Layout-dependent effects (LDEs) significantly impact analog circuit performance. Traditionally, designers have relied on symmetric placement of circuit components to mitigate variations caused by LDEs. However, due to non-linear nature of these effects, conventional methods often fall short. We propose an objective-driven, multi-level, multi-agent Q-learning framework to explore unconventional design space of analog layout, opening new avenues for optimizing analog circuit performance. Our approach achieves better variation performance than the state-of-the-art layout techniques. Notably, this is the first application of multi-agent RL in analog layout automation. The proposed approach is compared with non-ML approach based on simulated annealing.

翻译：布局依赖效应（LDEs）对模拟电路性能具有显著影响。传统上，设计者依赖电路元件的对称布局来缓解LDEs引起的变化。然而，由于这些效应的非线性特性，传统方法往往效果有限。我们提出了一种目标驱动的多级多智能体Q学习框架，以探索模拟布局的非常规设计空间，为优化模拟电路性能开辟了新途径。我们的方法在变化性能方面优于当前最先进的布局技术。值得注意的是，这是多智能体强化学习在模拟布局自动化中的首次应用。所提出的方法与基于模拟退火的非机器学习方法进行了比较。