| Autor: |
Haiguang Liao, Vinay Patil, Xuliang Dong, Devika Shanbhag, Elias Fallon, Taylor Hogan, Mirko Spasojevic, Levent Burak Kara |
| Rok vydání: |
2022 |
| Zdroj: |
Volume 3A: 48th Design Automation Conference (DAC). |
| DOI: |
10.1115/detc2022-90046 |
| Popis: |
We present an automatic power plane generation method to accelerate the design of printed circuit boards (PCB). In PCB design, while automatic solvers have been developed to predict important indicators such as the IR-drop, power integrity, and signal integrity, the generation of the power plane itself still largely relies on laborious manual methods. Our automatic power plane generation approach is based on genetic optimization combined with a multilayer perceptron and is able to automatically generate power planes across a diverse set of problems with varying levels of difficulty. Our method consists of an outer loop genetic optimizer (GO) and an inner loop multi-layer perceptron (MLP) that generate power planes automatically. The critical elements of our approach include contour detection, feature expansion, and a distance measure to enable island-minimizing complex power plane generation. We compare our approach to a baseline solution based on A*. The A* method consisting of a sequential island generation and merging process which can produce less than ideal solutions. Our experimental results show that our method outperforms A* in 71% of the design problems with varying levels board layout difficulty. We also present ablation studies demonstrating the influence of various algorithmic choices. Finally, we provide insights into how the power planes evolve with our model parameters to form feasible and desirable space partitions. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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