction autoencoder (RRAE) as a machine-learning-based surrogate modeling technique incorporating a truncated principal component analysis (PCA) stage enabling robust performances, reliable interpolation and constraint-preserving sampling based on small datasets. Trained on 421 HFSS simulations only, the RRAE managed the retrieval of 5 unique optimized VACNT-AFWG designs with operational bandwidths between 135 and 155 GHz. It has been benchmarked against a direct encoder–decoder regression baseline sharing the same backbone but without the rank-reduction stage that did not produce any HFSS-confirmed valid design in the proposed screening workflow. The RRAE hence presents itself as a scalable solution for surrogate-assisted optimization and shows to be a promising candidate for the design of next-generation low-loss waveguide technologies.