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

• URL: http://arxiv.org/abs/2511.21970v1
• Date: Wed, 26 Nov 2025 23:03:34 GMT
• Title: MOTIF-RF: Multi-template On-chip Transformer Synthesis Incorporating Frequency-domain Self-transfer Learning for RFIC Design Automation
• Authors: Houbo He, Yizhou Xu, Lei Xia, Yaolong Hu, Fan Cai, Taiyun Chi,
• Abstract summary: We develop multi-template machine learning (ML) surrogate models and apply them to the inverse design of transformers (XFMRs) in radio-frequency integrated circuits (RFICs)<n>New frequency-domain self-transfer learning technique exploits correlations between adjacent frequency bands, leading to around 30%-50% accuracy improvement in the S-s prediction.<n>Inverse design framework is validated using multiple impedance-matching tasks, all demonstrating fast and trustworthy performance.
• Score: 6.037841678034353
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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