A comparison between thin film solar cells made from co-evaporated CuIn1-xGaxSe2using a one-stage process versus a three-stage process

Until this day, the most efficient Cu(In,Ga)Se2 thin film solar cells have been prepared using a rather complex growth process often referred to as three-stage or multistage. This family of processes is mainly characterized by a first step deposited with only In, Ga and Se flux to form a first layer. Cu is added in a second step until the film becomes slightly Cu-rich, where-after the film is converted to its final Cu-poor composition by a third stage, again with no or very little addition of Cu. In this paper, a comparison between solar cells prepared with the three-stage process and a one-stage/in-line process with the same composition, thickness, and solar cell stack is made. The one-stage process is easier to be used in an industrial scale and do not have Cu-rich transitions. The samples were analyzed using glow discharge optical emission spectroscopy, scanning electron microscopy, X-ray diffraction, current–voltage-temperature, capacitance-voltage, external quantum efficiency, transmission/reflection, and photoluminescence. It was concluded that in spite of differences in the texturing, morphology and Ga gradient, the electrical performance of the two types of samples is quite similar as demonstrated by the similar J–V behavior, quantum spectral response, and the estimated recombination losses.

Adaptive Penalty and Barrier function based on Fuzzy Logic

Optimization methods have been used in many areas of knowledge, such as Engineering, Statistics, Chemistry, among others, to solve optimization problems. In many cases it is not possible to use derivative methods, due to the characteristics of the problem to be solved and/or its constraints, for example if the involved functions are non-smooth and/or their derivatives are not know. To solve this type of problems a Java based API has been implemented, which includes only derivative-free optimization methods, and that can be used to solve both constrained and unconstrained problems. For solving constrained problems, the classic Penalty and Barrier functions were included in the API. In this paper a new approach to Penalty and Barrier functions, based on Fuzzy Logic, is proposed. Two penalty functions, that impose a progressive penalization to solutions that violate the constraints, are discussed. The implemented functions impose a low penalization when the violation of the constraints is low and a heavy penalty when the violation is high. Numerical results, obtained using twenty-eight test problems, comparing the proposed Fuzzy Logic based functions to six of the classic Penalty and Barrier functions are presented. Considering the achieved results, it can be concluded that the proposed penalty functions besides being very robust also have a very good performance.