Machine Learning Optimal Ordering in Global Routing Problems in Semiconductors

TL;DR

This paper introduces a machine learning-based method for net ordering in global routing of multilayered semiconductor packages, demonstrating superior performance over traditional heuristic techniques.

Contribution

The paper presents a novel deep learning approach for net ordering in global routing, improving upon conventional heuristic-based methods in semiconductor design.

Findings

Machine learning-based net ordering outperforms heuristics in routing quality.

Deep learning significantly enhances the net ordering process.

Experimental results show improved routing efficiency in semiconductor packages.

Abstract

In this work, we propose a new method for ordering nets during the process of layer assignment in global routing problems. The global routing problems that we focus on in this work are based on routing problems that occur in the design of substrates in multilayered semiconductor packages. The proposed new method is based on machine learning techniques and we show that the proposed method supersedes conventional net ordering techniques based on heuristic score functions. We perform global routing experiments in multilayered semiconductor package environments in order to illustrate that the routing order based on our new proposed technique outperforms previous methods based on heuristics. Our approach of using machine learning for global routing targets specifically the net ordering step which we show in this work can be significantly improved by deep learning.