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9 March 2020 Smart on-chip Fourier-transform spectrometers harnessing machine learning algorithms (Conference Presentation)
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Abstract
Miniaturized silicon photonics spectrometers have great potential for mass market applications like medicine and hazard detection. However, the performance of state-of-the-art silicon spectrometers is limited by fabrication imperfections and temperature variations. In this work, we present a fundamentally new strategy that combines machine learning algorithms and on-chip spatial heterodyne Fourier-transform spectroscopy to identify specific absorption features operated under a wide range of temperatures in the presence of fabrication imperfections. We experimentally show differentiation of four different input spectra with unknown temperature variations as large as 10 °C. This is about 100x increase in operational range, compared to state-of-the-art retrieval techniques.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alaine Herrero-Bermello, Jiangfeng Li, Mohammad Khazaei, Yuri Grinberg, Aitor Velasco, Martin Vachon, Pavel Cheben, Lina Stankovic, Vladimir Stankovic, Dan-Xia Xu, Jens Schmid, and Carlos A. Alonso-Ramos "Smart on-chip Fourier-transform spectrometers harnessing machine learning algorithms (Conference Presentation)", Proc. SPIE 11284, Smart Photonic and Optoelectronic Integrated Circuits XXII, 112841W (9 March 2020); https://doi.org/10.1117/12.2546253
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