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DTSTAMP:20240626T180034Z
LOCATION:3010\, 3rd Floor
DTSTART;TZID=America/Los_Angeles:20240626T111500
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UID:dac_DAC 2024_sess142_RESEARCH1351@linklings.com
SUMMARY:TraceFormer: S-parameter Prediction Framework for PCB Traces based
  on Graph Transformer
DESCRIPTION:Research Manuscript\n\nDoyun Kim, Jaemin Park, Youngmin Oh, an
 d Bosun Hwang (Samsung)\n\nSignal integrity becomes more critical to moder
 n digital systems such as solid-state drives due to their high-speed opera
 tion. However, one of the challenges in signal integrity analysis is S-par
 ameter modeling process for printed circuit boards (PCB). Due to increasin
 g 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 PC
 B traces. Our framework constructs a graph from PCB traces and tokenizes t
 race segments with geometric and topological information. A transformer en
 coder produces PCB representations from the tokens, followed by extraction
  networks which predict four different types of complex-valued S-parameter
 s together. TraceFormer achieved above 0.99 R-squared score up to 15GHz fo
 r 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.\n\nTopic: EDA\n\nKey
 word: Analog CAD, Simulation, Verification and Test\n\nSession Chairs: Wei
 dong Cao (Washington University, St. Louis) and Ahmet Budak (Analog Device
 s, Inc. (ADI))
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