This paper presents a heuristic approach for solving the placement of Analog and Mixed-Signal Integrated Circuits. Placement is a crucial step in the physical design of integrated circuits. During this step, designers choose the position and variant of each circuit device. We focus on the specific class of analog placement, which requires so-called pockets, their possible merging, and parametrizable minimum distances between devices, which are features mostly omitted in recent research and literature. We formulate the problem using Integer Linear Programming and propose a priority-based constructive heuristic inspired by algorithms for the Facility Layout Problem. Our solution minimizes the perimeter of the circuit's bounding box and the approximated wire length. Multiple variants of the devices with different dimensions are considered. Furthermore, we model constraints crucial for the placement problem, such as symmetry groups and blockage areas. Our outlined improvements make the heuristic suitable to handle complex rules of placement. With a search guided either by a Genetic Algorithm or a Covariance Matrix Adaptation Evolution Strategy, we show the quality of the proposed method on both synthetically generated and real-life industrial instances accompanied by manually created designs. Furthermore, we apply reinforcement learning to control the hyper-parameters of the genetic algorithm. Synthetic instances with more than 200 devices demonstrate that our method can tackle problems more complex than typical industry examples. We also compare our method with results achieved by contemporary state-of-the-art methods on the MCNC and GSRC datasets.

翻译：本文提出了一种用于解决模拟与混合信号集成电路布局问题的启发式方法。布局是集成电路物理设计中的关键步骤。在此步骤中，设计者需确定每个电路器件的位置与变体选择。我们专注于模拟布局这一特定类别，其要求包含所谓的布局区域（pocket）、区域间的可合并性以及器件间参数化的最小间距——这些特征在当前研究与文献中大多被忽略。我们采用整数线性规划对问题进行建模，并提出一种受设施布局问题算法启发的基于优先级的构造启发式方法。我们的解决方案旨在最小化电路边界框的周长与近似线长。该方法考虑了具有不同尺寸的多种器件变体。此外，我们建模了布局问题的关键约束条件，如对称组和阻塞区域。我们所概述的改进使该启发式方法能够处理复杂的布局规则。通过采用遗传算法或协方差矩阵自适应进化策略进行搜索引导，我们在合成生成实例和实际工业实例（附带人工设计）上验证了所提方法的质量。进一步地，我们应用强化学习技术来控制遗传算法的超参数。在包含200个以上器件的合成实例中，我们的方法能够处理比典型工业案例更为复杂的问题。我们还在MCNC和GSRC基准数据集上，将所提方法与当前先进方法的结果进行了对比。