Work

Machine Learning-Based Performance Optimization of Copper–Graphene Heterointerconnects

Year: 2026

Type: article

Abstract: This work introduces a model-based optimization approach for on-chip copper-graphene interconnects. The aim of this study is to optimize the signal integrity (SI) characteristics of these nano interconnects. Employing existing optimization techniques to improve the SI performance of advanced technology node copper-graphene interconnects remains challenging due to prohibitive computational costs as these interconnects require precise characterization through computationally exhaustive full-wave simulations. Our current work proposes a flexible and efficient modeling and optimization framework that improves SI compared to electromagnetic (EM) simulations using advanced, data-intensive optimization algorithms such as Genetic Algorithm (GA) and Pattern Search. We use theGamultiobjectiveandParetosearchalgorithms to demonstrate the capabilities of the proposed framework and verify the accuracy of results in both single- and multiobjective scenarios with the help of two numerical examples.

Source: IEEE Transactions on Components Packaging and Manufacturing Technology

DOI: 10.1109/tcpmt.2026.3658703

Authors: Suyash Kushwaha, P. R. Shamini, Sourajeet Roy, Domenico Spina, Rohit Sharma

Institutions: Indian Institute of Technology Ropar, Indian Institute of Technology Roorkee, Vrije Universiteit Brussel

Language: English

Topic: Copper Interconnects and Reliability

Subfield: Electronic, Optical and Magnetic Materials

Field: Materials Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W7125963904