用频域自迁移学习提升射频芯片变压器逆向设计精度
MOTIF-RF: Multi-template On-chip Transformer Synthesis Incorporating Frequency-domain Self-transfer Learning for RFIC Design Automation
- 构建多模板机器学习模型，利用频段间相关性增强预测
- 频域自迁移使S参数预测准确率提升30%-50%
- 结合进化算法实现快速可靠逆向设计，适合芯片工程师使用
本文系统研究了多模板机器学习（ML）代理模型在射频集成电路（RFIC）中变压器（XFMR）逆向设计的应用。通过在不同变压器拓扑结构上使用相同数据集，对MLP、CNN、UNet、GT四种主流架构进行基准测试。为超越现有基线，提出一种频域自迁移学习技术，利用相邻频段间的相关性，使S参数预测准确率提升约30%-50%。在此基础上，开发基于协方差矩阵自适应进化策略（CMA-ES）的逆向设计框架，在多个阻抗匹配任务中均实现快速收敛与可信性能。该成果推进了RFIC中“规格到GDS”的自动化，为设计者提供可落地的AI集成工具。
原文摘要 · Abstract (English)
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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