Atomistic insights into adhesion characteristics of tungsten on titanium nitride using steered molecular dynamics with machine learning interatomic potential.

As transistor integration accelerates and miniaturization progresses, improving the interfacial adhesion characteristics of complex metal interconnect has become a major issue in ensuring semiconductor device reliability. Therefore, it is becoming increasingly important to interpret the adhesive properties of metal interconnects at the atomic level, predict their adhesive strength and failure mode, and develop computational methods that can be universally applied regardless of interface properties. In this study, we propose a method for theoretically understanding adhesion characteristics through steering molecular dynamics simulations based on machine learning interatomic potentials. We utilized this method to investigate the adhesion characteristics of tungsten deposited on titanium nitride barrier metal (W/TiN) as a representative metal interconnect structure in devices. Pulling tests that pull two materials apart and sliding tests that pull them against each other in a shear direction were implemented to investigate the failure mode and adhesive strength depending on TiN facet orientation. We found that the W/TiN interface showed an adhesive failure where they separate from each other when tested with pulling force on Ti-rich (111) or (001) facets while cohesive failures occurred where W itself was destroyed on N-rich (111) facet. The adhesion strength was defined as the maximum force causing failure during the pulling test for consistent interpretation and the strengths of tungsten were predicted to be strongest when deposited onto N-rich (111) facet while weakest on Ti-rich (111) facet.