Component Centric Placement Using Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2602.23540v1
• Date: Thu, 26 Feb 2026 22:44:03 GMT
• Title: Component Centric Placement Using Deep Reinforcement Learning
• Abstract summary: reinforcement learning is successfully applied to system-on-chip IP block placement and chiplet arrangement in complex packages.<n>PCB component placement presents unique challenges due to variation in component sizes, single- and double-sided boards, wirelength constraints, board constraints, and non-overlapping placement requirements.<n>In this work, we adopt a component-centric layout for automating PCB component placement using RL.
• Score: 0.20305676256390928
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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