Deriving the absorption coefficients of lattice mismatched InGaAs using genetic algorithm

Abstract

Lattice-mismatched InGaAs has appeared to be emerging semiconductor materials for sensors and photovoltaic applications. The absorption coefficients of the materials are crucial in designing high-performance semiconductor devices. Nevertheless, the absorption coefficient of lattice-mismatched InGaAs were not comprehensively studied to cater for the 2000–3000 nm applications. This study aims to determine the absorption coefficients of lattice-mismatched In0.73Ga0.27As and In0.83Ga0.17As semiconductor materials through photocurrent measurement which enables the absorption tail information to be extracted. In addition, this work demonstrates the incorporation of an innovative artificial intelligence-based method in solving the absorption coefficient of lattice-mismatched InGaAs, considering the detailed information of the structure design and material parameters. By selecting the best gene for the next iteration, the utilization of Genetic Algorithm has significantly reduced the number of iterations from a maximum of 10 000 to 300. Validation of the algorithm was conducted, showing a good agreement of absorption coefficient result compared to the published work on In0.72Ga0.28As. The absorption coefficient of In0.83Ga0.17As with an extended cutoff wavelength near 2.6 μm is newly reported in this paper. In addition, the extrapolation of the obtained absorption results demonstrates energy gaps of 0.475 eV for In0.73Ga0.27As and 0.55 eV for In0.83Ga0.17As, which are compatible with the reported bandgaps of these materials. The extracted absorption coefficient information can be used in the design of semiconductor devices for emerging technologies such as focal plane array, short wave infrared sensing and thermophotovoltaic.