Recent advances in machine learning (ML) for automating analog circuit synthesis have been significant, yet challenges remain. A critical gap is the lack of a standardized evaluation framework, compounded by various process design kits (PDKs), simulation tools, and a limited variety of circuit topologies. These factors hinder direct comparisons and the validation of algorithms. To address these shortcomings, we introduced AnalogGym, an open-source testing suite designed to provide fair and comprehensive evaluations. AnalogGym includes 30 circuit topologies in five categories: sensing front ends, voltage references, low dropout regulators, amplifiers, and phase-locked loops. It supports several technology nodes for academic and commercial applications and is compatible with commercial simulators such as Cadence Spectre, Synopsys HSPICE, and the open-source simulator Ngspice. AnalogGym standardizes the assessment of ML algorithms in analog circuit synthesis and promotes reproducibility with its open datasets and detailed benchmark specifications. AnalogGym's user-friendly design allows researchers to easily adapt it for robust, transparent comparisons of state-of-the-art methods, while also exposing them to real-world industrial design challenges, enhancing the practical relevance of their work. Additionally, we have conducted a comprehensive comparison study of various analog sizing methods on AnalogGym, highlighting the capabilities and advantages of different approaches. AnalogGym is available in the GitHub repository https://github.com/CODA-Team/AnalogGym. The documentation is also available at http://coda-team.github.io/AnalogGym/.

翻译：近年来，机器学习在自动化模拟电路综合方面取得了显著进展，但仍面临诸多挑战。一个关键缺口是缺乏标准化的评估框架，加之各种工艺设计套件、仿真工具以及有限的电路拓扑种类，这些因素阻碍了算法的直接比较与验证。为弥补这些不足，我们推出了AnalogGym——一个旨在提供公平、全面评估的开源测试套件。AnalogGym包含五大类共30种电路拓扑：传感前端、电压基准、低压差线性稳压器、放大器和锁相环。它支持多个适用于学术与商业应用的技术节点，并兼容Cadence Spectre、Synopsys HSPICE等商业仿真器以及开源仿真器Ngspice。AnalogGym通过其开放数据集和详细的基准规范，标准化了机器学习算法在模拟电路综合中的评估，并促进了研究的可复现性。其用户友好设计使研究人员能够轻松适配该套件，以进行稳健、透明的先进方法比较，同时接触真实的工业设计挑战，从而提升其工作的实际相关性。此外，我们已在AnalogGym上对多种模拟电路尺寸优化方法进行了全面比较研究，突显了不同方法的能力与优势。AnalogGym已在GitHub仓库https://github.com/CODA-Team/AnalogGym 中开放，相关文档亦可通过http://coda-team.github.io/AnalogGym/ 获取。