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Updated: Feb 28, 2026

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Revealing Neural Circuit Topography in Multi-Color
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一个基于图形的基准数据集,用于打印电路网清单分区
Scientific data
|February 25, 2026
概括
本研究介绍了BenchPCNP,这是一个用于打印电路网列分区的新数据集. 它解决了对标记数据的需求,以推进电子设计自动化中的图形机器学习.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 机器学习 机器学习
背景情况:
- 打印电路网清单分区对于电子设计自动化和逆向工程至关重要.
- 图形机器学习提供了新的方法,但缺乏基准数据集.
- 现有的研究面临挑战,因为缺乏高质量的,标记的网列数据.
研究的目的:
- 提出一种在Protel 2格式中构建网列表图形数据的方法.
- 创建一个标记的分区印刷电路网列图形数据集 (BenchPCNP).
- 为评估网列表分区研究提供可靠的基准.
主要方法:
- 开发了一种用于网列图形数据的构造方法.
- 根据IPC-2612标准收集和分割了50个经过生产验证的实用电路.
- 标注了数据集的54个不同的分区模块标签.
主要成果:
- 成功创建了BenchPCNP数据集.
- 该数据集包含50个分区电路,并附有详细的注释.
- 为全面分析提供54个独特的分区模块标签.
结论:
- BenchPCNP数据集解决了对标记网列数据的关键需求.
- 在网列表分区中促进研究一致性和域调整.
- 作为在EDA中推进图形机器学习的有价值的基准.
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