Physically Constrained PCB Placement Using Deep Reinforcement Learning
This thesis provides an in depth exploration of Reinforcement Learning (RL) based PCB component placement with emphasis on physically verified placements. Unlike prior methods that rely on heuristic proxies for placement quality, this work focuses entirely on routing based metrics that result in fun...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/139247 |