刘乘杰同学题为《A Diffusion Model-Based HybridNeural Network Framework for AutomaticTransistor-Level Generation of AnalogCircuits》论文已被International Conference on Computer-Aided Design（国际计算机辅助设计会议，ICCAD）接收。
ICCAD是电子设计自动化(EDA)领域最顶级的国际学术会议之一，与 DAC、DATE 并列为 EDA三大旗舰会议。
Abstract：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 ~ 8365×, 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://anonymous.4open.science/r/DiffCkt-ICCAD/.
总结：引入 DiffCkt 框架，利用扩散模型实现模拟电路晶体管级的自动生成，根据特定性能要求直接生成电路结构和器件参数，提高设计效率和自动化水平。
实验结果：输入随机指标，可以直接生成对应电路，正确率达80%以上。相较于相似工作，生成效率提高了2.21 ~ 8365×，达到了SOTA水准。
