Unet-Astar: A Deep Learning-Based Fast Routing Algorithm for Unified PCB Routing

被引:0
|
作者
Yin, Shiyuan [1 ]
Jin, Min [1 ]
Chen, Gang [1 ]
Gong, Guoliang [1 ]
Mao, Wenyu [1 ]
Lu, Huaxiang [1 ,2 ,3 ,4 ]
机构
[1] Chinese Acad Sci, Inst Semicond, High Speed Circuit & Neural Network Lab, Beijing 100083, Peoples R China
[2] Semicond Neural Network Intelligent Percept & Comp, Beijing 100083, Peoples R China
[3] Univ Chinese Acad Sci, Coll Microelect, Beijing 100049, Peoples R China
[4] Univ Chinese Acad Sci, Mat & Optoelect Res Ctr, Beijing 100049, Peoples R China
基金
中国国家自然科学基金;
关键词
Routing; Solid modeling; Layout; Task analysis; Prediction algorithms; Machine learning algorithms; Physical design; Printed circuits; Machine learning; printed circuit board; routing; machine learning; ESCAPE; DESIGN;
D O I
10.1109/ACCESS.2023.3323589
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In recent years, there has been extensive research on the routing problem of printed circuit boards (PCBs). Due to the increasing number of pins, high pin density, and unique physical constraints, manual PCB routing has become a time-consuming task to achieve design convergence. Previous work decomposed the problem into escape routing and area routing, focusing on these problems separately. However, there was always a gap between these two problems, requiring significant human effort for iterative algorithm adjustments. Furthermore, previous area routing work mainly focused on routing between ball grid array (BGA) packages in escape routing. However, in practice, many components are not in the form of BGA packages, such as passive devices, decoupling capacitors, and through-hole pin arrays. Therefore, it is necessary to study a unified routing approach. The current unified routing approach adopts the A* algorithm, but there is still room for improvement in routing speed. This paper proposes a new algorithm called Unet-Astar, which accelerates the routing efficiency by employing deep learning algorithms in a simulated environment. Additionally, a Deeper Unet is proposed for generating recommended regions for the routing algorithm. The new network structure can provide more contextual information, thereby improving routing efficiency. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm. Specifically, for all given test cases, our router achieves approximately a 70% improvement in runtime speed compared to the old router. Another major contribution of this work is the development of a routing problem set generator, which can generate parameterized routing problem sets with different sizes and constraints. This enables the evaluation of different routing algorithms and the generation of training datasets for future data-driven routing methods. All the code has been open-sourced and can be found at https://github.com/Firesuiry/Unet-Astar-For-PCB-Routing.
引用
收藏
页码:113712 / 113725
页数:14
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