The use of genetic algorithm for the design and optimization of advanced multi-junction solar cells

Multijunction solar cells consisting of series-stacked p-n junction layers offer a significant improvement in efficiency over conventional solar cells by generating power over a larger spectrum of sunlight. The design of multijunction solar cells is complicated by both the desire to have maximally efficient junction layers and the need to match the current produced in each junction layer under optimal load conditions. The ATLAS device simulator from Silvaco International has been shown in exclusive research (Michael and Green, 2003) at the Naval Postgraduate School to have the capability to simulate multifunction solar cells. This simulation tool has the ability to extract electrical characteristics from a solar cell and bypass the costly "build-and-test" design cycle. This paper proposes a method for using ATLAS data to optimize the power output of individual junction layers of a four junction InGaP/GaAs/InGaNAs/Ge solar cell and to construct these junction layers into a current-matched, optimum power multifunction solar cell. Individual junction layer optimization was accomplished through the use of a genetic search algorithm implemented in Matlab. The final multijunction cell current matching was performed using an iterative optimization routine also implemented in Matlab.