Skip to main navigation
Skip to search
Skip to main content
The Hebrew University of Jerusalem Home
Approve / Request updates on publications
Link opens in a new tab
Search content at The Hebrew University of Jerusalem
Home
Profiles
Research units
Research output
Prizes
Chip-scale atomic wave-meter enabled by machine learning
Eitan Edrei
, Niv Cohen
, Elam Gerstel
, Shani Gamzu-Letova
, Noa Mazurski
,
Uriel Levy
*
*
Corresponding author for this work
The Institute of Applied Physics
Research output
:
Contribution to journal
›
Article
›
peer-review
12
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Chip-scale atomic wave-meter enabled by machine learning'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Machine Learning
100%
Chip-scale
100%
Spectrometer
100%
Environmental Fluctuations
66%
Cost-effectiveness
33%
On chip
33%
Calibration Process
33%
Interference Pattern
33%
Silicon Photonics
33%
Optical Waves
33%
Atomic Vapor
33%
Fully Integrated
33%
Machine Learning Classification
33%
External Calibration
33%
Photonic chip
33%
High Spectral Resolution
33%
Random Interference
33%
On-chip Devices
33%
Accurate Calibration
33%
Integrated Spectrometers
33%
Reduced Footprint
33%
Random Waves
33%
Engineering
Learning System
100%
Photonics
50%
Interference Pattern
50%
Silicon Photonics
50%
External Calibration
50%
High Spectral Resolution
50%
Classification Algorithm
50%
Chip Device
50%
Optical Wave
50%
Random Wave
50%
Calibration Process
50%
Earth and Planetary Sciences
Machine Learning
100%
Spectrometer
100%
Photonics
50%
Spectral Resolution
25%
Random Wave
25%
Material Science
Silicon
100%