In this study, we investigate the role of film thickness in modulating the properties of Cu films deposited on BaTiO3 ceramic substrates using direct current magnetron sputtering (DCMS) and high-power pulsed magnetron sputtering (HiPIMS). While HiPIMS is known for producing dense films, and the thickness-dependent properties of sputtered Cu films are well-documented, this work uniquely explores the synergistic interplay between deposition technique and thickness on BaTiO3 ceramic substrates, revealing novel insights into stress evolution and property optimization for advanced microelectronic and coating applications. Cu films of 300 nm, 1000 nm, and 1700 nm were systematically compared for their microstructures, surface morphologies, and electrical and mechanical properties, elucidating the critical role of thickness in densification, stress state, and overall performance. The results indicate that the target current and voltage waveforms of HiPIMS are similar to square waves, and the ionization rate is significantly higher than that of DCMS. Still, the deposition rate at the same power of 180 W is only 44.6% of that of DCMS. The films obtained by both processes present a strong (111) orientation; the crystallite size of the DCMS film grows with increasing thickness, while the HiPIMS film shows increasing and then decreasing, and its residual stress is overall lower than that of DCMS. In terms of surface morphology, DCMS films appeared porous and rough, whereas HiPIMS films were denser and smoother. In terms of properties, the resistivity of HiPIMS films is significantly lower than that of DCMS, especially at 1000 nm thickness. The binding force is also better than that of DCMS, especially at thicknesses less than 1000 nm, which is mainly attributed to the compressive stresses introduced by the energetic ion bombardment at the early deposition stage. These findings provide new mechanistic insights into thickness-dependent stress and property modulation, offering a reference for tailoring high-performance Cu films through process optimization.
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Liao et al. (2025) studied this question.
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