학술논문

Consumer Electronics Product Manufacturing Time Reduction and Optimization Using AI-Based PCB and VLSI Circuit Designing
Document Type
Periodical
Source
IEEE Transactions on Consumer Electronics IEEE Trans. Consumer Electron. Consumer Electronics, IEEE Transactions on. 69(3):240-249 Aug, 2023
Subject
Power, Energy and Industry Applications
Components, Circuits, Devices and Systems
Fields, Waves and Electromagnetics
Routing
Circuit synthesis
Steiner trees
Very large scale integration
Artificial intelligence
Printed circuits
Machine learning
Reinforcement learning
NP-hard problem
PCB
VLSI
circuit design
circuit routing
artificial intelligence
machine learning
deep reinforcement learning
Language
ISSN
0098-3063
1558-4127
Abstract
Circuit design plays an essential role in all consumer electronics products. Printed circuit board (PCB) and very-large-scale integration (VLSI) circuit designing requires optimization of the electronic component’s placement and wire routing to connect the components. Currently, circuit routing processes have been performed manually by experts, which greatly increases the cost of human resources and time. Such heuristic circuit designs are not optimized and may have errors, which is why automated circuit routing algorithms are important. However, it is difficult to obtain an optimal solution in circuit routing as it is an NP-hard problem. In addition, poor circuit routing increases the wire length of the circuit, which causes an increase in circuit cost and weight as well as performance degradation. In order to achieve routing optimization, many technologies have been proposed, in which some have applied artificial intelligence (AI) to improve the overall performance and reduce the designing time. Accordingly, in this paper, routing problems in PCB and VLSI are explained, and proposed technologies to solve these routing problems are introduced. Especially, a detailed investigation and analysis of AI technologies grafted into circuit routing algorithms are explained, and the considerations for AI-based routing algorithms are presented.