Big datasets for nanomaterials; High-throughput imaging and spectroscopy of nano-opto-electronics

Optoelectronic nanomaterials often derive their functional properties from their size; for instance, we can obtain new meta-stable materials over reduced dimensionality, strong light-matter interaction from wavelength-scale materials, or enhanced environmental sensing ability due to a large surface-area to volume ratio. However, many important nanomaterials are produced using "bottom-up" or self-directed techniques, which can lead to heterogeneity in geometry and material quality. To enable future optoelectronic devices and circuits, we must understand and reduce this heterogeneity.

The objective of this project is for the student to develop tools and techniques for high-throughput characterization in order to measure and optimize such nanomaterials with single-element sensitivity. In the process, the student will also develop a dataset of imagery and spectroscopy measurements which enable a second objective of this project. A sufficiently large dataset with multi-parameter heterogeneity can be used to understand the interplay between geometry, material quality, and functional performance. The student will develop analytical approaches to mine this dataset using statistical and machine learning approaches, as a new route to exploit the disorder in nanomaterials as a diverse data set.

The primary outputs of this project are the development of high-throughput techniques, specific understanding of disorder in nanoscale materials (specifically III-V semiconductor and quantum-dot based), the development of a datastore for measurements and the analytical tools to mine this for deeper understanding of growth and application of nanoscale materials.