Transformer model designs pixel antennas with multiple solutions fast
Multi-Solution Inverse Design of Pixelated Patch Antennas Using an Autoregressive Transformer
Machine Learning
Summary
Designing antennas usually involves trial and error with many possible configurations, which takes a long time. The authors use a special type of AI called an autoregressive transformer to design 10 by 10 pixel antennas that match given radio signals. Their method can create many different antenna designs that work for the same radio target, and it does this faster and more reliably than older techniques. They also tested their designs by making real antennas and measuring their performance.
What this means in practice
- •For antenna engineers: Generate multiple pixel-based antenna layouts quickly for precise radio frequency targets, speeding up the antenna design process.
- •For wireless system developers: Create custom dual-band antennas that meet sharp frequency requirements without manually crafting detailed design curves.
Authors
Young-Mok Yun, Seung-Won Shin, Jaeyong Chung, Kyung-Sik Choi
Abstract
Deep-learning-based inverse design has emerged as a promising framework for pixelated patch antenna synthesis by enabling broader exploration of discrete geometry spaces and reducing design time compared with conventional template-based methods. Existing methods, including surrogate-assisted metaheuristic optimization, tandem inverse networks, and diffusion models, have advanced the field but still face practical limitations. Metaheuristic methods such as binary particle swarm optimization (BPSO) are sensitive to initialization and search hyperparameters and are often time-consuming, tandem networks have difficulty capturing the one-to-many nature of inverse antenna design, and diffusion models may require excessive complexity and data for moderate-scale binary layout synthesis. In this paper, an autoregressive (AR) transformer is presented for the inverse design of 10 x 10 pixelated patch antennas. Its sequential discrete generation process enables the synthesis of multiple feasible structures for a given target response. The pretrained model is further fine-tuned for dual-band design using step-shaped target responses with sharp spectral transitions, allowing intuitive band specification without manually constructing detailed S11 curves. Compared with BPSO, our proposed method generates valid and diverse antenna candidates more efficiently. Fabricated and measured examples confirm that distinct pixelated layouts can realize the desired antenna responses, thereby demonstrating the multi-solution capability of the proposed framework.