Miniaturized infrared spectrometer based on the tunable graphene plasmonic filter

Area of Science:

• Optoelectronics
• Nanotechnology
• Spectroscopy

Background:

• Miniaturizing conventional spectrometers faces challenges in balancing size, cost, signal-to-noise ratio, and spectral resolution.
• Existing spectrometers are often bulky, limiting their application in portable devices.

Purpose of the Study:

• To propose a novel miniaturized infrared spectrometer.
• To investigate the performance factors influencing spectral reconstruction.
• To develop an advanced decoding algorithm for improved spectral recovery.

Main Methods:

• Integration of tunable graphene plasmonic filters with infrared detectors.
• Dynamic modulation of light via graphene Fermi energy tuning.
• Spectrum reconstruction using decoding algorithms like ridge regression and neural networks.

Main Results:

• Graphene carrier mobility and signal-to-noise ratio are critical for spectral resolution and precision.
• A hybrid decoding algorithm combining ridge regression and neural networks outperforms individual methods.
• Sub-hundred nanometer spectral resolution achieved across the 8–14 µm wavelength range.

Conclusions:

• The proposed microchip-scale spectrometer offers a viable solution for portable infrared spectral imaging.
• The device leverages tunable graphene plasmonic filters for dynamic spectral modulation.
• Advanced decoding algorithms are essential for high-performance spectral reconstruction in miniaturized systems.