2026-09-19T12:38:15Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1559112024-08-02T03:57:52Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Daniel, Luca
Ngô, Thomas
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
2024-08-01T19:06:58Z
2024-08-01T19:06:58Z
2024-02
2024-07-11T15:29:37.019Z
https://hdl.handle.net/1721.1/155911
Designing a printed circuit board (PCB) is a complex process that involves creating a schematic, placing components, ensuring that every component is routable, and performing simulations to predict the behavior of the PCB before it is manufactured. With the rise of technological innovations, the demand for chips will increase, putting pressure on the electronic design automation (EDA) industry to innovate in PCB design. As part of Cadence’s Allegro X AI team, which aims to develop AI technology to automate PCB designers’ tasks, we explored the application of multi-objective genetic optimization in component placements as an alternative method for automating component placement. More specifically, we applied genetic optimization to a two-sided printed circuit board (PCB). We discovered that employing multiple objectives, such as half-perimeter wirelength and routability, produces promising component placements.
M.Eng.
Massachusetts Institute of Technology
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Copyright retained by author(s)
https://creativecommons.org/licenses/by-nc-nd/4.0/
Application of Multi-Objective Genetic Optimization in PCB Component Placement
Thesis
application/pdf
Master
Master of Engineering in Computation and Cognition
Publication
unknown
unknown
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<Abstract>Designing a printed circuit board (PCB) is a complex process that involves creating a schematic, placing components, ensuring that every component is routable, and performing simulations to predict the behavior of the PCB before it is manufactured. With the rise of technological innovations, the demand for chips will increase, putting pressure on the electronic design automation (EDA) industry to innovate in PCB design. As part of Cadence’s Allegro X AI team, which aims to develop AI technology to automate PCB designers’ tasks, we explored the application of multi-objective genetic optimization in component placements as an alternative method for automating component placement. More specifically, we applied genetic optimization to a two-sided printed circuit board (PCB). We discovered that employing multiple objectives, such as half-perimeter wirelength and routability, produces promising component placements.</Abstract>
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