Optimization of Ga content gradient in Cu(In,Ga)Se<sub>2</sub> solar cells through machine learning and device simulation

Abstract

Cu(In,Ga)Se<sub>2</sub> (CIGS) solar cell is a kind of highly efficient thin film solar cell, for which Ga ratio (Ga/(Ga+In), GGI) gradient engineering is an efficient approach to achieving high open circuit voltage under no short circuit current loss. In this work, we firstly evaluate the room and the strategies for improving the device performance of the CIGS solar cells based on the comparison among their theoretical efficiency limits. Then we investigate the different schemes of “V” type GGI gradient and their effects on device performance through machine learning and device simulation. Based on these studies, we optimize the scheme of “V” type GGI gradient and obtain a high efficiency of 26% from device simulation. The carrier kinetics for the effect of modifying GGI gradient on device performance are analyzed. This work provides an approach to optimizing the GGI gradient to obtain highly efficient CIGS solar cells, which is referable for experimental optimization.