用深度学习优化芯片封装布线中的网络排序，提升全局布线效果。
Machine Learning Optimal Ordering in Global Routing Problems in Semiconductors
- 基于深度学习自动学习网络排序策略，替代传统启发式方法。
- 在多层芯片封装环境中，排序效果优于传统启发式方法。
- 适合从事集成电路设计与自动化布线的工程师参考。
本文提出一种新的方法，用于多层半导体封装中全局布线过程的网络排序。该方法基于机器学习技术，在多层半导体封装环境下进行全局布线实验，验证了所提方法在网表排序上的表现优于基于启发式评分函数的传统方法。研究聚焦于全局布线中的层分配阶段，证明深度学习可显著提升网络排序质量。结果表明，采用机器学习进行网络排序能有效改善布线性能。
原文摘要 · Abstract (English)
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.
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