Integrated phase-change photonic devices and systems

This is the author accepted manuscript. The final version is available from CUP via the DOI in this recordDriven by the rapid rise of silicon photonics, optical signaling is moving from the realm of long-distance communications to chip-to-chip, and even on-chip domains. If on-chip signaling becomes optical, we should consider what more we might do with light than just communicate. We might, for example, set goals for the storing and processing of information directly in the optical domain. Doing this might enable us to supplement, or even surpass, the performance of electronic processors, by exploiting the ultrahigh bandwidth and wavelength division multiplexing capabilities offered by optics. In this article, we show how, by using an integrated photonics platform that embeds chalcogenide phase-change materials into standard silicon photonics circuits, we can achieve some of these goals. Specifically, we show that a phase-change integrated photonics platform can deliver binary and multilevel memory, arithmetic and logic processing, as well as synaptic and neuronal mimics for use in neuromorphic, or brain-like, computing-all working directly in the optical domain.European Union Horizon 202

Reconfigurable nanophotonic devices using phase-change materials

This is the final version of the article. Available from E\PCOS via the URL in this record.Nanophotonic integrated circuits enable realizing functional optical devices using efficient design and fabrication routines. Their inherent stability and scalability makes them attractive for applications where optical signal processing is combined with coupling to external light stimuli. A majority of nanophotonic devices is, however, based on passive materials, which do not provide low-power tuning options or knobs for reconfigurability. We address this shortcoming by combining passive silicon nitride photonic devices with tunable phase-change materials [1]. Such a platform allows realizing both on-chip optical data storage [2] and active photonic components. Implementing on-chip photonic memories has been pursued for a long time, in particular for fabricating memory devices which are able to retain their state after the storage process. Photonic data storage would dramatically improve performance in existing computing architectures by reducing the latencies associated with electrical memories and potentially eliminating optoelectronic conversions. Furthermore, multi-level photonic memories with random access would allow for leveraging even greater computational capability. Thus far, photonic memories have been predominantly volatile, meaning that their state is lost once the input power is removed. We exploit hybrid photonic-phasechange materials to implement robust, non-volatile, all-photonic memories. By using optical near-field coupling within on-chip waveguides, we realize bit storage of up to eight levels in a single device that readily switches between intermediate states. We show that individual memory elements can be addressed using a wavelength multiplexing scheme. Such multi-level, multi-bit devices provide a pathway towards eliminating the von Neumann bottleneck and portend a new paradigm in all-photonic memory and non-conventional computing. We further show that such devices can be operated with short optical pulses, both for write and read operations

On-chip photonic synapse

This is the final version of the article. Available from the American Association for the Advancement of Science via the DOI in this record.The search for new "neuromorphic computing" architectures that mimic the brain's approach to simultaneous processing and storage of information is intense. Because, in real brains, neuronal synapses outnumber neurons by many orders of magnitude, the realization of hardware devices mimicking the functionality of a synapse is a first and essential step in such a search. We report the development of such a hardware synapse, implemented entirely in the optical domain via a photonic integrated-circuit approach. Using purely optical means brings the benefits of ultrafast operation speed, virtually unlimited bandwidth, and no electrical interconnect power losses. Our synapse uses phase-change materials combined with integrated silicon nitride waveguides. Crucially, we can randomly set the synaptic weight simply by varying the number of optical pulses sent down the waveguide, delivering an incredibly simple yet powerful approach that heralds systems with a continuously variable synaptic plasticity resembling the true analog nature of biological synapses.This research was supported via the Engineering and Physical Sciences Research Council Manufacturing Fellowship EP/J018694/1, the Wearable and Flexible Technologies (WAFT) collaboration (EP/M015173/1), and the Chalcogenide Advanced Manufacturing Partnership (EP/M015130/1)

All-optical spiking neurosynaptic networks with self-learning capabilities

This is the author accepted manuscript. The final version is available from Nature Research via the DOI in this record.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.Engineering and Physical Sciences Research Council (EPSRC)European CommissionDeutsche Forschungsgemeinschaft (DFG

Integrated phase-change photonics for all-optical processing

This is the final version of the article. Available from E\PCOS via the URL in this record.Embedding phase-change materials (PCMs) in on-chip photonic circuitry enables nonvolatile alloptical operation of integrated optical devices. This hybrid system has been used so far in terms of memory applications. However, it also provides the capability to all-optically process light signals. Here, we use picosecond pulses to demonstrate both all-optical routing and all-optical arithmetic operations within the on-chip photonic circuitry

