Diffusion Barrier Prediction of Graphene and Boron Nitride for Copper Interconnects by Deep Learning

Manareldeen Ahmed,Yan Li,Wenchao Chen,Er-Ping Li

doi:10.1109/access.2020.3039257

Manareldeen Ahmed, Yan Li + Show 2 more

Open Access

https://doi.org/10.1109/access.2020.3039257

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Abstract

The continuous scaling-down size of interconnects should be accompanied with ultra-thin diffusion barrier layers, which is used to suppress Cu diffusion into the dielectrics. Unfortunately, conventional barrier layers with thicknesses less than 4 nm fail to perform well. With the advent of 2D layered materials, graphene and hexagonal boron nitride have been proposed as alternative Cu diffusion barriers with thicknesses of ≈1 nm. However, defects such as vacancies may evolve into a Cu diffusion path, which is a challenging problem in design of diffusion barrier layers. The energy barrier of Cu atom diffused through a di-vacancy defect in graphene and hexagonal boron nitride is calculated by density functional theory. It is found that graphene offers higher energy barrier to Cu than hexagonal boron nitride. The higher energy barrier is attributed to the stronger interaction between Cu and C atoms in graphene as shown by charge density difference and Bader’s charge. Furthermore, we use the energy barriers of different vacancy structures and generate a dataset that will be used for machine learning. Our trained convolutional neural network is used to predict the energy barrier of Cu migration through randomly configured defected graphene and hexagonal boron nitride with $R^{2}$ of >99% for $4 \times 4$ supercell. These results provide guides on choosing between 2D materials as barrier layers, and applying deep learning to predict the 2D barrier performance.

Highlights

A CCORDING to the international technology roadmap for semiconductors (ITRS) [1], the device size is shrinking continuously from 22 nm in 2012, to 14 nm in 2014, 10 nm in 2016, 7 nm in 2018 and 5 nm in 2020
In addition to Cu diffusion, the size effect presents a major issue in Cu interconnects
GRAPHENE AND HEXAGONAL BORON NITRIDE AS BARRIER LAYERS IN CU INTERCONNECTS we investigate graphene and h-BN as barrier layers for Cu atom diffusion through sheet defect

Summary

INTRODUCTION

A CCORDING to the international technology roadmap for semiconductors (ITRS) [1], the device size is shrinking continuously from 22 nm in 2012, to 14 nm in 2014, 10 nm in 2016, 7 nm in 2018 and 5 nm in 2020. In addition to the scaling-down issues for these materials, the adhesion to Cu is not ideal and presents a challenge Other materials such as Ta has been integrated between the diffusion barrier and Cu. With the advent of nanotechnology, 2D materials can play an important role in IC technology. Many 2D materials were found to exhibit good blocking properties Among these are graphene and hexagonal boron nitride (hBN). A combination of machine learning and material databases successfully predicted several properties of stoichiometric inorganic crystalline material, such as material classification, bandgap energy and heat capacities [29] Another combination of analytical solution and molecular dynamics were developed to train a shallow and deep neural networks to predict fracture stress of graphene samples [30].

GRAPHENE AND HEXAGONAL BORON NITRIDE AS BARRIER LAYERS IN CU INTERCONNECTS

ENERGY BARRIER CALCULATION

CONCLUSION

SIMULATION DETAILS