Real time intelligent process control system for thin film solar cell manufacturing

Abstract

This project addresses the problem of lower solar conversion efficiency and waste in the typical solar cell manufacturing process. The work from the proposed development will lead toward developing a system which should be able to increase solar panel conversion efficiency by an additional 12-15% resulting in lower cost panels, increased solar technology adoption, reduced carbon emissions and reduced dependency on foreign oil. All solar cell manufacturing processes today suffer from manufacturing inefficiencies that currently lead to lower product quality and lower conversion efficiency, increased product cost and greater material and energy consumption. This results in slower solar energy adoption and extends the time solar cells will reach grid parity with traditional energy sources. The thin film solar panel manufacturers struggle on a daily basis with the problem of thin film thickness non-uniformity and other parameters variances over the deposited substrates, which significantly degrade their manufacturing yield and quality. Optical monitoring of the thin films during the process of the film deposition is widely perceived as a necessary step towards resolving the non-uniformity and non-homogeneity problem. In order to enable the development of an optical control system for solar cell manufacturing, a new type of low cost optical sensor ismore » needed, able to acquire local information about the panel under deposition and measure its local characteristics, including the light scattering in very close proximity to the surface of the film. This information cannot be obtained by monitoring from outside the deposition chamber (as traditional monitoring systems do) due to the significant signal attenuation and loss of its scattering component before the reflected beam reaches the detector. In addition, it would be too costly to install traditional external in-situ monitoring systems to perform any real-time monitoring over large solar panels, since it would require significant equipment refurbishing needed for installation of multiple separate ellipsometric systems, and development of customized software to control all of them simultaneously. The proposed optical monitoring system comprises AccuStrata’s fiber optics sensors installed inside the thin film deposition equipment, a hardware module of different components (beyond the scope of this project) and our software program with iterative predicting capability able to control material bandgap and surface roughness as films are deposited. Our miniature fiber optics monitoring sensors are installed inside the vacuum chamber compartments in very close proximity where the independent layers are deposited (an option patented by us in 2003). The optical monitoring system measures two of the most important parameters of the photovoltaic thin films during deposition on a moving solar panel - material bandgap and surface roughness. In this program each sensor array consists of two fiber optics sensors monitoring two independent areas of the panel under deposition. Based on the monitored parameters and their change in time and from position to position on the panel, the system is able to provide to the equipment operator immediate information about the thin films as they are deposited. This DoE Supply Chain program is considered the first step towards the development of intelligent optical control system capable of dynamically adjusting the manufacturing process “on-the-fly” in order to achieve better performance. The proposed system will improve the thin film solar cell manufacturing by improving the quality of the individual solar cells and will allow for the manufacturing of more consistent and uniform products resulting in higher solar conversion efficiency and manufacturing yield. It will have a significant impact on the multibillion-dollar thin film solar market. We estimate that the financial impact of these improvements if adopted by only 10% of the industry ($7.7 Billion) would result in about $1.5 Billion in savings by 2015 (at the assumed 20% improvement). This can be achieved by optimizing the manufactured product and process in real time without changing the manufacturing technology.« less