Machine Learning Driven Self-correcting Autonomous Metrology Systems (SAMS)

The NIST-on-a-chip program has led to the development of a range of disruptive measurement solutions that leverage quantum and (nano)photonic tools to enable traceable measurements that either eliminate or greatly diminish the need for frequent calibrations. However, a successful transition of these technologies from lab to marketplace will depend on a range complex variables including the user-community’s ability to frictionlessly assimilate these technologies into their workflow.

To democratize advanced metrology technologies and make them accessible to all users regardless of expertise, the collaborative effort is focused on the development of machine learning-driven autonomous metrology research systems. The goal is to remove the “PhD-in-the-loop” and accelerate the development of self-correcting quantum and photonic sensor networks. To accomplish this goal our approach relies on combining machine learning with machine-controlled measurement tools for closed loop experiment design, execution, and analysis, where experiment design is guided by active learning, Bayesian optimization, and similar methods. A key challenge is the integration of prior knowledge into the data analysis, including both device physics and material properties.

We are primarily interested in quantum (NV diamond) and photonic sensor networks for thermodynamic metrology (temperature, pressure, and humidity). Team has demonstrated the use of machine learning models to compensate for long-term drift in Fiber Bragg grating based temperature sensors and is currently working on machine-controlled NV diamond spectrometer for pressure, force and dosimetry measurements.

Opportunities

If you are interested in joining our team as a post-doc, guest researcher, collaborator, or student volunteer send us an email.