Interconnects are essential components of any electronic system. Their design, modeling and optimization are becoming complex and computationally expensive with the evolution of semiconductor technology as the devices of nanometer dimensions are being used. In high-speed applications, system level simulations are needed to ensure the robustness of a system in terms of signal and power quality. The simulations are becoming very expensive because of the large dimensional systems and their full-wave models. Machine learning techniques can be used as computationally efficient alternatives in the design cycle of the interconnects. This paper presents a review of the applications of machine learning techniques for design, optimization and analysis of interconnects in high-speed electronic systems. A holistic discussion is presented, including the basics of interconnects, their impact on the system performance, popular machine learning techniques and their applications related to the interconnects. The performance evaluation, optimization and variability analysis of interconnects are discussed in detail. Future scope and overlook that are presented in the literature are also discussed.

中文翻译：

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用于互连建模和性能分析的机器学习技术

互连是任何电子系统的重要组成部分。随着半导体技术的发展，随着纳米尺寸器件的使用，它们的设计、建模和优化变得复杂且计算成本高昂。在高速应用中，需要系统级仿真来确保系统在信号和电源质量方面的稳健性。由于大维度系统及其全波模型，模拟变得非常昂贵。机器学习技术可用作互连设计周期中计算效率高的替代方案。本文综述了机器学习技术在高速电子系统互连设计、优化和分析中的应用。提出了一个整体的讨论，包括互连的基础知识、它们对系统性能的影响、流行的机器学习技术及其与互连相关的应用。详细讨论了互连的性能评估、优化和可变性分析。还讨论了文献中提出的未来范围和忽视。