Physicists from the University of Texas at Dallas and Yale University have developed a quantum sensor that can detect all the fundamental properties of a light wave.
A new concept based on quantum geometry was demonstrated in the research published in the journal Nature.
When you want to understand a wave of light, you have to use different instruments to gather information. Dr. Fan Zhang is an associate professor of physics in the School of Natural Sciences and Mathematics and is one of the authors of the study.
A single device that is just a tiny and thin chip can determine all these properties in a very short time.
The device is based on a novel family of two-dimensional materials. The review article was published in Nature.
There are periodic structures in the 2D materials. A pattern with an order of magnitude larger periodicity can be formed if two layers of the same material are sandwiched together. The electronic properties of the metamaterial are vastly different from those of a single layer alone or two naturally aligned layers.
Two layers of naturally occurring bilayer graphene are used for a total of four atomic layers in the device thatZhang and his colleagues chose to demonstrate.
Patrick Cheung, a physics PhD student at UT Dallas, is one of the co-lead authors of the study. If you want to produce a current in a material, you have to use a bias. There is no bias in this case; we shine a light on the metamaterial, and the light causes a current to be generated. The magnitude and phase of the photovoltage are dependent on a number of factors.
By tuning the metamaterial, a light wave can create a 2D map that is unique to that wave and can be inferred from the wave's properties.
Two metal plates were placed on top and underneath the moiré metamaterial by the researchers. The researchers were able to tune the quantum geometric properties of the material to make it look like afingerprint.
The team used a neural network to decipher fingerprints.
"We start with light for which we know the intensity, wavelength and polarization, shine it through the device and tune it in different ways to generate different fingerprints." After training the neural network with a data set of about 10,000 examples, the network is able to recognize the fingerprints. The light can be characterized once it learns enough.
The Texas Advanced Computing Center is located on the UT Austin campus.
Patrick has been good at pencil-and-paper analytical calculations, but now he is an expert in using a supercomputer, which is required for this work. Our job is to find new science. We want our students to know what they are best at. I'm happy that Patrick and I were able to figure it out.
More information: Chun Ning Lau et al, Reproducibility in the fabrication and physics of moiré materials, Nature (2022). DOI: 10.1038/s41586-021-04173-z Journal information: Nature
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