• Journal of Terahertz Science and Electronic Information Technology
  • Vol. 21, Issue 6, 819 (2023)
CHEN Xingyu*, SHI Dan, and WANG Yunpeng
Author Affiliations
  • [in Chinese]
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    DOI: 10.11805/tkyda2020679 Cite this Article
    CHEN Xingyu, SHI Dan, WANG Yunpeng. PCB crosstalk prediction based on machine learning[J]. Journal of Terahertz Science and Electronic Information Technology , 2023, 21(6): 819 Copy Citation Text show less

    Abstract

    With the rapid improvement of clock frequency in electronic system, crosstalk has become one of the problems that Printed Circuit Board(PCB) designers must concern. Although the design cost has been cut to a certain degree, it still takes a lot of time to simulate the crosstalk on PCB even with the help of high-speed circuit simulation software. Aiming to improve the efficiency of PCB crosstalk prediction, a new data structure is proposed to describe PCBs. The factors that cause crosstalk on PCB are comprehensively analyzed, and a PCB crosstalk prediction system is built by using Natural Language Processing(NLP), which reduces the time for crosstalk prediction to the magnitude of seconds and achieves 73.2% accuracy.
    CHEN Xingyu, SHI Dan, WANG Yunpeng. PCB crosstalk prediction based on machine learning[J]. Journal of Terahertz Science and Electronic Information Technology , 2023, 21(6): 819
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