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Efficient complementary metal-oxide semiconductor-based nonlinear optical devices in the near-infrared are in strong demand. Due to two-photon absorption in silicon, however, much nonlinear research is shifting towards unconventional photonics platforms. In this work, we demonstrate the generation of an octave-spanning coherent supercontinuum in a silicon waveguide covering the spectral region from the near- to shortwave-infrared. With input pulses of 18 pJ in energy, the generated signal spans the wavelength range from the edge of the silicon transmission window, approximately 1.06 to beyond 2.4 μm, with a −20 dB bandwidth covering 1.124–2.4 μm. An octave-spanning supercontinuum was also observed at the energy levels as low as 4 pJ (−35 dB bandwidth). We also measured the coherence over an octave, obtaining , in good agreement with the simulations. In addition, we demonstrate optimization of the third-order dispersion of the waveguide to strengthen the dispersive wave and discuss the advantage of having a soliton at the long wavelength edge of an octave-spanning signal for nonlinear applications. This research paves the way for applications, such as chip-scale precision spectroscopy, optical coherence tomography, optical frequency metrology, frequency synthesis and wide-band wavelength division multiplexing in the telecom window.

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretic data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) spectra of metal-organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental spectra and shuffled, then merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra.For the first time, one-to-one material identification was achieved. The optimized model showed the highest identification accuracy of 96.7% for the Top 5 ranking among a dataset of 1012 MOFs.Neighborhood components analysis (NCA) on the experimental XRD spectra shows that the spectra from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD spectra. This CNN model trained by the data-augmentation technique would not only open numerous potential applications for identifying XRD spectra for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR.