The electronic design automation of analog circuits has been a longstanding challenge in the integrated circuit field due to the huge design space and complex design trade-offs among circuit specifications. In the past decades, intensive research efforts have mostly been paid to automate the transistor sizing with a given circuit topology. By recognizing the graph nature of circuits, this paper presents a Circuit Graph Neural Network (CktGNN) that simultaneously automates the circuit topology generation and device sizing based on the encoder-dependent optimization subroutines. Particularly, CktGNN encodes circuit graphs using a two-level GNN framework (of nested GNN) where circuits are represented as combinations of subgraphs in a known subgraph basis. In this way, it significantly improves design efficiency by reducing the number of subgraphs to perform message passing. Nonetheless, another critical roadblock to advancing learning-assisted circuit design automation is a lack of public benchmarks to perform canonical assessment and reproducible research. To tackle the challenge, we introduce Open Circuit Benchmark (OCB), an open-sourced dataset that contains $10$K distinct operational amplifiers with carefully-extracted circuit specifications. OCB is also equipped with communicative circuit generation and evaluation capabilities such that it can help to generalize CktGNN to design various analog circuits by producing corresponding datasets. Experiments on OCB show the extraordinary advantages of CktGNN through representation-based optimization frameworks over other recent powerful GNN baselines and human experts' manual designs. Our work paves the way toward a learning-based open-sourced design automation for analog circuits. Our source code is available at \url{https://github.com/zehao-dong/CktGNN}.

翻译：摘要：模拟电路的电子设计自动化因巨大的设计空间及电路规格间复杂的权衡，成为集成电路领域长期存在的挑战。过去数十年间，大量研究主要致力于在给定电路拓扑结构下自动进行晶体管尺寸优化。通过识别电路的图结构本质，本文提出了一种电路图神经网络（CktGNN），其能基于编码器依赖的优化子程序同时实现电路拓扑生成与器件尺寸的自动化。特别地，CktGNN采用双层GNN框架（嵌套GNN）对电路图进行编码，将电路表示为已知子图基中若干子图的组合。这种方式通过减少进行消息传递的子图数量，显著提升了设计效率。然而，推动学习辅助电路设计自动化的另一关键障碍在于缺乏可进行标准评估与可重复研究的公开基准。为应对这一挑战，我们引入了开放电路基准（OCB），这是一个包含10,000个不同运算放大器及其精心提取的电路规格的开源数据集。OCB还配备了可交互的电路生成与评估能力，能通过生成相应数据集，帮助将CktGNN泛化至各类模拟电路设计中。在OCB上的实验表明，基于表征的优化框架使CktGNN相较于其他近期强大的GNN基线方法及人类专家手工设计具有显著优势。我们的工作为基于学习的开源模拟电路设计自动化铺平了道路。源代码见：\url{https://github.com/zehao-dong/CktGNN}。