Low-voltage-tunable electromechanical photonic directional coupler in silicon nitride for telecom wavelengths.

Silicon nitride is a low-loss photonic integrated circuit (PIC) platform. However, silicon nitride also shows small nonlinear optical properties and is dielectric, which makes the implementation of programmability challenging. Typically, the thermo-optic effect is used for this, but modulators based on this effect are often slow and cross talk-limited. Here, we present a different approach to programmability in silicon nitride photonics. Micro-electromechanical elements are added to a photonic directional coupler, forming two H-shaped structures. The coupling can be changed by applying a voltage to electrodes placed onto the H-structure, which are then attracted by an electrostatic force. These suspended directional couplers show an insertion loss of 0.67 dB and demonstrate switching with 1.1±0.1 µs rise times, representing a valuable addition to the thermal photonic modulators in silicon nitride technology that offer higher modulation speeds while keeping a comparable insertion loss.

3DSC - a dataset of superconductors including crystal structures.

Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors, the use of data science tools is to date slowed down by a lack of accessible data. In this work, we present a new and publicly available superconductivity dataset ('3DSC'), featuring the critical temperature TC of superconducting materials additionally to tested non-superconductors. In contrast to existing databases such as the SuperCon database which contains information on the chemical composition, the 3DSC is augmented by approximate three-dimensional crystal structures. We perform a statistical analysis and machine learning experiments to show that access to this structural information improves the prediction of the critical temperature TC of materials. Furthermore, we provide ideas and directions for further research to improve the 3DSC. We are confident that this database will be useful in applying state-of-the-art machine learning methods to eventually find new superconductors.

Efficient adiabatic-coupler-based silicon nitride waveguide crossings for photonic quantum computing.

Optical integrated quantum computing protocols, in particular using the dual-rail encoding, require that waveguides cross each other to realize, e.g., SWAP or Toffoli gate operations. We demonstrate efficient adiabatic crossings. The working principle is explained using simulations, and several test circuits are fabricated in silicon nitride (SiN) to characterize the coupling performance and insertion loss. Well-working crossings are found by experimentally varying the coupler parameters. The adiabatic waveguide crossing (WgX) outperforms a normal directional coupler in terms of spectral working range and fabrication variance stability. The insertion loss is determined using two different methods: using the transmission and by incorporating crossings in microring resonators. We show that the latter method is very efficient for low-loss photonic components. The lowest insertion loss is 0.18 dB (4.06%) enabling high-fidelity NOT operations. The presented WgX represents a high-fidelity (96.2%) quantum NOT operation.

Graph neural networks for materials science and chemistry.

Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.

Aluminum nitride integration on silicon nitride photonic circuits: a hybrid approach towards on-chip nonlinear optics.

Aluminum nitride (AlN) is an emerging material for integrated quantum photonics due to its large χ(2) nonlinearity. Here we demonstrate the hybrid integration of AlN on silicon nitride (SiN) photonic chips. Composite microrings are fabricated by reactive DC sputtering of c-axis oriented AlN on top of pre-patterned SiN. This new approach does not require any patterning of AlN and depends only on reliable SiN nanofabrication. This simplifies the nanofabrication process drastically. Optical characteristics, such as the quality factor, propagation losses and group index, are obtained. Our hybrid resonators can have a one order of magnitude increase in quality factor after the AlN integration, with propagation losses down to 0.7 dB/cm. Using finite-element simulations, phase matching in these waveguides is explored.

Efficient Optomechanical Mode-Shape Mapping of Micromechanical Devices.

Visualizing eigenmodes is crucial in understanding the behavior of state-of-the-art micromechanical devices. We demonstrate a method to optically map multiple modes of mechanical structures simultaneously. The fast and robust method, based on a modified phase-lock loop, is demonstrated on a silicon nitride membrane and shown to outperform three alternative approaches. Line traces and two-dimensional maps of different modes are acquired. The high quality data enables us to determine the weights of individual contributions in superpositions of degenerate modes.