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Chip-scale atomic wave-meter enabled by machine learning.

Eitan Edrei¹, Niv Cohen², Elam Gerstel¹

¹Department of Applied Physics, The Center for Nanoscience and Nanotechnology, The Hebrew University, Jerusalem 91904, Israel.

Science Advances

|April 15, 2022

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This study introduces a novel on-chip random spectrometer (RS) integrating atomic vapor and machine learning. This advancement enhances calibration accuracy and environmental robustness for miniaturized optical instruments.

Area of Science:

Photonics and Spectrometry
Optical Engineering
Machine Learning Applications

Background:

Miniaturized optical wave-meters and spectrometers are crucial for advanced applications.
Random spectrometers (RS) offer high spectral resolution and cost-effectiveness but suffer from environmental sensitivity and require external calibration.
Existing chip-scale RS platforms have reduced footprints but retain susceptibility to environmental fluctuations.

Purpose of the Study:

To develop a robust and accurately calibrated on-chip random spectrometer.
To overcome the environmental sensitivity and calibration limitations of current RS devices.
To integrate atomic vapor with a photonic chip for enhanced spectrometer performance.

Main Methods:

Integration of atomic vapor with a silicon photonic chip.

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Development and application of a machine learning classification algorithm.

Utilizing the correlation between wavelength and random interference patterns for spectral measurement.

Main Results:

Demonstration of a paradigm shift in random spectrometer design.
Achieved accurate calibration and enhanced robustness against environmental fluctuations.
Successful creation of an on-chip random wave-meter device.

Conclusions:

The integration of atomic vapor and machine learning offers a robust solution for on-chip spectrometers.
The developed device addresses key limitations of existing random spectrometers.
This work paves the way for fully integrated spectrometers in silicon photonics.