In the era of advanced nanotechnology where billions of transistors are fabricated in a single chip, high-speed operations are challenging due to packaging related issues. In High-Speed Very Large Scale Integration (VLSI) systems, decoupling capacitors are essentially used in power delivery networks to reduce power supply noise and to maintain a low impedance of the power delivery networks. In this paper, the cumulative impedance of a power delivery network is reduced below the target impedance by using state-of-the-art metaheuristic algorithms to choose and place decoupling capacitors optimally. A Matrix-based Evolutionary Computing (MEC) approach is used for efficient usage of metaheuristic algorithms. Two case studies are presented on a practical system to demonstrate the proposed approach. A comparative analysis of the performance of state-of-the-art metaheuristics is presented with the insights of practical implementation. The consistency of results in both the case studies confirms the validity of the proposed appraoch.

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.

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