Automated design of analog and radio-frequency circuits using supervised or reinforcement learning from simulation data has recently been studied as an alternative to manual expert design. It is straightforward for a design agent to learn an inverse function from desired performance metrics to circuit parameters. However, it is more common for a user to have threshold performance criteria rather than an exact target vector of feasible performance measures. In this work, we propose a method for generating from simulation data a dataset on which a system can be trained via supervised learning to design circuits to meet threshold specifications. We moreover perform the to-date most extensive evaluation of automated analog circuit design, including experimenting in a significantly more diverse set of circuits than in prior work, covering linear, nonlinear, and autonomous circuit configurations, and show that our method consistently reaches success rate better than 90% at 5% error margin, while also improving data efficiency by upward of an order of magnitude. A demo of this system is available at circuits.streamlit.app

翻译：利用监督学习或强化学习从仿真数据中自动设计模拟与射频电路，近年来已被研究作为人工专家设计的替代方案。设计代理学习从期望性能指标到电路参数的逆函数是直观的。然而，用户更常遇到的是阈值性能标准，而非精确的可行性能测量目标向量。在本工作中，我们提出了一种方法，基于仿真数据生成数据集，使系统能够通过监督学习训练以设计满足阈值规格的电路。此外，我们进行了迄今为止最广泛的自动化模拟电路设计评估，包括在比先前工作更多样化的电路集合中进行实验，涵盖线性、非线性及自治电路配置，并证明我们的方法在5%误差容限下始终能达到90%以上的成功率，同时将数据效率提升了一个数量级以上。本系统的演示可见于 circuits.streamlit.app