Wolfram H. P. Pernice

Bio: Wolfram H. P. Pernice is an academic researcher from University of Münster. The author has contributed to research in topics: Photonics & Nanophotonics. The author has an hindex of 56, co-authored 286 publications receiving 10102 citations. Previous affiliations of Wolfram H. P. Pernice include University of Oxford & Karlsruhe Institute of Technology.

All-optical spiking neurosynaptic networks with self-learning capabilities.

Abstract: Software implementations of brain-inspired computing underlie many important computational tasks, from image processing to speech recognition, artificial intelligence and deep learning applications. Yet, unlike real neural tissue, traditional computing architectures physically separate the core computing functions of memory and processing, making fast, efficient and low-energy computing difficult to achieve. To overcome such limitations, an attractive alternative is to design hardware that mimics neurons and synapses. Such hardware, when connected in networks or neuromorphic systems, processes information in a way more analogous to brains. Here we present an all-optical version of such a neurosynaptic system, capable of supervised and unsupervised learning. We exploit wavelength division multiplexing techniques to implement a scalable circuit architecture for photonic neural networks, successfully demonstrating pattern recognition directly in the optical domain. Such photonic neurosynaptic networks promise access to the high speed and high bandwidth inherent to optical systems, thus enabling the direct processing of optical telecommunication and visual data. An optical version of a brain-inspired neurosynaptic system, using wavelength division multiplexing techniques, is presented that is capable of supervised and unsupervised learning.

Integrated all-photonic non-volatile multi-level memory

Abstract: Researchers use phase-change materials to demonstrate an integrated optical memory with 13.4 pJ switching energy.

Harnessing optical forces in integrated photonic circuits

Abstract: The force exerted by photons is of fundamental importance in light-matter interactions. For example, in free space, optical tweezers have been widely used to manipulate atoms and microscale dielectric particles. This optical force is expected to be greatly enhanced in integrated photonic circuits in which light is highly concentrated at the nanoscale. Harnessing the optical force on a semiconductor chip will allow solid state devices, such as electromechanical systems, to operate under new physical principles. Indeed, recent experiments have elucidated the radiation forces of light in high-finesse optical microcavities, but the large footprint of these devices ultimately prevents scaling down to nanoscale dimensions. Recent theoretical work has predicted that a transverse optical force can be generated and used directly for electromechanical actuation without the need for a high-finesse cavity. However, on-chip exploitation of this force has been a significant challenge, primarily owing to the lack of efficient nanoscale mechanical transducers in the photonics domain. Here we report the direct detection and exploitation of transverse optical forces in an integrated silicon photonic circuit through an embedded nanomechanical resonator. The nanomechanical device, a free-standing waveguide, is driven by the optical force and read out through evanescent coupling of the guided light to the dielectric substrate. This new optical force enables all-optical operation of nanomechanical systems on a CMOS (complementary metal-oxide-semiconductor)-compatible platform, with substantial bandwidth and design flexibility compared to conventional electrical-based schemes.

High-speed and high-efficiency travelling wave single-photon detectors embedded in nanophotonic circuits

Abstract: Ultrafast, high-efficiency single-photon detectors are among the most sought-after elements in modern quantum optics and quantum communication. However, imperfect modal matching and finite photon absorption rates have usually limited their maximum attainable detection efficiency. Here we demonstrate superconducting nanowire detectors atop nanophotonic waveguides, which enable a drastic increase of the absorption length for incoming photons. This allows us to achieve high on-chip single-photon detection efficiency up to 91% at telecom wavelengths, repeatable across several fabricated chips. We also observe remarkably low dark count rates without significant compromise of the on-chip detection efficiency. The detectors are fully embedded in scalable silicon photonic circuits and provide ultrashort timing jitter of 18 ps. Exploiting this high temporal resolution, we demonstrate ballistic photon transport in silicon ring resonators. Our direct implementation of a high-performance single-photon detector on chip overcomes a major barrier in integrated quantum photonics.

Photonics for artificial intelligence and neuromorphic computing

Abstract: Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, particularly related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges. Photonics offers an attractive platform for implementing neuromorphic computing due to its low latency, multiplexing capabilities and integrated on-chip technology.

Cavity Optomechanics

Abstract: We review the field of cavity optomechanics, which explores the interaction between electromagnetic radiation and nano- or micromechanical motion This review covers the basics of optical cavities and mechanical resonators, their mutual optomechanical interaction mediated by the radiation pressure force, the large variety of experimental systems which exhibit this interaction, optical measurements of mechanical motion, dynamical backaction amplification and cooling, nonlinear dynamics, multimode optomechanics, and proposals for future cavity quantum optomechanics experiments In addition, we describe the perspectives for fundamental quantum physics and for possible applications of optomechanical devices

Deep learning with coherent nanophotonic circuits

Abstract: Artificial Neural Networks have dramatically improved performance for many machine learning tasks. We demonstrate a new architecture for a fully optical neural network that enables a computational speed enhancement of at least two orders of magnitude and three orders of magnitude in power efficiency over state-of-the-art electronics.

Electromagnetically induced transparency and slow light with optomechanics

Abstract: Controlling the interaction between localized optical and mechanical excitations has recently become possible following advances in micro- and nanofabrication techniques. So far, most experimental studies of optomechanics have focused on measurement and control of the mechanical subsystem through its interaction with optics, and have led to the experimental demonstration of dynamical back-action cooling and optical rigidity of the mechanical system. Conversely, the optical response of these systems is also modified in the presence of mechanical interactions, leading to effects such as electromagnetically induced transparency (EIT) and parametric normal-mode splitting. In atomic systems, studies of slow and stopped light (applicable to modern optical networks and future quantum networks) have thrust EIT to the forefront of experimental study during the past two decades. Here we demonstrate EIT and tunable optical delays in a nanoscale optomechanical crystal, using the optomechanical nonlinearity to control the velocity of light by way of engineered photon-phonon interactions. Our device is fabricated by simply etching holes into a thin film of silicon. At low temperature (8.7 kelvin), we report an optically tunable delay of 50 nanoseconds with near-unity optical transparency, and superluminal light with a 1.4 microsecond signal advance. These results, while indicating significant progress towards an integrated quantum optomechanical memory, are also relevant to classical signal processing applications. Measurements at room temperature in the analogous regime of electromagnetically induced absorption show the utility of these chip-scale optomechanical systems for optical buffering, amplification, and filtering of microwave-over-optical signals.

In-memory computing with resistive switching devices

Abstract: Modern computers are based on the von Neumann architecture in which computation and storage are physically separated: data are fetched from the memory unit, shuttled to the processing unit (where computation takes place) and then shuttled back to the memory unit to be stored. The rate at which data can be transferred between the processing unit and the memory unit represents a fundamental limitation of modern computers, known as the memory wall. In-memory computing is an approach that attempts to address this issue by designing systems that compute within the memory, thus eliminating the energy-intensive and time-consuming data movement that plagues current designs. Here we review the development of in-memory computing using resistive switching devices, where the two-terminal structure of the devices, their resistive switching properties, and direct data processing in the memory can enable area- and energy-efficient computation. We examine the different digital, analogue, and stochastic computing schemes that have been proposed, and explore the microscopic physical mechanisms involved. Finally, we discuss the challenges in-memory computing faces, including the required scaling characteristics, in delivering next-generation computing. This Review Article examines the development of in-memory computing using resistive switching devices.