Machine Learning for Improved Current Density Reconstruction from 2D Vector Magnetic Images
arXiv:2407.14553 · doi:10.1103/PhysRevApplied.23.034035
Abstract
The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high spatial frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional (2D) images of vector magnetic fields acquired by a quantum diamond microscope (QDM) utilizing a surface layer of Nitrogen Vacancy (NV) centers in diamond. Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower SNR data, reducing the data collection time by a factor of about 400 and permitting reconstructions of weaker and three-dimensional current sources.
17 pages, 10 figures. Includes Supplemental Information
References in corpus (8)
- Magnetic Field Fingerprinting of Integrated Circuit Activity with a Quantum Diamond Microscope
- Improved current density and magnetisation reconstruction through vector magnetic field measurements
- Nanoscale magnetism and magnetic phase transitions in atomically thin CrSBr
- Imaging of sub-A currents in bilayer graphene using a scanning diamond magnetometer
- Magnetic imaging of superconducting qubit devices with scanning SQUID-on-tip
- Imaging current paths in silicon photovoltaic devices with a quantum diamond microscope
- Vector Magnetic Current Imaging of an 8 nm Process Node Chip and 3D Current Distributions Using the Quantum Diamond Microscope
- Helical Luttinger liquid on the edge of a 2-dimensional topological antiferromagnet