用深度强化学习实现电路板组件智能布局，逼近人工水平。
Component Centric Placement Using Deep Reinforcement Learning
- 以主控元件为中心，优先布局周边元器件，缩小搜索空间。
- 在九个真实电路板上测试，布线长度接近人工设计水平。
- 适合需要高效自动布局的电子设计工程师参考。
自动化印刷电路板（PCB）组件布局是布局设计的关键步骤。尽管强化学习（RL）已在系统级芯片IP块和芯粒封装中取得成功，但PCB布局因元件尺寸差异、单双面板、布线长度约束、板面限制及非重叠要求等面临独特挑战。本文提出一种基于组件中心的布局方法：首先将主组件固定于板心，被动元件围绕其引脚就近放置；其次将主组件周围空隙离散化，大幅压缩搜索空间但仍覆盖所有可行位置；同时利用先验知识——每个被动元件需靠近对应电源，设计奖励函数，避免无效探索。采用组件中心布局，我们实现了Deep Q-Network、Actor-Critic算法与模拟退火等多种方法。在九个不同复杂度的真实PCB上评估表明，最佳方法在布线长度与可行性方面已接近人工水平。
原文摘要 · Abstract (English)
Automated placement of components on printed circuit boards (PCBs) is a critical stage in placement layout design. While reinforcement learning (RL) has been successfully applied to system-on-chip IP block placement and chiplet arrangement in complex packages, PCB component placement presents unique challenges due to several factors: variation in component sizes, single- and double-sided boards, wirelength constraints, board constraints, and non-overlapping placement requirements. In this work, we adopt a component-centric layout for automating PCB component placement using RL: first, the main component is fixed at the center, while passive components are placed in proximity to the pins of the main component. Free space around the main component is discretized, drastically reducing the search space while still covering all feasible placement; second, we leverage prior knowledge that each passive's position has to be near to its corresponding voltage source. This allows us to design the reward function which avoids wasted exploration of infeasible or irrelevant search space. Using the component centric layout, we implemented different methods including Deep Q-Network, Actor-Critic algorithm and Simulated Annealing. Evaluation on over nine real-world PCBs of varying complexity shows that our best proposed method approaches near human-like placements in terms of wirelength and feasibility.
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