Martha Gallivan
Department of Mechanical Engineering
Control and Dynamical Systems
California Institute of Technology
Modeling and Control of Thin Film Deposition
Thin film deposition is an industrially-important process that is critical in the manufacture of integrated circuits and MEMS devices. As the size of integrated circuits shrinks and material complexity grows, the empirical approach to process design is becoming increasingly difficult. A systematic, model-based approach for the intelligent design of time-varying process parameters may provide an alternative to the current trial-and-error method. Viewing thin film deposition as an input-output system, with process parameters as inputs and film properties as outputs, methods developed within control theory could be used to design closed-loop controllers and to compute optimal input trajectories. However, a major challenge in applying the methods of control theory to a thin film deposition process has been the formulation of models compatible with established control techniques. We consider a lattice model of thin film deposition, which is the basis for stochastic, atomic-scale Monte Carlo simulations of film growth. Instead of analyzing these rule-based dynamics, we focus on the underlying probabilistic ``master equation,'' a high-order ordinary differ- ential equation. A comparison is made between the film properties obtainable with constant process parameters and with parameters that are periodic in time. In the limit when the input is fast, new constant ``effective'' inputs are created that enable film properties unattainable with constant process parameters. The effect of periodic process parameters is also investigated in experiment for a molecular beam epitaxy growth process; initial data will be presented.