New Project: Big-Data for nano-electronics

Patrick has been awarded a 4-year research fellowship from UKRI for a project on “big-data for nano-electronics”. This Future Leaders Fellowship will enable Patrick to focus on building a research group to develop a new methodology for accelerating the study of functional nanotechnology.

Project Summary

The modern world runs on nanotechnology; we are connected by a fibre-network using nanostructured lasers, and use computers and phones made of nanometre scale transistors. The next generation of nanotechnology promises to incorporate multiple functionalities into single nanomaterial elements; this is “functional nanotechnology”. Here, the size of the material itself provides functionality – for instance for sensing, computing, or interacting with light.  The most powerful and scalable approaches to making these structures use bottom-up or “self-assembled” methods; however, as this production technique emerges from the laboratory and into industry, issues such as yield, heterogeneity, and functional parameter spread have arisen.

Functional performance in these nanomaterials is determined by geometry. As such, variations in size or composition affect performance in complex ways. In this project, I will combine high-speed and high-throughput techniques to measure the shape, composition and performance of hundreds of thousands of functional nanoparticles from each production run. By combining this big data with statistical analytics, I will create a new methodology to understand and then optimize cutting-edge functional nanomaterials, working with academic partners in Cambridge, University College London, Strathclyde, Lund (Sweden) and the Australian National University, and industrial partners including AIXTRON and Nanoco.

The ultimate goal of this project is to enable demonstration and scale-up of transformative devices based on novel nanotechnology, for sensing, computing, telecommunication and quantum technology.

New Paper – Optical Study of p-Doping in GaAs Nanowires for Low-Threshold and High-Yield Lasing

Our new work on large-scale statistical spectroscopy to optimize nanowire lasers is published today in Nano Letters. In this work, PhD student Arturo studied thousands of nanowires to identify the lowest threshold nanowire, as well as to model emission to identify the primary sources of non-radiative emission.

By quickly sorting nanowires by doping and length, he was able to demostrate sub-sets with over 90% yield and class-leading thresholds, pointing the way towards electrically injected nanolasers.

Congratulations Arturo!

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(Left) Far-field emission from a record breaking nanowire laser, emitting at room temperature and low exciation levels. (Right) Doping vs Quantum efficiency, showing two key limiting behaviours.

Reference: “Optical Study of p-Doping in GaAs Nanowires for Low-Threshold and High-Yield Lasing”, Juan Alanis et al., Nano Letters, ASAP 2018, DOI: 10.1021/acs.nanolett.8b04048

New Publication – Large-scale statistics for threshold optimization of optically pumped nanowire lasers

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Arturo’s recent work has been published in Nano Letters [at  http://pubs.acs.org/doi/10.1021/acs.nanolett.7b01725 ]. This work was done in collaboration with the group of Professor Chennupati Jagadish at the Australian National University.

In this paper, we describe how automated and high-speed spectroscopy can be applied to nanomaterials to collect sufficient data to apply statistical techniques. Using this approach, we are able to use the inherent spread in parameter space which arises from bottom-up growth techniques to provide pointers to future optimization.

Importantly, we also reveal that for a specific growth of semiconductor nanolasers, we are able to achieve yields of over 50% and lowest pump thresholds of 42μJ/cm2, a record for room-temperature near infra-red nanolasers.

Congratulations Arturo!