Functional properties of thin film metal oxides depend upon their stoichiometric and structural uniformity. Controlling the film deposition process can help tune the functionality of these films by ensuring the control over chemistry and structure of the films. The high volume manufacturing of functional devices will benefit from the development of reliable control models developed from research efforts in designing robust manufacturing processes. The use of neural networks as computer models to simulate the molecular beam epitaxy (MBE) of iron oxide thin films is presented in this work. Monte Carlo experiments are used to study the sensitivities and significances of process control variables to the stoichiometric performance indicators. Moreover, we also explore the relationship between growth dynamics of iron oxide (Fe2O3, Fe3O4, and mixed FexOy) and magnesium oxide (MgO) thin films. The common metal adsorption controlled growth mechanism of two films with different structural and stoichiometric complexities were observed and the similarities among the trends of analogous stoichiometric indicators at comparable metal arrival rates of the two films are presented. The dependence of undesirable bonding states of iron and magnesium metals with the film thicknesses was also observed in both processes. The commonalities suggest the potential to use of neural network assisted Monte Carlo analysis to link common atomic-level mechanisms to processing variables in one nano-scale system and use them to predict some level of behavior in other nanoscale processes with similar atomic-level mechanisms.
Pulse source injection molecular beam epitaxy and characterization of nano-scale thin GaN layers on Si substrates
