Circuit Partitioning for PCB Netlist Based on Net Attributes

Da Meng, Yanze Zheng
{"title":"Circuit Partitioning for PCB Netlist Based on Net Attributes","authors":"Da Meng, Yanze Zheng","doi":"10.1109/ICMLC56445.2022.9941328","DOIUrl":null,"url":null,"abstract":"As we all know, the difficulty of automatic placement and routing is proportional to the size of the circuit. Through Printed circuit board (PCB) netlist partition algorithms, PCB circuits can be divided into different sub-modules, and the problem scale can be effectively reduced in order to obtain the optimal automatic layout and routing. It is observed that when engineers design circuits, they usually mark important nets by annotation, called net attributes. This paper proposes a PCB netlist partition approach based on net attributes. Our partition approach takes the netlist as input, and module partition set as output. Firstly, the modules are pre-partitioned using net attributes. Further, the special patterns in circuits are discovered, and the scattered resistors, capacitors and other components caused by pre-partitioning according to net attributes would be allocated to initial modules by classifying and module matching rules. Our method is evaluated on 11 PCB netlists, and experimental results show that our proposed netlist partition approach outperforms the state of the arts, which can achieve 80%–96% partition accuracy.","PeriodicalId":117829,"journal":{"name":"2022 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Machine Learning and Cybernetics (ICMLC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC56445.2022.9941328","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

As we all know, the difficulty of automatic placement and routing is proportional to the size of the circuit. Through Printed circuit board (PCB) netlist partition algorithms, PCB circuits can be divided into different sub-modules, and the problem scale can be effectively reduced in order to obtain the optimal automatic layout and routing. It is observed that when engineers design circuits, they usually mark important nets by annotation, called net attributes. This paper proposes a PCB netlist partition approach based on net attributes. Our partition approach takes the netlist as input, and module partition set as output. Firstly, the modules are pre-partitioned using net attributes. Further, the special patterns in circuits are discovered, and the scattered resistors, capacitors and other components caused by pre-partitioning according to net attributes would be allocated to initial modules by classifying and module matching rules. Our method is evaluated on 11 PCB netlists, and experimental results show that our proposed netlist partition approach outperforms the state of the arts, which can achieve 80%–96% partition accuracy.
基于网络属性的PCB网表电路划分
众所周知,自动放置和布线的难度与电路的大小成正比。通过印制电路板(Printed circuit board, PCB)网表划分算法,将PCB电路划分为不同的子模块,有效地减小了问题规模,从而获得最优的自动布局布线。我们观察到,工程师在设计电路时,通常会对重要的网络进行标注,称为网络属性。提出了一种基于网络属性的PCB网表划分方法。我们的分区方法将网络列表作为输入,模块分区集作为输出。首先,使用net属性对模块进行预分区。进而发现电路中的特殊模式,根据网络属性预划分导致的电阻、电容等元器件的分散,通过分类和模块匹配规则将其分配给初始模块。我们的方法在11个PCB网表上进行了评估,实验结果表明,我们提出的网表划分方法优于目前的技术水平,可以达到80%-96%的划分精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信