Calculating with light using a chip-scale all-optical abacus

This is the final version of the article. Available from Springer Nature via the DOI in this record.Machines that simultaneously process and store multistate data at one and the same location can provide a new class of fast, powerful and efficient general-purpose computers. We demonstrate the central element of an all-optical calculator, a photonic abacus, which provides multistate compute-and-store operation by integrating functional phase-change materials with nanophotonic chips. With picosecond optical pulses we perform the fundamental arithmetic operations of addition, subtraction, multiplication, and division, including a carryover into multiple cells. This basic processing unit is embedded into a scalable phase-change photonic network and addressed optically through a two-pulse random access scheme. Our framework provides first steps towards light-based non-von Neumann arithmetic.The authors acknowledge support by Deutsche Forschungsgemeinschaft (DFG) grants PE 1832/2-1 and EPSRC grant EP/J018783/1. M.S. acknowledges support from the Karlsruhe School of Optics and Photonics (KSOP) and the Stiftung der Deutschen Wirtschaft (sdw). C.R. is grateful to JEOL UK and the Clarendon Fund for funding his graduate studies. H.B. acknowledges support from the John Fell Fund and the EPSRC (EP/J00541X/2 and EP/J018694/1). The authors also acknowledge support from the DFG and the State of Baden-Württemberg through the DFG-Center for Functional Nanostructures (CFN). The authors thank S. Diewald for assistance with device fabrication

Reconfigurable Nanophotonic Cavities with Nonvolatile Response

 This is the author accepted manuscript. The final version is available from American Chemical Society via the DOI in this recordThe use of phase-change materials on waveguide photonics is presently being purported for a range of applications from on-chip photonic data storage to new computing paradigms. Photonic integrated circuits in combination with phase-change materials provide on-chip control handles, featuring nonvolatility and operation speeds down to the nano- and picosecond regime. Besides ultrafast control, efficient operation of nonvolatile elements is crucial and requires compact photonic designs. Here we embed phase-change materials in photonic crystal cavities to realize tunable nanophotonic devices which can be reconfigured on demand. The devices exploit strong light matter interactions between the resonant modes of the cavity and the evanescently coupled phase-change material cell. This results in an increased transmission contrast and a power reduction of 520% over conventional phase-change nanophotonic devices when reversibly switched with optical pulses. Such designs can thus open up new areas of reconfigurable nanophotonics without sacrificing the speeds or functionality for applications in optical memory cells, optical switches, and tunable wavelength filters.Engineering and Physical Sciences Research Council (EPSRC)European Research CouncilEuropean Union Horizon 202

Enhanced performance in plasmonic integrated phase-change memories

This is the final version.We here propose feasible strategies to improve the performance of integrated phase-change photonic memories by the use of plasmonic enhancement. Several solutions are investigated, focusing in particular on optimising the optical readout contrast (transmission modulation) that can be achieved between crystalline and amorphous states. Results show that by embedding the plasmonic nanoantenna within the body of the waveguide, or by using multiple coupled nanoantennas in series, significant improvements in optical readout contrast can be achieved, while maintaining relatively small insertion losses.European Union Horizon 202

A plasmonic route towards the energy scaling of on-chip integrated all-photonic phase-change memories

This is the author accepted manuscript.Phase-change photonic memory devices, conventionally implemented as a thin layer of phase-change material deposited on the top of an integrated Si or SiN waveguide, have the flexibility to be applied in a widely diverse context, as a pure memory device, a logic gate, an arithmetic processing unit and for biologically inspired computing. In all such applications increasing the speed, and reducing the power consumption, of the phase-switching process is most desirable. In this work, therefore, we investigate, via simulation, a novel integrated photonic device architecture that exploits plasmonic effects to enhance the light-matter interaction. Our device comprises a dimer nanoantenna fabricated on top of a SiN waveguide and with a phase-change material deposited into the gap between the two nanoantenna halves. We observed very considerably increased device speeds and reduced energy requirements, of up to two orders of magnitude, when compared to the conventional structure.Engineering and Physical Sciences Research Council (EPSRC

In-memory computing on a photonic platform

This is the final version. Available from the publisher via the DOI in this record.All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors or Oxford Research Archive for Data (https://ora.ox.ac.uk).Collocated data processing and storage are the norm in biological computing systems such as the mammalian brain. As our ability to create better hardware improves, new computational paradigms are being explored beyond von Neumann architectures. Integrated photonic circuits are an attractive solution for on-chip computing which can leverage the increased speed and bandwidth potential of the optical domain, and importantly, remove the need for electro-optical conversions. Here we show that we can combine integrated optics with collocated data storage and processing to enable all-photonic in-memory computations. By employing nonvolatile photonic elements based on the phase-change material, Ge2Sb2Te5, we achieve direct scalar and matrix-vector multiplication, featuring a novel single-shot Write/Erase and a drift-free process. The output pulse, carrying the information of the light-matter interaction, is the result of the computation. Our all-optical approach is novel, easy to fabricate and operate, and sets the stage for development of entirely photonic computers.Engineering and Physical Sciences Research Council (EPSRC)Deutsche Forschungsgemeinschaft (DFG)European Research Council (ERC