发表机构
University of Moratuwa（莫拉图瓦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出双Transformer架构cktFormer，通过图表示建模电路关系，以节点和边预测模型协同生成有效电路，在有效性上超越AnalogGenie和cktGNN。
AI 中文摘要
电路设计是一个复杂且迭代的过程，需要电子工程领域的专业知识。它涉及在满足性能约束（如功率效率、成本效益和信号完整性）的同时选择组件。然而，人工设计耗时且容易出错。尽管制造流程的其他阶段已受益于AI驱动的优化，电路设计仍然是瓶颈，限制了整体生产力。生成式AI和机器学习有望自动化和改进这一阶段，提高效率和准确性。为解决这一问题，我们提出了一种双Transformer架构，通过利用注意力机制建模复杂的非序列电路关系，弥合AI与电路设计之间的差距。我们的方法将网表数据构建为基于图的表示，从而有效学习电路拓扑和组件交互。该系统由两个相互关联的模型组成：一个节点预测模型用于提出组件，一个边预测模型用于推断有效连接。这种协作且解耦的设计同时捕获了组件级语义和全局结构一致性。在我们的实验中，该架构在生成电路的有效性方面优于近期模型（如AnalogGenie和cktGNN）。通过解决现有方法的关键局限性，我们的工作推进了电子工程的自动化，并为AI驱动的电路综合贡献了一个基准。
英文摘要
Circuit design is a complex and iterative process that requires expertise in electronic engineering. It involves selecting components while meeting performance constraints, such as power efficiency, cost-effectiveness, and signal integrity. However, manual design is time-consuming and prone to errors. Although other stages of the manufacturing pipeline have benefited from AI-driven optimizations, circuit design remains a bottleneck, limiting overall productivity. Generative AI and machine learning offer the potential to automate and improve this stage, boosting efficiency and accuracy. To address this, we introduce a dual transformer architecture that bridges the gap between AI and circuit design by leveraging attention mechanisms to model complex, non-sequential circuit relationships. Our approach structures netlist data into graph-based representations, enabling effective learning of circuit topology and component interactions. The system consists of two interlinked models: a node prediction model that proposes components and an edge prediction model that infers valid connections. This collaborative and decoupled design captures both component-level semantics and global structural coherence. In our experiments, this architecture outperforms recent models such as AnalogGenie and cktGNN in the validity of generated circuits. By addressing key limitations in existing methods, our work advances automation in electronics engineering and contributes a benchmark for AI-driven circuit synthesis.
Comments6 pages, 6 figures, IECON 2025 51st Annual Conference of the IEEE Industrial Electronics Society
Journal refIECON 2025 - 51st Annual Conference of the IEEE Industrial Electronics Society, 2025, pp. 1-6
DOI：10.1109/IECON58223.2025.11221564
