标题： DiffCkt：一种基于扩散模型的混合神经网络框架，用于模拟电路的自动晶体管级生成 显示英文标题

标题： DiffCkt: A Diffusion Model-Based Hybrid Neural Network Framework for Automatic Transistor-Level Generation of Analog Circuits

摘要： 模拟电路设计包括预布局和布局阶段。 其中，预布局阶段直接决定最终电路性能，但严重依赖经验工程师根据特定应用场景进行手动设计。 为克服这些挑战并自动化模拟电路预布局设计阶段，我们引入了DiffCkt：一种基于扩散模型的混合神经网络框架，用于模拟电路的自动晶体管级生成，可以直接生成针对特定性能要求的电路结构和器件参数。 为了更准确地量化由DiffCkt生成的电路效率，我们引入了电路生成效率指数（CGEI），该指数由单个生成电路的优值（FOM）和消耗的时间共同决定。 与相关研究相比，DiffCkt的CGEI提高了$2.21 \sim 8365\times$倍，达到了最先进（SOTA）水平。 总之，这项工作表明扩散模型具有学习和生成模拟电路结构及器件参数的显著能力，为模拟电路预布局设计的自动化提供了一种革命性方法。 电路数据集将开源，其预览版可在 https://github.com/CjLiu-NJU/DiffCkt 获取。 显示英文摘要

摘要： Analog circuit design consists of the pre-layout and layout phases. Among them, the pre-layout phase directly decides the final circuit performance, but heavily depends on experienced engineers to do manual design according to specific application scenarios. To overcome these challenges and automate the analog circuit pre-layout design phase, we introduce DiffCkt: a diffusion model-based hybrid neural network framework for the automatic transistor-level generation of analog circuits, which can directly generate corresponding circuit structures and device parameters tailored to specific performance requirements. To more accurately quantify the efficiency of circuits generated by DiffCkt, we introduce the Circuit Generation Efficiency Index (CGEI), which is determined by both the figure of merit (FOM) of a single generated circuit and the time consumed. Compared with relative research, DiffCkt has improved CGEI by a factor of $2.21 \sim 8365\times$, reaching a state-of-the-art (SOTA) level. In conclusion, this work shows that the diffusion model has the remarkable ability to learn and generate analog circuit structures and device parameters, providing a revolutionary method for automating the pre-layout design of analog circuits. The circuit dataset will be open source, its preview version is available at https://github.com/CjLiu-NJU/DiffCkt.