用改进图形卷积网络与边缘特征聚合进行互连的电迁移分析
Ruqing Ye1, Xiaoming Chen1,2
1School of Integrated Circuits, Dalian University of Technology, Dalian 116024, China.
Micromachines
|August 29, 2024
概括
这项研究引入了一种新的图形神经网络方法,用于预测集成电路互连中的水静电应力,大大提高了电迁移分析的准确性和速度.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
背景情况:
- 在先进的集成电路中,电迁移 (EM) 是一个关键的可靠性问题.
- 传统的使用部分微分方程 (PDEs) 的液压应力分析在计算上是密集的,对于全芯片分析来说是不切实际的.
- 准确的液态应力预测对于理解和减轻EM故障至关重要.
研究的目的:
- 开发一种计算效率高,准确的方法,用于预测电路互连中的液态应力.
- 利用图形神经网络 (GNN) 来建模相互连接结构及其相关的应力分布.
- 提高集成电路中电迁移分析的速度和准确性.
主要方法:
- 概念化的电路互连树作为GNN框架内的图形.
- 使用有限元解决方案软件生成地面真实性水静电应力数据.
- 开发和训练了一种改进的图形卷积网络 (GCN),具有边缘特征聚合和注意力机制.
主要成果:
- 与最初的GCN相比,拟议的GCN模型在根平均平方误差 (RMSE) 中实现了15%的改善.
- 该模型显示,与传统的有限元素方法相比,溶液速度显著增加.
- 成功预测了相互连接树内的节点的液态应力值.
结论:
- 基于GNN的方法为集成电路中的液态应力分析提供了高效和准确的替代方案.
- 这种方法显著加快了电迁移可靠性评估,使实际的全芯片分析成为可能.
- 开发的模型有助于提高先进的半导体设备的长期可靠性.
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