Department of Materials Science & Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Materials Research Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Nat Commun. 2018 Oct 23;9(1):4405. doi: 10.1038/s41467-018-06773-2.
On-chip spectrometers have the potential to offer dramatic size, weight, and power advantages over conventional benchtop instruments for many applications such as spectroscopic sensing, optical network performance monitoring, hyperspectral imaging, and radio-frequency spectrum analysis. Existing on-chip spectrometer designs, however, are limited in spectral channel count and signal-to-noise ratio. Here we demonstrate a transformative on-chip digital Fourier transform spectrometer that acquires high-resolution spectra via time-domain modulation of a reconfigurable Mach-Zehnder interferometer. The device, fabricated and packaged using industry-standard silicon photonics technology, claims the multiplex advantage to dramatically boost the signal-to-noise ratio and unprecedented scalability capable of addressing exponentially increasing numbers of spectral channels. We further explore and implement machine learning regularization techniques to spectrum reconstruction. Using an 'elastic-D' regularized regression method that we develop, we achieved significant noise suppression for both broad (>600 GHz) and narrow (<25 GHz) spectral features, as well as spectral resolution enhancement beyond the classical Rayleigh criterion.
片上光谱仪具有在许多应用中提供显著的尺寸、重量和功率优势的潜力，例如光谱传感、光网络性能监测、高光谱成像和射频频谱分析。然而，现有的片上光谱仪设计在光谱通道数量和信噪比方面受到限制。在这里，我们展示了一种变革性的片上数字傅里叶变换光谱仪，该光谱仪通过对可重构马赫-曾德尔干涉仪进行时域调制来获取高分辨率光谱。该设备采用工业标准硅光子技术制造和封装，声称具有复用优势，可以显著提高信噪比，并具有前所未有的可扩展性，能够满足指数级增长的光谱通道数量。我们进一步探索并实现了光谱重建的机器学习正则化技术。使用我们开发的“弹性 D”正则化回归方法，我们实现了对宽带 (>600 GHz) 和窄带 (<25 GHz) 光谱特征的显著噪声抑制，以及超越经典瑞利准则的光谱分辨率增强。
