Group PhD student Ruqaiya Al-Abri has a new paper accepted in the Wiley journal, Small. This collaborative work was carried out with semiconductor growth experts Sudhakar Sivakumar and Martin Magnusson of the University of Lund (Sweden) and X-ray photoelectron spectroscopy specialists Conor Byrne and Alex Walton (University of Manchester Chemistry).
In her new work, Ruqaiya uses high-throughput, automated experiments to measure thousands of single nanostuctures - known as nanowires - of a typical semiconductor material, zinc-doped gallium arsenide. Each nanowire was grown in a process known as aerotaxy, developed at the University of Lund, where they are grown in free-space through a seeded approach (much as how rain droplets form around dust particles). While this process is hugely scalable for industrial use, it suffers from being hard to control unformity at the single-wire level.
Rather than reject this inhomogeneity, Ruqaiya has instead used this inherent variability in nanowire geometry (size, shape) and the concentration of zinc atoms (doping) to understand the underlying physical processes in this material. She used a technique known as Bayesian modelling, often more commonly used in astrophysics and data-science, to improve a model of electronic behaviour. This in turn was used to set 11,000 nanowires in order and create a 'video' of emission as a function of time - much as Eadweard Muybridge did with his famous Horse in Motion - with a time resolution of less than 0.000,000,000,000,100 seconds (100 femtoseconds)!
This methodology can be generally applied to understand dynamics in new and emerging materials, to help us rigerously understand materials in the presence of significant non-uniformity, and to assess yield and performance in large populations. It forms part of the "Big-data for Nano-Electronics" research project.
Publication: Al-Abri, Al Amairi, Church, Byrne, Sivakumar, Walton, Magnusson and Parkinson, "Sub-Picosecond Carrier Dynamics Explored using Automated High-Throughput Studies of Doping Inhomogeneity within a Bayesian Framework", Wiley Small, 2300053 (2023), DOI: 10.1002/smll.202300053