Big-data for nano-electronics

Demand for high density, integrated electronics has become a defining feature of modern technology. At its ultimate limit, nanotechnology can enable low-cost and highly scalable sensors, computing elements, and lighting. The industrial benefits are clear - in particular bottom-up fabrication allows for high-level functionality and huge production scale at low cost. As this production technique emerges from the laboratory and into industry, issues such as yield, heterogeneity, and functional parameter spread have emerged as a critical aspect for efficacy to be established in advanced nanomaterials.

To date, no framework exists for studying inhomogeneity in functional nano-electronics. I will combine highly-scaled measurements with cutting-edge data techniques to establish a gold-standard methodology for functional nanotechnology development, enabling industrial take-up. This will build on experimental approaches that I have recently demonstrated, including machine-vision identification of nanomaterials and automated electronic and optical spectroscopy, alongside computational approaches for rapid and technique-independent re-identification of single nanoparticles. I will implement analytics which draw on existing population-study methods such as linear and multivariate correlation; a specific goal of this project is to translate advanced techniques from diverse fields including astrophysics and health research, and in particular apply Bayesian analysis for model identification and augmented intelligence (including machine learning methods) where appropriate.

These methodologies will be developed to study cutting edge challenges in functional nanomaterials; starting with the development of lasers for chip-to-chip communication, and the production of an industrially relevant capability for single-particle nanotechnology characterisation. By bringing this methodology together with pick-and-place capability through project partners, this project will enable demonstration of extremely low-yield yet transformative devices based on novel nanotechnology, for sensing, telecommunication or quantum devices.

In defining a methodology for studying, understanding and optimizing nanoelectronic technology for homogeneity, this research will have impact across academic, industrial and governmental domains. For industry, the development of a tool and framework for studying functional inhomogeneity is a critical step accelerating the adoption of bottom-up produced nanotechnology. For academia, new production methods and novel materials will be rapidly screened for homogeneity - and furthermore, the best-in-growth nanoparticles can be identified for further research or demonstration of revolutionary technologies. For government laboratories, this method can be used to help define functional parameter requirements and homogeneity measures in this emerging field.

By partnering with local and international collaborators across academia and industry, this research will both lead the development of this important methodology within this research community, while having the potential to be responsive to emerging requirements for entirely novel functional nanoelectronics.

In the broader community, the tool will be made available for use through the established Royce Institute for Advanced Materials mechanism, and analytic code and datasets will be open-sourced for wide-scale uptake. I will present the findings of this research at international conferences to provide the widest possible exposure for the proposed methodology.