Melika Dabiri

Major: Computer Science
Mentors: Rachel Schoeppner, Professor Ania Jayich

Exploring Diamond Growth for Quantum Applications: A Data-Driven Approach

Diamond is an attractive material for quantum applications because it plays host to many defects that can form qubits. Of these defects, nitrogen-vacancy (NV) centers are of particular significance due to their applications in quantum sensing and quantum simulations. The ability to controllably engineer NV’s and characterize their properties, such as coherence, density, and depth confinement through the process of diamond growth is an outstanding materials challenge. As such, data analysis of current growth methods is integral to distinguishing what growth techniques yield higher success rates.

In this project, we take a data-driven approach to explore the large parameter space of diamond grown using the process of Plasma Enhanced Chemical Vapor Deposition (PECVD). Secondary Ion Mass Spectrometry (SIMS) is used to assess the composition of grown samples. Together, these processes produce data quantifying the growth results. This data is then organized using the Quantum Data Hub (QDH), a platform created for the storage and analysis of quantum research data. The QDH stores, curates, and manages experimental and theoretical scientific data and offers a portable analysis environment in order to aid the data-driven discovery and development of new methods and materials. Through the help of QDH as well as other data-driven approaches, we aim to understand the process of diamond growth in terms of its parameters - temperature, gas flow rate, doping time, growth time, miscut angle, etc - and optimize our methods for forming reproducible and reliable qubit systems.