Deep-learning algorithms for imperfection-resilient Fourier-transform spectroscopy in silicon

Silicon photonics spectrometers have great potential for applications in medicine and hazard detection. However, silicon spectrometers are very sensitive to fabrication imperfections and environmental conditions. Here, we study the use of deep-learning algorithms to improve tolerance of Fourier-transform spectrometers against fabrication imperfections and temperature variations.