MOTIF-RF: Multi-template On-chip Transformer Synthesis Incorporating Frequency-domain Self-transfer Learning for RFIC Design Automation

2025/11/26 by Huan He, He, Houbo, Yizhou Xu +9
Engineering · #Advanced Power Amplifier Design #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Microwave and Dielectric Measurement Techniques #Radio Frequency Integrated Circuit Design #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2511.21970

openalex publication_date 2025/11/26 · openalex created_date 2025/12/03 · openalex updated_date 2026/07/28

Abstract

This paper presents a systematic study on developing multi-template machine learning (ML) surrogate models and applying them to the inverse design of transformers (XFMRs) in radio-frequency integrated circuits (RFICs). Our study starts with benchmarking four widely used ML architectures, including MLP-, CNN-, UNet-, and GT-based models, using the same datasets across different XFMR topologies. To improve modeling accuracy beyond these baselines, we then propose a new frequency-domain self-transfer learning technique that exploits correlations between adjacent frequency bands, leading to around 30%-50% accuracy improvement in the S-parameters prediction. Building on these models, we further develop an inverse design framework based on the covariance matrix adaptation evolutionary strategy (CMA-ES) algorithm. This framework is validated using multiple impedance-matching tasks, all demonstrating fast convergence and trustworthy performance. These results advance the goal of AI-assisted specs-to-GDS automation for RFICs and provide RFIC designers with actionable tools for integrating AI into their workflows.

Citations

• Recipe for a General, Powerful, Scalable Graph Transformer
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• Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers
• Attention Is All You Need
• The Mythos of Model Interpretability
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