Optimization by a genetic algorithm of the light-extraction efficiency of a GaN light-emitting diode

Abstract

Light extraction from light-emitting materials is fundamentally limited by internal reflections due to the high dielectric-constant contrast between the material that produces the light and the emergent medium. These internal reflections can however be reduced significantly by a well-designed texturation of the surface of the emitting material. We used a genetic algorithm to determine optimal geometrical and material parameters for this texturation, the objective being to maximize the extraction of light of a GaN light-emitting diode (LED). This study, which was restricted to two-dimensional texturations, shows that symmetric triangles actually correspond to the optimal shape. The dielectric constant of the material used for this texturation should ideally have the same dielectric constant as the GaN. The optimal texturation determined in this work leads to a light-extraction efficiency of 11.1%, which improves significantly the value of 3.7% obtained with a flat surface and the value of 5.7% achieved in previous work.

A fair comparison between ultrathin crystalline-silicon solar cells with either periodic or correlated disorder inverted pyramid textures

“…Four parameters have to be optimized: the period P UC of the unit cell which corresponds to the pitch of the pattern, the filling fraction f UC of the holes at the top c-Si surface, and the thicknesses of the ARC and BR layers, respectively t ARC and t BR . The optimization of these parameters was performed by a Genetic Algorithm (GA) method [46][47][48][49] thanks to a home-made code (see [50] for details). The idea consists in working with a population of 100 individuals, each individual being representative of a given set of parameters which are actually represented by a string of binary digits (DNA).…”

Opal-Like Photonic Structuring of Perovskite Solar Cells Using a Genetic Algorithm Approach

“…Off the strength of those results, the main goal of this study is to further optimize the rational structure of PSCs to be closer to experimental reality by using a genetic algorithm (GA). The GA finds a global optimum or a high-quality solution within a multi-dimensional parameter space for a dedicated problem, such as light absorption [34][35][36]. This procedure enables the smart exploration of a larger range of parameters and is particularly relevant for the absorption inside the PSCs (e.g., the thicknesses of the different layers or the radius of the spheres).…”

Genetic Algorithms: An Evolutionary Approach to Optical Engineering

“…In previous work [11,[17][18], we used genetic algorithms to address optical engineering problems. In Ref.…”

“…In Ref. [11], the objective was to maximize the light-extraction efficiency of a GaN Light-Emitting Diode (LED). The genetic algorithm had to determine the geometrical and material characteristics of a periodic texturation for the surface of the GaN, with the objective to maximize the percentage of light produced within the GaN that is actually transmitted into air.…”

“…We replace the worst n rand individuals of the population by random individuals for the next generation (n rand = 0.1×n pop × (1-p), with p = |s-0.5|/0.5 a progress indicator and s the genetic similarity). The genetic similarity s corresponds to the fraction of the bits in the population whose value is identical to the best individual [9,11]. We then select N = n pop -n rand individuals in the remaining part of the population by a rank-based roulette wheel selection (the best individuals have more chance to be selected; the selection probabilities decrease linearly with the ranking) [2].…”