Computationally Driven Advances in Cu-CNT On-Chip Interconnect Materials: From First Principles to Machine Learning

Abstract

As the component dimensions in integrated circuits shrink to extreme scales, the complexity of interconnect systems is increasing significantly, necessitating an urgent and comprehensive upgrade of interconnect materials and manufacturing processes. As the “bridge” linking various on-chip components, the performance of interconnect materials directly influences the overall chip performance, and their evolution has long been a critical driver of advances in chip technology. In recent years, copper-carbon nanotube (Cu-CNT) interconnects have garnered significant attention because they offer electrical conductivity that surpasses that of pure copper, along with a more straightforward fabrication process. Despite the exceptional overall performance of Cu-CNT composites, systematically elucidating their bulk behavior and interfacial bonding mechanisms remains a formidable challenge. Overcoming this bottleneck requires an in-depth investigation of the complex interactions at the Cu-CNT interface and assessment of their effects on the material's mechanical stability and thermal management performance. This review summarizes the applications of atomic-scale first-principles calculations, molecular dynamics (MD) simulations, multiphysics modeling, and machine learning methods to Cu-CNT materials and illustrates their use with examples from recent representative studies. Specifically, it emphasizes the pivotal role of machine learning in deciphering the mechanisms of Cu-CNT composites over multiple spatial and temporal scales. This review provides a systematic reference for academic research and engineering applications of Cu-CNT-based chip interconnect materials and offers perspectives for the development of next-generation high-performance interconnects.

Keywords

Cu-CNT interface / first-principles calculations / machine learning / molecular dynamics / multiphysics modeling

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