In this article, we present a PAM-4 IBIS-AMI model derived from machine learning for time-domain simulation. More specifically, we report a Laguerre–Volterra-expanded feed-forward neural network (LVFFN) approach with one hidden layer and ten neurons to model the 28-Gb/s PAM-4 high-speed link buffer. The proposed LVFFN model reduces the model size and improves the computational efficiency dramatically compared with the Volterra series model and other transitional artificial neural network models. The LVFFN model is implemented in IBIS-AMI, an industrial standard, and is simulated in existing software platforms for eye-diagram analysis. This work has two innovations: 1) we propose a method that dramatically reduces the neural network model complexity through a Laguerre–Volterra expansion when modeling weakly nonlinear systems with memory and 2) we implement an LVFFN model into IBIS-AMI to enhance the model interoperability and transportability.

中文翻译：

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Laguerre–Volterra前馈神经网络，用于对PAM-4高速链路建模

在本文中，我们提出了一个PAM-4 IBIS-AMI模型，该模型源自机器学习，用于时域仿真。更具体地说，我们报告了Laguerre-Volterra扩展的前馈神经网络（LVFFN）方法，其中包含一个隐藏层和十个神经元，以对28 Gb / s PAM-4高速链路缓冲区进行建模。与Volterra级数模型和其他过渡性人工神经网络模型相比，所提出的LVFFN模型减小了模型大小，并显着提高了计算效率。LVFFN模型在工业标准IBIS-AMI中实现，并在现有软件平台中进行仿真以进行眼图分析。这项工作有两项创新：