Defect-smart AlN: A Bayesian route to quantum functionality

Research question
Room-temperature (RT) quantum operation is essential for scalable, energy-efficient quantum technologies but remains challenging due to reliance on cryogenic systems. Ultra-wide-bandgap aluminum nitride (AlN) is a promising platform, offering stable quantum defects at RT and compatibility with industrial processing. The key challenge is precise control of doping and defect formation during scalable MOCVD growth.

This project develops a reproducible framework for deterministic defect engineering in AlN using physics-informed Bayesian optimization of growth parameters. By synthesizing and analyzing multiple samples, we will map defect-property landscapes and gain general insights into growth mechanisms. The resulting open-source optimization tools will accelerate development across platforms.

Sustainability aspects

AlN enables sustainable quantum technologies by eliminating the need for energy-intensive cryogenic cooling and helium consumption, reducing energy use by up to 100–1000×. Aluminum is abundant, recyclable, and widely available, supporting resource efficiency. MOCVD growth further enhances sustainability through high precursor utilization and scalable wafer processing. Compared to alternatives like diamond or SiC, AlN offers a lower environmental footprint.