Application of Multi-Objective Genetic Optimization in PCB Component Placement

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Ngô, Thomas

Advisor dc:contributor.advisor

• Daniel, Luca

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/155911

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/155911