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 functioning placements without the need for fine tuning. Additionally, this exploration considers true use cases of PCB auto-placement where a human-in-the-loop pre-places a set list of components and the auto-placer places the remaining. This is achieved by first restricting the placement domain to only ring placements; a domain where routing calculations become accessible. Within the ring placement domain, an RL agent is trained to place components on a simulated PCB canvas such that there are no component overlaps or wire crossings upon manufacture. Through the use of an unbounded reward system, the agent is trained progressively with PCB complexity gradually increasing as training steps are run. The resulting placements are robust to varying numbers of components as well as component shape and size. Finally, this thesis concludes with a discussion about further work and challenges facing the future of PCB auto-placement.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Crocker, Peter

Advisor dc:contributor.advisor

• Chan, Vincent W.S.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/139247

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/139247