Residual Stresses in Cu/Ni Multilayer Thin Films Measured Using the Sin2ψ Method
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Residual stresses in multilayer thin films are of substantial importance to the service life of advanced engineering systems. In this investigation, the residual stresses in magnetron sputtered Cu/Ni multilayer thin films were characterized using x-ray diffraction (XRD) and the sin2ψ method. The influence of layer thickness on residual stress was explored for films with alternating Ni and Cu layers with equal layer thicknesses ranging from 10 nm to 100 nm. To address peak broadening and overlapping, the Gaussian Mixture Model (GMM) and Expectation Maximization (EM) algorithm were employed, and the peak position was determined using the Center of Gravity (CoG) method. Results showed tensile residual stress in both the Cu and Ni layers and a prominent layer thickness dependence. The stress in the Ni layers increased from roughly 880 MPa to 1550 MPa with decreasing layer thickness from 100 nm to 10 nm. In the Cu layers, the stress remained relatively constant at ~250 MPa and then substantially decreased for the 10 nm thickness. The findings confirm that the XRD-based approach can be applied for residual stress measurement in nanoscale multilayer thin films, provided that peak broadening and overlapping issues are addressed. Furthermore, the residual stress in metal multilayers is strongly dependent on layer thickness.
KeywordsResidual stress Multilayer thin-films Nanolaminates sin2ψ method X-ray diffraction
The authors acknowledge technical support from the Molecular Analysis Facility (MAF) of the University of Washington for the residual stress analysis. The MAF is a National Nanotechnology Coordinated Infrastructure site at the University of Washington, which is supported in part by the National Science Foundation (grant ECC-1542101), the University of Washington, the Molecular Engineering & Sciences Institute, the Clean Energy Institute, and the National Institutes of Health. Special thanks are extended to Liam Bradshaw for training and consultation on the use of XRD. The team would like to thank Yang Zhou of the UW for his assistance with SEM imaging, as well as Tyler Johnson of the UW for his consultation on classification algorithms.
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