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Physical Measurement Laboratory

Applied Physics Division

Projects/Programs

Displaying 51 - 58 of 58
Fig. 1. Die containing 285 superparamagnetic tunnel junctions.

Spintronics for Neuromorphic Computing

Ongoing
Magnetic tunnel junctions (see Fig. 1) consist of two thin films of ferromagnetic material separated by a few atomic layers of an insulating material. The insulator is so thin that electrons can tunnel quantum mechanically through it. The rate at which the electrons tunnel is affected by the

Subspace-Informed Deep Learning Solutions for Nanoscale Microscopy

Ongoing
Cutting-edge nanoscale microscopy techniques such as near-field scanning microwave microscopy (NSMM) and atom probe tomography (APT) enable a multi-dimensional virtual representation of an artifact to be constructed from a set of experimental measurements. These imaging techniques, which are
solar simulator graph

Super-Continuum Laser Solar Simulator

Completed
The design of the super-continuum solar simulator is shown below. Light is produced from 450 nm to 2400 nm in sub-nanosecond pulses at a repetition rate of 80 MHz. The total irradiance is sufficient to illuminate a 1 cm 2 cell at more than 1-sun. Initial tests have confirmed that solar cells respond
Passive millimeter-wave image of concealed contraband (handgun and ceramic knife).

Terahertz Imaging and Sources

Ongoing
Over the past several years, we have focused on bolometric detection of THz radiation. Our initial work on cryogenic detectors has been extended to antenna-coupled bolometers operating at room temperature. These detectors provide a rugged, low-cost alternative for homeland security applications; e.g

THz Detection of Corrosion on Rebar Embedded in Concrete

Ongoing
We developed a new technology to detect and quantify the extent of corrosion in embedded rebar using Terahertz (THz) and microwave spectroscopies. This new method exploits the likelihood that a number of iron corrosion products are antiferromagnetic and absorb THz and microwave radiation. In this

Training and optimization of hardware neural networks

Ongoing
The goal of this project is to develop a general method that can train many different types of neural networks, and to demonstrate and evaluate their performance on new emerging hardware. We aim to develop and demonstrate training on diverse hardware platforms, and in the presence of realistic noise