Hierarchical Automatic Power Plane Generation with Genetic Optimization and Multilayer Perceptron

TL;DR

This paper introduces GOMLP, an automated method combining genetic optimization and neural networks to generate power planes in PCB design, outperforming traditional A* methods in efficiency and quality.

Contribution

The paper presents a novel automated power plane generation approach using genetic algorithms and multilayer perceptrons, applicable to diverse PCB problems and extending to multilayer designs.

Findings

GOMLP outperforms A* in 71% of single-layer problems.

The method effectively minimizes islands in power plane generation.

H-GOMLP extends the approach to multilayer problems using hierarchical clustering.

Abstract

We present an automatic multilayer 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 GOMLP 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 on single layer power plane problems, our method outperforms A* in 71% of the problems with varying levels of board layout difficulty. We further describe H-GOMLP, which extends GOMLP to multilayer power plane problems using hierarchical clustering and net similarities based on the Hausdorff distance.