All-optical wavelength conversion using mode switching in InP microdisc laser

Wavelength conversion using an indium phosphide based microdisc laser (MDL) heterogeneously integrated on a silicon-on-insulator waveguide is reported. Several lasing modes are present within the disc cavity, between which wavelength conversion can be performed by mode switching and spectral filtering. For the first time, low-power wavelength up- and downconversion using one single MDL is demonstrated. Operation with a bit error rate below 10(-9) at 2.5 Gbit/s and operation below the forward-error-correction limit of 10(-3) at 10 Gbit/s are shown without the use of additional seeding beams

Integrated optical backplane amplifier

A solution for compensating losses in optical interconnects is provided. Large-core Al2O3:Nd3+ channel waveguide amplifiers are characterized and tested in combination with passive polymer waveguides. Coupling losses between the two waveguides are investigated in order to optimize the channel geometries of the two waveguide types. A tapered Al2O3:Nd3+ waveguide is designed to improve the pump intensity in the active region. A maximum 0.21-dB net gain at a signal wavelength of 880 nm is demonstrated in a structure in which an Al2O3:Nd3+ waveguide is coupled between two polymer waveguides. The gain can be improved by increasing the pump power and adjusting the waveguide properties of the amplifier

A low-power high-speed InP microdisk modulator heterogeneously integrated on a SOI waveguide

We report on the modulation characteristics of indium phosphide (InP) based microdisks heterogeneously integrated on a silicon–on–insulator (SOI) waveguide. We present static extinction ratios and dynamic operation up to 10 Gb/s. Operation with a bit–error rate below 1 × 10-9 is demonstrated at 2.5, 5.0 and 10.0 Gb/s and the performance is compared with that of a commercial modulator. Power penalties are analyzed with respect to the pattern length. The power consumption is calculated and compared with state–of–the–art integrated modulator concepts. We demonstrate that InP microdisk modulators combine low–power and low–voltage operation with low footprint and high–speed. Moreover, the devices can be fabricated using the same technology as for lasers, detectors and wavelength converters, making them very attractive for co–integration

Survey of Photonic and Plasmonic Interconnect Technologies for Intra-Datacenter and High-Performance Computing Communications

Large scale data centers (DC) and high performance computing (HPC) systems require more and more computing power at higher energy efficiency. They are already consuming megawatts of power, and a linear extrapolation of trends reveals that they may eventually lead to unrealistic power consumption scenarios in order to satisfy future requirements (e.g., Exascale computing). Conventional complementary metal oxide semiconductor (CMOS)-based electronic interconnects are not expected to keep up with the envisioned future board-to-board and chip-to-chip (within multi-chip-modules) interconnect requirements because of bandwidth-density and power-consumption limitations. However, low-power and high-speed optics-based interconnects are emerging as alternatives for DC and HPC communications; they offer unique opportunities for continued energy-efficiency and bandwidth-density improvements, although cost is a challenge at the shortest length scales. Plasmonics-based interconnects on the other hand, due to their extremely small size, offer another interesting solution for further scaling operational speed and energy efficiency. At the device-level, CMOS compatibility is also an important issue, since ultimately photonics or plasmonics will have to be co-integrated with electronics. In this paper, we survey the available literature and compare the aforementioned interconnect technologies, with respect to their suitability for high-speed and energy-efficient on-chip and offchip communications. This paper refers to relatively short links with potential applications in the following interconnect distance hierarchy: local group of racks, board to board, module to module, chip to chip, and on chip connections. We compare different interconnect device modules, including low-energy output devices (such as lasers, modulators, and LEDs), photodetectors, passive devices (i.e., waveguides and couplers) and electrical circuitry (such as laserdiode drivers, modulator drivers, transimpedance, and limiting amplifiers). We show that photonic technologies have the potential to meet the requirements for selected HPC and DC applications in a shorter term. We also present that plasmonic interconnect modules could offer ultra-compact active areas, leading to high integration bandwidth densities, and low device capacitances allowing for ultra-high bandwidth operation that would satisfy the application requirements further into the future

Brain-inspired nanophotonic spike computing:challenges and prospects

Nanophotonic spiking neural networks (SNNs) based on neuron-like excitable subwavelength (submicrometre) devices are of key importance for realizing brain-inspired, power-efficient artificial intelligence (AI) systems with high degree of parallelism and energy efficiency. Despite significant advances in neuromorphic photonics, compact and efficient nanophotonic elements for spiking signal emission and detection, as required for spike-based computation, remain largely unexplored. In this invited perspective, we outline the main challenges, early achievements, and opportunities toward a key-enabling photonic neuro-architecture using III-V/Si integrated spiking nodes based on nanoscale resonant tunnelling diodes (nanoRTDs) with folded negative differential resistance. We utilize nanoRTDs as nonlinear artificial neurons capable of spiking at high-speeds. We discuss the prospects for monolithic integration of nanoRTDs with nanoscale light-emitting diodes and nanolaser diodes, and nanophotodetectors to realize neuron emitter and receiver spiking nodes, respectively. Such layout would have a small footprint, fast operation, and low power consumption, all key requirements for efficient nano-optoelectronic spiking operation. We discuss how silicon photonics interconnects, integrated photorefractive interconnects, and 3D waveguide polymeric interconnections can be used for interconnecting the emitter-receiver spiking photonic neural nodes. Finally, using numerical simulations of artificial neuron models, we present spike-based spatio-temporal learning methods for applications in relevant AI-based functional tasks, such as image pattern recognition, edge detection, and SNNs for inference and learning. Future developments in neuromorphic spiking photonic nanocircuits, as outlined here, will significantly boost the processing and transmission capabilities of next-generation nanophotonic spike-based neuromorphic architectures for energy-efficient AI applications. This perspective paper is a result of the European Union funded research project ChipAI in the frame of the Horizon 2020 Future and Emerging Technologies Open programme.</p

Ferroelectric, Analog Resistive Switching in Back‐End‐of‐Line Compatible TiN/HfZrO4/TiOx Junctions

Due to their compatibility with complementary metal–oxide–semiconductor technologies, hafnium‐based ferroelectric devices receive increasing interest for the fabrication of neuromorphic hardware. Herein, an analog resistive memory device is fabricated with a process developed for back‐end‐of‐line integration. A 4.5 nm‐thick HfZrO4 (HZO) layer is crystallized into the ferroelectric phase, a thickness thin enough to allow electrical conduction through the layer. A TiOx interlayer is used to create an asymmetric junction as required for transferring a polarization state change into a modification of the conductivity. Memristive functionality is obtained, both in the pristine state and after ferroelectric wake‐up, involving redistribution of oxygen vacancies in the ferroelectric layer. The resistive switching is shown to originate directly from the ferroelectric properties of the HZO layer.ISSN:1862-6270ISSN:1862-625

An Integrated Photorefractive Analog Matrix-Vector Multiplier for Machine Learning

AI is fueling explosive growth in compute demand that traditional digital chip architectures cannot keep up with. Analog crossbar arrays enable power efficient synaptic signal processing with linear scaling on neural network size. We present a photonic photorefractive crossbar array for neural network training and inference on local analog memory. We discuss the concept and present results based on the first prototype hardware.This research was funded by European Union’s Horizon 2020 research and innovation program, grant numbers 828841 (ChipAI) and 860360 (Post-Digital).Peer reviewe