RESUMEN
Lithium niobate (LN) devices have been widely used in optical communication and nonlinear optics due to its attractive optical properties. The emergence of the thin-film lithium niobate on insulator (LNOI) improves performances of LN-based devices greatly. However, a high-efficient fiber-chip optical coupler is still necessary for the LNOI-based devices for practical applications. In this paper, we demonstrate a highly efficient and polarization-independent edge coupler based on LNOI. The coupler, fabricated by a standard semiconductor process, shows a low fiber-chip coupling loss of 0.54 dB/0.59 dB per facet at 1550 nm for TE/TM light, respectively, when coupled with an ultra-high numerical aperture fiber (UHNAF) of which the mode field diameter is about 3.2 µm. The coupling loss is lower than 1dB/facet for both TE and TM light in the wavelength range of 1527 nm to 1630 nm. A relatively large tolerance for optical misalignment is also proved, due to the coupler's large mode spot size up to 3.2 µm. The coupler shows a promising stability in high optical power and temperature variation.
RESUMEN
The high performance of thin film lithium niobate on insulator (LNOI) platform shows potential for electro-optical signal processing and nonlinear optics systems. To realize precise polarization management for sub-wavelength devices, we theoretically and experimentally investigate fundamental transverse electric (TE) and transverse magnetic (TM) mode hybridization in an x-cut LNOI ridge waveguide. Sudden jumps in the free-spectrum-range (FSR) of these modes in a fabricated microring resonator demonstrate the mode hybridization. The measured Q-factor of the lithium niobate (LN) microring is 1.78 million near the critical coupling condition. The hybridization wavelength was designed at 1562 nm and observed at 1537 nm. Potential applications include fundamental mode conversion, polarization rotation, polarization splitter, and polarization insensitive waveguides in optical receiver module.
RESUMEN
In recent years, neural networks have been used to generate symbolic melodies. However, the long-term structure in the melody has posed great difficulty to design a good model. In this article, we present a hierarchical recurrent neural network (HRNN) for melody generation, which consists of three long-short-term-memory (LSTM) subnetworks working in a coarse-to-fine manner along time. Specifically, the three subnetworks generate bar profiles, beat profiles, and notes, in turn, and the output of the high-level subnetworks are fed into the low-level subnetworks, serving as guidance to generate the finer time-scale melody components in the low-level subnetworks. Two human behavior experiments demonstrate the advantage of this structure over the single-layer LSTM which attempts to learn all hidden structures in melodies. Compared with the recently proposed models MidiNet and MusicVAE, the HRNN produces better melodies evaluated by humans.