We report multi-component high-entropy materials as extraordinarily robust diffusion barriers and clarify the highly suppressed interdiffusion kinetics in the multi-component materials from structural and thermodynamic perspectives. The failures of six alloy barriers with different numbers of elements, from unitary Ti to senary TiTaCrZrAlRu, against the interdiffusion of Cu and Si were characterized, and experimental results indicated that, with more elements incorporated, the failure temperature of the barriers increased from 550 to 900°C. The activation energy of Cu diffusion through the alloy barriers was determined to increase from 110 to 163 kJ/mole. Mechanistic analyses suggest that, structurally, severe lattice distortion strains and a high packing density caused by different atom sizes, and, thermodynamically, a strengthened cohesion provide a total increase of 55 kJ/mole in the activation energy of substitutional Cu diffusion, and are believed to be the dominant factors of suppressed interdiffusion kinetics through the multi-component barrier materials.

To inhibit rapid Cu diffusion in interconnect structures, an effective diffusion barrier of high thermal stability is strongly demanded. Thus in this study a nitride nanocomposite film of equimolar five-element high-entropy alloy (AlCrTaTiZr)N was developed and deposited by reactive sputtering. Thermal stability of the (AlCrTaTiZr)N film and its barrier performance to the interdiffusion of Si and Cu were investigated under thermal annealing at . The (AlCrTaTiZr)N film, constructed of mixed crystalline and amorphous nanocomposite structure, was found to remain thermally stable at an extremely high temperature of with low electrical resistance. Neither interdiffusion between Si and Cu through the (AlCrTaTiZr)N layer nor formation of any silicides occurred. Severe lattice distortions caused by the addition of multiprincipal elements and the nanocomposite structure of nanocrystallites surrounded by an amorphous matrix without the existence of grain boundaries were expected as the dominant factors for the high thermal stability and superior diffusion resistance of the (AlCrTaTiZr)N film as an effective barrier material.

Small angle scattering techniques have now been routinely used to quantitatively determine the potential of mean force in colloidal suspensions. However the numerical accuracy of data interpretation is often compounded by the approximations adopted by liquid state analytical theories. To circumvent this long standing issue, here we outline a machine learning strategy for determining the effective interaction in the condensed phases of matter using scattering. Via a case study of colloidal suspensions, we show that the effective potential can be probabilistically inferred from the scattering spectra without any restriction imposed by model assumptions. Comparisons to existing parametric approaches demonstrate the superior performance of this method in accuracy, efficiency, and applicability. This method can effectively enable quantification of interaction in highly correlated systems using scattering and diffraction experiments.