Goals:

Introduction: 

Currently, the semiconductor industry is moving towards using copper interconnect lines due to their decreased resistivity and increased resistance to electromigration. The crystallographic texture of Cu thin films has been examined and was found to have a more complex behavior than that of Al-Cu. (111), (100), and (110) oriented grains are frequently observed. Experiments by Ryu et. al. have shown that the electromigration lifetime of (111) textured Cu films is about 4 times longer than that of (100) textured Cu films. Therefore, the semiconductor industry is interested in methods to control interconnect microstructures. It would be extremely useful to be able to predict the microstructure (grain size, grain shape, grain orientation, texture, voids, dislocation density, and roughness) of polycrystalline thin films as a function of their deposition conditions (temperature, flux distribution, deposition method, substrate geometry, materials). Computer models capable of predicting the final film microstructures would be very useful in helping process engineers to more efficiently optimize processing conditions, provided that the models are reliable, fast, and easy to use. We developed an atomic Kinetic Lattice Monte Carlo (KLMC) model which describes deposition, surface self-diffusion (including single adatom, dimer and ledge adatom diffusion), nucleation, and film growth on fcc metal substrates. The inputs of the KLMC model, namely the activation energies for diffusion, are calculated by using the simple embedded-atom method (EAM). Using this model, we determine the relative growth rates of Cu (100), (110) and (111) facets as a function of substrate temperature, deposition rate and facet size and thus predict some aspects of microstructural evolution, namely grain orientations and texturing, during PVD Cu thin film growth.

Participants:

Methods: 

We use three levels of Simulation:

Results, Publications and Presentations:

Acknowledgements (funding and computational resources):