硅中不完美弹性傅里叶变换光谱的深度学习算法

2021 IEEE 17th International Conference on Group IV Photonics (GFP) Pub Date : 2021-12-01 DOI:10.1109/GFP51802.2021.9673932

Zindine Mokeddem, D. Melati, D. González-Andrade, Thi Thuy Duong Dinh, M. Montesinos-Ballester, E. Cassan, D. Marris-Morini, Y. Grinberg, P. Cheben, Danxia Xu, J. Schmid, L. Vivien, A. V. Velasco, C. Alonso‐Ramos

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引用次数: 0

摘要

硅光子学光谱仪在医学和危害检测方面具有巨大的应用潜力。然而，硅光谱仪对制造缺陷和环境条件非常敏感。在这里，我们研究了使用深度学习算法来提高傅里叶变换光谱仪对制造缺陷和温度变化的容忍度。

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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.

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2021 IEEE 17th International Conference on Group IV Photonics (GFP)

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