Raman spectra of carbon nanowalls (CNWs) grown using dc plasma-enhanced chemical vapor deposition were analyzed. The Raman spectra of CNWs exhibited G and D bands at ∼1580 and ∼1350cm−1, respectively. It is found that the bandwidth of the G band is relatively narrow, even when the peak intensity ratio of D band to G band is significantly high. This spectral feature of CNWs is distinguished from those of typical graphitelike carbons reported so far. From the comparison of these spectral features, it is shown that CNWs are composed of small crystallites with a high degree of graphitization.

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Predicting physical response of an artificially structured material is of particular interest for scientific and engineering applications. Here we use deep learning to predict optical response of artificially engineered nanophotonic devices. In addition to predicting forward approximation of transmission response for any given topology, this approach allows us to inversely approximate designs for a targeted optical response. Our Deep Neural Network (DNN) could design compact (2.6 × 2.6 μm2) silicon-on-insulator (SOI)-based 1 × 2 power splitters with various target splitting ratios in a fraction of a second. This model is trained to minimize the reflection (to smaller than ~ −20 dB) while achieving maximum transmission efficiency above 90% and target splitting specifications. This approach paves the way for rapid design of integrated photonic components relying on complex nanostructures.

We report the observation of bistable polarization switching in a vertical-cavity surface-emitting laser under optical injection. The wavelength dependence of the switching is measured. It is found that this polarization switching is achieved through injection locking where both the wavelength and the polarization of the vertical-cavity laser are locked to the injected optical signal.

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