Optical information capacity of silicon

Modern computing and data storage systems increasingly rely on parallel architectures where processing and storage load is distributed within a cluster of nodes. The necessity for high-bandwidth data links has made optical communication a critical constituent of modern information systems and silicon the leading platform for creating the necessary optical components. While silicon is arguably the most extensively studied material in history, one of its most important attributes, an analysis of its capacity to carry optical information, has not been reported. The calculation of the information capacity of silicon is complicated by nonlinear losses, phenomena that emerge in optical nanowires as a result of the concentration of optical power in a small geometry. Nonlinear losses are absent in silica glass optical fiber and other common communication channels. While nonlinear loss in silicon is well known, noise and fluctuations that arise from it have never been considered. Here we report sources of fluctuations that arise from two-photon absorption and free-carrier plasma effects and use these results to investigate the theoretical limit of the information capacity of silicon. Our results show that noise and fluctuations due to nonlinear absorption become significant and limit the information capacity well before nonlinear loss itself becomes dominant. We present closed-form analytical expressions that quantify the capacity and provide an intuitive understanding of the underlying interactions. Our results provide the capacity limit and its origin, and suggest solutions for extending it via coding and coherent signaling. The amount of information that can be transmitted by light through silicon is the key element in future information systems. Results presented here are not only applicable to silicon but also to other semiconductor optical channels

Photonic integration enabling new multiplexing concepts in optical board-to-board and rack-to-rack interconnects

New broadband applications are causing the datacenters to proliferate, raising the bar for higher interconnection speeds. So far, optical board-to-board and rack-to-rack interconnects relied primarily on low-cost commodity optical components assembled in a single package. Although this concept proved successful in the first generations of optical-interconnect modules, scalability is a daunting issue as signaling rates extend beyond 25 Gb/s. In this paper we present our work towards the development of two technology platforms for migration beyond Infiniband enhanced data rate (EDR), introducing new concepts in board-to-board and rack-to-rack interconnects. The first platform is developed in the framework of MIRAGE European project and relies on proven VCSEL technology, exploiting the inherent cost, yield, reliability and power consumption advantages of VCSELs. Wavelength multiplexing, PAM-4 modulation and multi-core fiber (MCF) multiplexing are introduced by combining VCSELs with integrated Si and glass photonics as well as BiCMOS electronics. An in-plane MCF-to-SOI interface is demonstrated, allowing coupling from the MCF cores to 340x400 nm Si waveguides. Development of a low-power VCSEL driver with integrated feed-forward equalizer is reported, allowing PAM-4 modulation of a bandwidth-limited VCSEL beyond 25 Gbaud. The second platform, developed within the frames of the European project PHOXTROT, considers the use of modulation formats of increased complexity in the context of optical interconnects. Powered by the evolution of DSP technology and towards an integration path between inter and intra datacenter traffic, this platform investigates optical interconnection system concepts capable to support 16QAM 40GBd data traffic, exploiting the advancements of silicon and polymer technologies

Silicon-organic hybrid electro-optical devices

Organic materials combined with strongly guiding silicon waveguides open the route to highly efficient electro-optical devices. Modulators based on the so-called silicon-organic hybrid (SOH) platform have only recently shown frequency responses up to 100 GHz, high-speed operation beyond 112 Gbit/s with fJ/bit power consumption. In this paper, we review the SOH platform and discuss important devices such as Mach-Zehnder and IQ-modulators based on the linear electro-optic effect. We further show liquid-crystal phase-shifters with a voltage-length product as low as V pi L = 0.06 V.mm and sub-mu W power consumption as required for slow optical switching or tuning optical filters and devices

Direct fiber vector eigenmode multiplexing transmission seeded by integrated optical vortex emitters

Spatial modes have received substantial attention over the last decades and are used in optical communication applications. In fiber-optic communications, the employed linearly polarized modes and phase vortex modes carrying orbital angular momentum can be synthesized by fiber vector eigenmodes. To improve the transmission capacity and miniaturize the communication system, straightforward fiber vector eigenmode multiplexing and generation of fiber-eigenmode-like polarization vortices (vector vortex modes) using photonic integrated devices are of substantial interest. Here, we propose and demonstrate direct fiber vector eigenmode multiplexing transmission seeded by integrated optical vortex emitters. By exploiting vector vortex modes (radially and azimuthally polarized beams) generated from silicon microring resonators etched with angular gratings, we report data-carrying fiber vector eigenmode multiplexing transmission through a 2-km large-core fiber, showing low-level mode crosstalk and favorable link performance. These demonstrations may open up added capacity scaling opportunities by directly accessing multiple vector eigenmodes in the fiber and provide compact solutions to replace bulky diffractive optical elements for generating various optical vector beams

Nanophotonic reservoir computing with photonic crystal cavities to generate periodic patterns

Reservoir computing (RC) is a technique in machine learning inspired by neural systems. RC has been used successfully to solve complex problems such as signal classification and signal generation. These systems are mainly implemented in software, and thereby they are limited in speed and power efficiency. Several optical and optoelectronic implementations have been demonstrated, in which the system has signals with an amplitude and phase. It is proven that these enrich the dynamics of the system, which is beneficial for the performance. In this paper, we introduce a novel optical architecture based on nanophotonic crystal cavities. This allows us to integrate many neurons on one chip, which, compared with other photonic solutions, closest resembles a classical neural network. Furthermore, the components are passive, which simplifies the design and reduces the power consumption. To assess the performance of this network, we train a photonic network to generate periodic patterns, using an alternative online learning rule called first-order reduced and corrected error. For this, we first train a classical hyperbolic tangent reservoir, but then we vary some of the properties to incorporate typical aspects of a photonics reservoir, such as the use of continuous-time versus discrete-time signals and the use of complex-valued versus real-valued signals. Then, the nanophotonic reservoir is simulated and we explore the role of relevant parameters such as the topology, the phases between the resonators, the number of nodes that are biased and the delay between the resonators. It is important that these parameters are chosen such that no strong self-oscillations occur. Finally, our results show that for a signal generation task a complex-valued, continuous-time nanophotonic reservoir outperforms a classical (i.e., discrete-time, real-valued) leaky hyperbolic tangent reservoir (normalized root-mean-square errors = 0.030 versus NRMSE = 0.127)