IMPROVING THE SENSITIVITY AND ANALYTICAL POWER OF ELECTRICALLY DETECTED MAGNETIC RESONANCE THROUGH HARDWARE AND SOFTWARE
Open Access
- Author:
- Manning, Brian
- Graduate Program:
- Engineering Science and Mechanics
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- May 17, 2021
- Committee Members:
- Michael Lanagan, Major Field Member
Patrick Lenahan, Chair & Dissertation Advisor
Saptarshi Das, Major Field Member
Jing Yang, Outside Unit & Field Member
Albert Segall, Program Head/Chair - Keywords:
- Magnetic Resonance
EDMR
Electrically Detected
Hardware
Software
EPR
Resonance - Abstract:
- Magnetic resonance techniques offer unparalleled analytical power in the identification of paramagnetic defect centers in semiconductors and insulators. The preliminary technique, electron paramagnetic resonance (EPR), has a typical sensitivity of about 10 billion defects per Gauss of linewidth. The sensitivity of EPR can be improved via electrical detection, known as electrically detected magnetic resonance (EDMR). EDMR is the most sensitive analytical technique for identifying the chemical and physical nature of defects and has proven to be invaluable in elucidating atomic-scale imperfections in nanoscale solid-state semiconducting and insulating materials and devices. Hardware and software improvements can be used in conjunction with electrical detection to further improve the sensitivity of magnetic resonance measurements and develop new techniques. This research includes the development and a demonstration of several magnetic resonance methods: electrically detected electron-nuclear double resonance (EDENDOR), ultra-low field frequency swept EDMR, and electrically detected rapid-scan (EDRS). The importance and application of each technique is also highlighted. In addition to this, software-based filtering techniques have been developed to improve the sensitivity of the magnetic resonance measurements. This work introduces an improved upper-diagonal (UD) decomposition recursive least-squares (RLS) algorithm with an exponential sliding window (UD-SWRLS) for use in adaptive signal averaging (ASA). We also introduce a real-time fast pseudomodulation technique for use in rapid-scan spectroscopy. We believe that these new techniques and methods could serve as a vital tool for the future of material and device research.