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Aligning Superconducting Transition-Edge Sensors by Reflected Wave Intensity Measurement.

Authors :
Ma, Pei-Sa
Zhang, Hong-Fan
Zhou, Xingxiang
Source :
Sensors (14248220). Apr2023, Vol. 23 Issue 7, p3495. 18p.
Publication Year :
2023

Abstract

It is critical to accurately align a quantum photon detector such as a superconducting transition-edge sensor (TES) to an optical fiber in order to optimize its detection efficiency. Conventionally, such alignment requires advanced infrared imaging equipment or sophisticated microfabrication. We introduce a novel technique based on the simple idea of reflected wave intensity measurement which allows to determine the boundary of the sensor and align it accurately with the fiber. By routing a light wave through an optical fiber for normal incidence on the surface of the sensor chip, and separating the reflected wave coupled back into the fiber from the input signal with a circulator, we can observe the variation in the reflected wave intensity when the beam spot of the fiber crosses the boundary between the sensor and substrate that have different reflectivity, and adjust the position of the fiber such that its output falls on the sensor. We evaluate quantitatively the precision of our alignment method, as well as the conditions that must be met to avoid photon loss caused by light beam divergence. After demonstrating the working principle of our scheme and verifying the alignment result experimentally, we employ it for efficient input signal coupling to a TES device, which is used for photon-number-resolving measurement to showcase the successful application of our alignment method in practice. Relying on only ordinary and widely used optical elements that are easy to operate and low in cost, our solution is much less demanding than conventional methods. Dramatically easier to implement and not restricted by the detection mechanism of the sensor, it is accessible to a much broader community. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14248220
Volume :
23
Issue :
7
Database :
Academic Search Index
Journal :
Sensors (14248220)
Publication Type :
Academic Journal
Accession number :
163037640
Full Text :
https://doi.org/10.3390/s23073495