Estimation of Indium Composition in GaxIn1-xN/GaN Layers from V-Pit Diameter Measurements

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

This article is not in any list yet, why not save it to one of your lists.
Log in to save this article

Abstract

Threading dislocations in Ga x In 1−x N/GaN heterostructures give rise to the formation of V-pits, which act as effective strain-relief centers by redistributing elastic stress along their inclined facets. The geometry and size of these V-pits therefore directly reflect the strain state of the epilayer and its dependence on indium composition and layer thickness. Building on this physical understanding, we present a practical method to estimate the indium composition (x) in Ga x In 1−x N/GaN layers by measuring the diameter of V-pits from microscopy images and using the layer thickness obtained from growth parameters. V-pits, which are inverted hexagonal pyramidal defects originating at threading dislocations, play a key role in strain relaxation in GaInN/GaN epilayers. Based on theoretical considerations and published experimental data, an empirical relation is developed that links V-pit diameter and layer thickness to indium composition. The relation follows a hyperbolic-type dependence, consistent with the gradual and saturating nature of strain relaxation mediated by V-pit formation. Validation against literature data obtained from X-ray reciprocal space mapping, transmission electron microscopy, and scanning electron microscopy shows good agreement between predicted and measured compositions. Contour and three-dimensional representations derived from the model allow rapid estimation of the indium composition once the V-pit diameter and layer thickness are known, eliminating the need for high-resolution X-ray diffraction measurements. The approach is applicable to layers grown under comparable growth temperatures and substrate dislocation densities to those used for calibration, and offers a fast, non-destructive methodology for process monitoring and optimization in GaInN/GaN -based devices, including LEDs, HEMTs, and solar cells.

Article activity feed