Recent breakthroughs in photonics-based quantum, neuromorphic and analogue processing have pointed out the need for new schemes for fully programmable nanophotonic devices. Universal optical elements based on interferometer meshes are large compared to the limited chip real estate, restricting the scalability of the approach. Here, we propose an ultracompact platform for low-loss programmable elements using the complex transmission matrix of a multi-port multimode waveguide. Our approach allows the design of arbitrary transmission matrices using patterns of weakly scattering perturbations, which is successfully achieved by means of a deep learning inverse network. The demonstrated platform allows full control over both the intensity and phase of all outputs in a 3x3 multiport device using a footprint of 33x6 µm 2 and for typical perturbations achievable in experiments.

Micro-and Nano-electromechanical (MEM/NEM) relays can operate with zero-leakage at far higher temperatures and levels of radiation than transistors, but have poor reliability. This work demonstrates improvement in reliability of MEM relays using nano-crystalline graphite (NCG)-coated contact surfaces. The high stability of NCG in ambient air, along with its low surface energy, appear to make it an ideal contact material. NCG-coated relays achieved over 2.8 million fast, hot-switching cycles with a drain current of at least 5 µA and on-resistance under 17 kΩ, in ambient air. The relays also were tested in slow, hot-switching cycles designed to increase the electrical stress on the contact, and consistently achieved on-currents up to 50 µA or the imposed current limit without failure. The eventual cause of failure appeared to be mechanical stress on the NCG layer over repeated cycling causing degradation. Increasing the layer thickness is expected to further improve the contact reliability. The relays are scalable and can be used as micro-or nano-scale switches in electronic components designed for very high temperatures and levels of radiation.

Thick epitaxial BaTiO 3 films ranging from 120 nm to 1 μm were grown by off-axis RF magnetron sputtering on SrTiO 3 -templated silicon-on-insulator (SOI) substrates for use in electro-optic applications, where such large thicknesses are necessary. The films are of high quality, rivaling those grown by molecular beam epitaxy (MBE) in crystalline quality, but can be grown 10 times faster. Extraction of lattice parameters from geometric phase analysis of atomic-resolution scanning transmission electron microscopy images revealed how the in-plane and out-of-plane lattice spacings of sputtered BaTiO 3 changes as a function of layer position within a thick film. Our results indicate that compared to molecular beam epitaxy, sputtered films retain their out-of-plane polarization (c-axis) orientation for larger thicknesses. We also find an unusual re-transition from in-plane polarization (a-axis) to out-ofplane polarization (c-axis), along with an anomalous lattice expansion, near the surface. We also studied a method of achieving 100% a-axis-oriented films using a two-step process involving amorphous growth and recrystallization of a seed layer followed by normal high temperature growth. While this method is successful in achieving full a-axis orientation even at low thicknesses, the resulting film has a large number of voids and misoriented grains. Electro-optic measurement using a transmission setup of a sputtered BTO film grown using the optimized conditions yields an effective Pockels coefficient as high as 183 pm/V. A Mach− Zehnder modulator fabricated on such films exhibits phase shifting with an equivalent Pockels coefficient of 157 pm/V. These results demonstrate that sputtered BTO thick films can be used for integrated electro-optic modulators for Si photonics.

Emerging applications such as the Internet-of-Things and more-electric aircraft require electronics with integrated data storage that can operate in extreme temperatures with high energy efficiency. As transistor leakage current increases with temperature, nanoelectromechanical relays have emerged as a promising alternative. However, a reliable and scalable non-volatile relay that retains its state when powered off has not been demonstrated. Part of the challenge is electromechanical pull-in instability, causing the beam to snap in after traversing a section of the airgap. Here we demonstrate an electrostatically actuated nanoelectromechanical relay that eliminates electromechanical pull-in instability without restricting the dynamic range of motion. It has several advantages over conventional electrostatic relays, including low actuation voltages without extreme reduction in critical dimensions and near constant actuation airgap while the device moves, for improved electrostatic control. With this nanoelectromechanical relay we demonstrate the first hightemperature non-volatile relay operation, with over 40 non-volatile cycles at 200 ∘ C.

Two‐terminal memristor has emerged as one of the most promising neuromorphic artificial electronic devices for their structural resemblance to biological synapses and ability to emulate many synaptic functions. In this work, a memristor based on the back‐end‐of‐line (BEOL) material silicon carbide (SiC) is developed. The thin film memristors demonstrate excellent binary resistive switching with compliance‐free and self‐rectifying characteristics which are advantageous for the implementation of high‐density 3D crossbar memory architectures. The conductance of this SiC‐based memristor can be modulated gradually through the application of both DC and AC signals. This behavior is demonstrated to further emulate several vital synaptic functions including paired‐pulse facilitation (PPF), post‐tetanic potentiation (PTP), short‐term potentiation (STP), and spike‐rate‐dependent plasticity (SRDP). The synaptic function of learning‐forgetting‐relearning processes is successfully emulated and demonstrated using a 3 × 3 artificial synapse array. This work presents an important advance in SiC‐based memristor and its application in both memory and neuromorphic computing.