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TraceFormer: S-parameter Prediction Framework for PCB Traces based on Graph Transformer

Published: 07 November 2024 Publication History

Abstract

Signal integrity becomes more critical to modern digital systems such as solid-state drives due to their high-speed operation. However, one of the challenges in signal integrity analysis is S-parameter modeling process for printed circuit boards (PCB). Due to increasing PCB design complexity, existing numerical methods take too long to solve governing equations for S-parameters. To overcome the issue, we present a novel deep learning framework, TraceFormer, to predict S-parameters of PCB traces. Our framework constructs a graph from PCB traces and tokenizes trace segments with geometric and topological information. A transformer encoder produces PCB representations from the tokens, followed by extraction networks which predict four different types of complex-valued S-parameters together. TraceFormer achieved above 0.99 R-squared score up to 15GHz for 4-port PCB designs, resulting in less than 3.1% and 4.2% errors in terms of the eye diagram's width and height, respectively.

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Shutong Qi et al. "Deep neural networks for rapid simulation of planar microwave circuits based on their layouts". IEEE Transactions on Microwave Theory and Techniques, 2022.
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Ren Shibata et al. "A Novel Convolutional-Autoencoder Based Surrogate Model for Fast S-parameter Calculation of Planar BPFs". In IEEE International Microwave Symposium, 2022.
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Sriram Ravula et al. "One-Dimensional Deep Image Prior for Curve Fitting of S-Parameters from Electromagnetic Solvers". ICCAD, 2023.
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Daehwan Lho et al. "Channel characteristic-based deep neural network models for accurate eye diagram estimation in high bandwidth memory (HBM) silicon interposer". IEEE Trans. on Electromagnetic Compatibility, 2021.
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Alexey Dosovitskiy et al. "An image is worth 16×16 words: Transformers for image recognition at scale". ICLR, 2021.
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Uri Alon et al. "On the bottleneck of graph neural networks and its practical implications". arXiv preprint arXiv:2006.05205, 2020.
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cover image ACM Conferences
DAC '24: Proceedings of the 61st ACM/IEEE Design Automation Conference
June 2024
2159 pages
ISBN:9798400706011
DOI:10.1145/3649329
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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New York, NY, United States

Publication History

Published: 07 November 2024

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Author Tags

  1. signal integrity
  2. S-parameters
  3. PCB
  4. trace
  5. eye diagram
  6. channel modeling

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DAC '24
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DAC '24: 61st ACM/IEEE Design Automation Conference
June 23 - 27, 2024
CA, San Francisco, USA

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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