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 dem...
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| Other Authors: | |
| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/155911 |
| _version_ | 1811071718066749440 |
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| author | Ngô, Thomas |
| author2 | Daniel, Luca |
| author_facet | Daniel, Luca Ngô, Thomas |
| author_sort | Ngô, Thomas |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T08:55:22Z |
| format | Thesis |
| id | mit-1721.1/155911 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T08:55:22Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1559112024-08-02T03:57:52Z Application of Multi-Objective Genetic Optimization in PCB Component Placement Ngô, Thomas Daniel, Luca Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences 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. 2024-08-01T19:06:58Z 2024-08-01T19:06:58Z 2024-02 2024-07-11T15:29:37.019Z Thesis https://hdl.handle.net/1721.1/155911 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/pdf Massachusetts Institute of Technology |
| spellingShingle | Ngô, Thomas Application of Multi-Objective Genetic Optimization in PCB Component Placement |
| title | Application of Multi-Objective Genetic Optimization in PCB Component Placement |
| title_full | Application of Multi-Objective Genetic Optimization in PCB Component Placement |
| title_fullStr | Application of Multi-Objective Genetic Optimization in PCB Component Placement |
| title_full_unstemmed | Application of Multi-Objective Genetic Optimization in PCB Component Placement |
| title_short | Application of Multi-Objective Genetic Optimization in PCB Component Placement |
| title_sort | application of multi objective genetic optimization in pcb component placement |
| url | https://hdl.handle.net/1721.1/155911 |
| work_keys_str_mv | AT ngothomas applicationofmultiobjectivegeneticoptimizationinpcbcomponentplacement |