Frederick Wellstood

We describe a magnetic inverse that can extract a 3D current path from a magnetic field image of the path. To evaluate performance, we processed hundreds of simulated images with accurately known parameters and examined a few real images for comparison. The simulated images were built with 1-10 segments that could meander between 1-3 layers. Segments were aligned along the x or y directions, or 45 degrees to x and y. Some images had a small overall rotation. Each image had random noise with a standard deviation of 0.1 nT and some images included random position errors with a standard deviation of 0.1 micron (typical for MAGMA SQUID microscope). Typical path and image parameters were sample-sensor separations z between 0.02 and 1 mm, segment lengths between 0.05 and 2 mm, and current between 0.1 and 2 mA. We find that the vertical and lateral resolution are comparable, proportional to the depth z of the current path, and inversely proportional to the total signal-to-noise ratio (S/N) of the image. For high enough S/N, the vertical and lateral resolution was typically better than z/1000, implying sub-micron vertical and lateral localization of circuit paths out to depths z ~ 1 mm.

Frederick C. Wellstood is currently a MAGMA Fellow at Neocera, a Prof. Emeritus in the Department of Physics, University of Maryland, College Park, Maryland, and an Emeritus Fellow in the Joint Quantum Institute at the University of Maryland and NIST. He received his Ph.D. in Physics in 1988 at the University of California, Berkeley, California, with a thesis titled “Excess Noise in the dc SQUID; 4.2 K to 20 mK”, which was directed by 2026 Nobel Laureate Professor John Clarke. While at Maryland, some of his research has involved developing scanning SQUID microscopes and conducting research on quantum computation using phase qubits.