Big-data for nano-electronics (Renewal)

Automated, data-driven, and high-throughput experimentation is already revolutionising materials exploration and optimization. While great strides have been made in using this approach to optimize bulk properties of materials, functional nanomaterials remain poorly understood due to the complex and often non-linear relationship between material quality, geometry, and performance. In the first part of my fellowship, I have developed and demonstrated a unique experimental and statistical methodology to study individual nanomaterial performance at huge scale, with tens of thousands to millions of measurements. This has provided unique insight, robust statistical evidence, and industrially useful yield analysis.

In the renewal period I will lead a world-class team to tackle urgent challenges in nanotechnology, namely scale-up for quantum photonic technologies, and ultra-high-throughput for novel materials. My program will draw on the expertise and capability of 10 international academic and industrial partners to maximise the impact of the research.