When one considers the limited fuel resources of energy, the importance of photovoltaic systems in the future energy provision becomes obvious. The limited conversion efficiency of these systems however severely limits their use currently. During the last two decades, there have been lots of studies to enhance the efficiency of photovoltaic systems. The efficiency of such systems is highly dependent on controling their microstructure within strict limits. Two important factors affecting this microstructure are the surface roughness and porosity of the produced thin film solar cells. Due to the lack of accurate sensors that can timely provide microstructure information, model based approaches to control the quality of these photovoltaic systems have been pursued. The models that predict film microstructure however, pose significant challenges both from an analysis and control point of view. Such difficulties are attributed in part to the unavailability of closed form models to describe the process evolution at molecular-level detail and their computationally intensive nature that prevents their real time implementation. To circumvent the mentioned limitations, one of the previous approaches includes identified stochastic partial differential equation models to design the controller. This approach however assumes specific structure to the nonlinear stochastic terms. Another approach relied on the off-line and subsequently the on-line identification of bilinear models for the process, which are then used for the controller synthesis. The objective of the present work is to develop a model predictive control algorithm to simultaneously regulate the surface roughness and porosity of thin film solar cell to obtain higher efficiency. To simulate the evolution of thin film growth, Kinetic Monte Carlo simulation is utilized. Two processes in this simulation include deposition with considering shadowing effect and migration of the deposited particles. For achieving the closed loop model of the simulated system, fuzzy system identification is utilized. The proposed method explicitly accounts for the presence of stochastic terms in the process and the issue of noise in the measurements. The kalman filter is applied to the model to reach to modeling results with more accuracy. The developed model is then employed with a model predictive framework to drive the process at the desired process objective. The simulation results demonstrate the effectiveness of the nonlinear model predictive controller designed to regulate the surface roughness and porosity of the thin film solar cell to achieve higher efficiency.