Abstract
This article proposes an accelerated statistical eye diagram estimation method for efficient signal integrity (SI) analysis. An eye diagram is a critical metric in the SI analysis, however obtaining an eye diagram is time-consuming in simulation. The required time might be a few weeks depending on the complexity. Because the eye diagram is obtained by superposition of the received waveforms. Statistical eye diagram estimation methods were proposed to make the eye diagram acquisition efficient, however, it still has a limited improvement due to a large number of convolution operations. To address this limitation, the proposed method includes two approaches: (i) an increased voltage step in the amplitude PDF with a compensation factor and (ii) skippable and replaceable convolution operations. The efficiency of the proposed method is verified by comparing the derived result with that of a conventional transient simulation. The previous estimation method requires 1,065 s while approaches (i) and (ii) reduce the required time to 88 and 510 s, respectively. When both approaches are applied, the required time is reduced to 17 s in comparison to the simulation time of 259,200 s required for a transient simulation. Approach (ii) reduces the number of convolution operations from 768,000 to 103,000. Therefore, the proposed method successfully reduces the required simulation time and the required number of convolutions to obtain a statistical eye diagram.
Recommended Citation
J. Park et al., "Accelerated Statistical Eye Diagram Estimation Method For Efficient Signal Integrity Analysis," IEEE Access, vol. 11, pp. 132699 - 132707, Institute of Electrical and Electronics Engineers, Jan 2023.
The definitive version is available at https://doi.org/10.1109/ACCESS.2023.3336568
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Electromagnetic compatibility (EMC); eye diagram; eye diagram estimation method; high-speed systems; signal integrity (SI); single-bit response (SBR)
International Standard Serial Number (ISSN)
2169-3536
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2023 The Authors, All rights reserved.
Publication Date
01 Jan 2023
Comments
National Science Foundation, Grant IIP-1916535