FNNs Models for Regression of S-Parameters in Multilayer Interconnects with Different Electrical Lengths

Sánchez-Masís, Allan; Rimolo-Donadio, Renato; Roy, Kallol; Sulaiman, Modar; Schuster, Christian

doi:10.1109/lamc59011.2023.10375594

2023 IEEE MTT-S Latin America Microwave Conference (LAMC) 2023

DOI: 10.1109/lamc59011.2023.10375594

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FNNs Models for Regression of S-Parameters in Multilayer Interconnects with Different Electrical Lengths

Allan Sánchez-Masís,

Renato Rimolo-Donadio,

Kallol Roy

et al.

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2024

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Cited by 3 publications

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GNN-SP: Fast S-Parameter Estimation of Chiplet Interconnect via Graph Neural Network

Liu,

Li,

et al. 2024

IEEE Trans. Compon., Packag. Manufact. Technol.

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GNN-SP: Fast S-Parameter Estimation of Chiplet Interconnect via Graph Neural Network

Liu,

Li,

et al. 2024

IEEE Trans. Compon., Packag. Manufact. Technol.

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Generation and Application of a Very Large Dataset for Signal Integrity Via Array and Link Analysis

Hillebrecht,

Schierholz,

Hassab

et al. 2024

IEEE Trans. Electromagn. Compat.

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Predicting the Characteristics of High-Speed Serial Links Based on a Deep Neural Network (DNN)—Transformer Cascaded Model

Wu,

Zhou,

Jiang

et al. 2024

Electronics

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The design level of channel physical characteristics has a crucial influence on the transmission quality of high-speed serial links. However, channel design requires a complex simulation and verification process. In this paper, a cascade neural network model constructed of a Deep Neural Network (DNN) and a Transformer is proposed. This model takes physical features as inputs and imports a Single-Bit Response (SBR) as a connection, which is enhanced through predicting frequency characteristics and equalizer parameters. At the same time, signal integrity (SI) analysis and link optimization are achieved by predicting eye diagrams and channel operating margins (COMs). Additionally, Bayesian optimization based on the Gaussian process (GP) is employed for hyperparameter optimization (HPO). The results show that the DNN–Transformer cascaded model achieves high-precision predictions of multiple metrics in performance prediction and optimization, and the maximum relative error of the test-set results is less than 2% under the equalizer architecture of a 3-taps TX FFE, an RX CTLE with dual DC gain, and a 12-taps RX DFE, which is more powerful than other deep learning models in terms of prediction ability.

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FNNs Models for Regression of S-Parameters in Multilayer Interconnects with Different Electrical Lengths

Cited by 3 publications

References 12 publications

GNN-SP: Fast S-Parameter Estimation of Chiplet Interconnect via Graph Neural Network

GNN-SP: Fast S-Parameter Estimation of Chiplet Interconnect via Graph Neural Network

Generation and Application of a Very Large Dataset for Signal Integrity Via Array and Link Analysis

Predicting the Characteristics of High-Speed Serial Links Based on a Deep Neural Network (DNN)—Transformer Cascaded Model

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