TARGet：基于图神经网络的拓扑感知融合射频电路功能建模
TARGet: Topology-Aware Fusion-based Radio Frequency Circuit Functional Modeling using Graph Neural Networks
- University of Utah（犹他大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
TARGet提出一种拓扑感知的融合架构，结合图神经网络与子电路连接感知网络，实现跨拓扑射频电路功能建模，以低数据需求达到高精度，并支持零样本迁移。
AI中文摘要：
模拟与射频（RF）电路的自动综合是一个新兴领域，需要高效的电路建模方法。近年来，机器学习（ML）解决方案在这方面发挥了有前景的作用。然而，许多现有的ML方法要求为每种电路拓扑单独训练数据，即使仅添加或移除单个电路组件也是如此。此外，它们忽略了电路拓扑信息，这限制了其捕捉复杂组件交互的能力。再者，它们依赖具有扁平特征表示的全连接神经网络，这需要大量的训练数据。在这项工作中，我们提出了一种开源的拓扑感知射频电路建模方法TARGet。我们的模型在两个层面上考虑电路：子电路和整体电路拓扑。在子电路层面，TARGet利用S参数表示来捕捉子电路行为，而非依赖单个电路组件，为射频构建模块提供了可复用的行为抽象。此外，TARGet明确地将电路拓扑信息纳入模型，使其能够跨多种拓扑进行学习。TARGet引入了一种新颖的基于融合的架构，该架构整合了图神经网络（GNN）和子电路连接感知神经网络，以提高数据效率。在多个射频电路拓扑上的实验评估表明，与最先进的（SOTA）方法相比，TARGet实现了低于1%的预测误差，同时将所需训练数据减少了高达35.5倍。此外，在严格的1%误差阈值下，TARGet相对于SOTA模型实现了9.7倍的预测精度提升。一项留出匹配网络评估进一步展示了向未见子电路拓扑的零样本迁移，其中TARGet将NMAE降低了高达45%。
英文摘要：
Automatic synthesis of analog and Radio Frequency (RF) circuits is an emerging area that requires an efficient circuit modeling method. In recent years, Machine Learning (ML) solutions have played a promising role in this regard. However, many existing ML approaches require separate training data for each circuit topology, even when a single circuit component is added or removed. In addition, they overlook circuit topology information, which limits their ability to capture complex component interactions. Furthermore, they rely on fully connected neural networks with flat feature representations, which require substantial amounts of training data. In this work, we propose an open-source topology-aware RF circuit modeling method, TARGet. Our model considers the circuit at two levels: sub-circuits and the overall circuit topology. At the sub-circuit level, TARGet leverages S-parameter representations to capture sub-circuit behavior rather than relying on individual circuit components, providing a reusable behavioral abstraction for RF building blocks. Moreover, TARGet explicitly incorporates circuit topology information into the model, enabling it to learn across multiple topologies. TARGet introduces a novel fusion-based architecture that integrates Graph Neural Networks (GNNs) and sub-circuit connectivity-aware neural networks to improve data efficiency. Experimental evaluation across multiple RF circuit topologies demonstrates that TARGet achieves sub-1% prediction error while reducing the required training data by up to 35.5x compared to state-of-the-art (SOTA) approaches. Furthermore, TARGet achieves 9.7x higher prediction accuracy under a strict 1% error threshold relative to SOTA models. A held-out matching-network evaluation further demonstrates zero-shot transfer to an unseen sub-circuit topology, where TARGet reduces NMAE by up to 45%.
