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

Modelling phase-change integrated photonic devices

Available from E\PCOS via the link in this recordWe report the progress made on the development of a self-consistent 3-dimensional simulation framework, yielding the time and spatially resolved electric field, temperature and material phase, for integrated phase-change photonic devices. We illustrate the analysis made for a prototypical integrated phase-change photonic memory, and report the results of SET and RESET operations.Engineering and Physical Sciences Research Council (EPSRC

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

Plasmonically-enhanced all-optical integrated phase-change memory

This is the final version. Available on open access from the Optical Society of America via the DOI in this record.Integrated phase-change photonic memory devices offer a novel route to non-volatile storage and computing that can be carried out entirely in the optical domain, obviating the necessity for time and energy consuming opto-electrical conversions. Such memory devices generally consist of integrated waveguide structures onto which are fabricated small phase-change memory cells. Switching these cells between their amorphous and crystalline states modifies significantly the optical transmission through the waveguide, so providing memory, and computing, functionality. To carry out such switching, optical pulses are sent down the waveguide, coupling to the phase-change cell, heating it up, and so switching it between states. While great strides have been made in the development of integrated phase-change photonic devices in recent years, there is always a pressing need for faster switching times, lower energy consumption and a smaller device footprint. In this work, therefore, we propose the use of plasmonic enhancement of the light-matter interaction between the propagating waveguide mode and the phase-change cell as a means to faster, smaller and more energy-efficient devices. In particular, we propose a form of plasmonic dimer nanoantenna of significantly sub-micron size that, in simulations, offers significant improvements in switching speeds and energies. Write/erase speeds in the range 2 to 20 ns and write/erase energies in the range 2 to 15 pJ were predicted, representing improvements of one to two orders of magnitude when compared to conventional device architectures.Engineering and Physical Sciences Research Council (EPSRC

A behavioural model for integrated phase-change photonics

This is the author accepted manuscript. The final version is available from the European Phase Change and Ovonics Symposium via the link in this recordThe use of phase-change materials in integrated photonics applications has enabled the development of new types of all-optical devices, including multilevel photonic memories, arithmetic and logic processors and synaptic and neuron mimics. In order to design, optimise and understand the performance of large-scale systems, fast and accurate material and device models are needed. Here we present a behavioural model for phase-change photonic devices that can simulate the write, erase and readout operations in timespans compatible with system level performance evaluation.European Union Horizon 2020Engineering and Physical Sciences Research Council (EPSRC

Behavioral modeling of integrated phase-change photonic devices for neuromorphic computing applications

This is the final version. Available from AIP Publishing via the DOI in this record. The combination of phase-change materials and integrated photonics has led to the development of new forms of all-optical devices, includingphotonic memories, arithmetic and logic processors, and synaptic and neuronal mimics. Such devices can be readily fabricated into photonicintegrated circuits, so potentially delivering large-scale all-optical arithmetic-logic units and neuromorphic processing chips. To facilitate inthe design and optimization of such large-scale systems, and to aid in the understanding of device and system performance, fast yet accuratecomputer models are needed. Here, we describe the development of a behavioral modeling tool that meets such requirements, being capableof essentially instantaneous modeling of the write, erase, and readout performance of various integrated phase-change photonic devices,including those for synaptic and neuronal mimics.Engineering and Physical Sciences Research Council (EPSRC)European Commissio

System-Level Simulation for Integrated Phase-Change Photonics

This is the author accepted manuscript. The final version is available on open access from IEEE via the DOI in this recordConventional computing systems are limited in performance by the well-known von Neumann bottleneck, arising from the physical separation of processor and memory units. The use of electrical signals in such systems also limits computing speeds and introduces significant energy losses. There is thus a pressing need for unconventional computing approaches, ones that can exploit the high bandwidths/speeds and low losses intrinsic to photonics. A promising platform for such a purpose is that offered by integrated phase-change photonics. Here, chalcogenide phase-change materials are incorporated into standard integrated photonics devices to deliver wide-ranging computational functionality, including non-volatile memory and fast, low-energy arithmetic and neuromorphic processing. We report the development of a compact behavioral model for integrated phase change photonic devices, one which is fast enough to allow system level simulations to be run in a reasonable timescale with basic computing resources, while also being accurate enough to capture the key operating characteristics of real devices. Moreover, our model is readily incorporated with commercially available simulation software for photonic integrated circuits, thereby enabling the design, simulation and optimization of large-scale phase-change photonics systems. We demonstrate such capabilities by exploring the optimization and simulation of the operating characteristics of two important phase-change photonic systems recently reported, namely a spiking neural network system and a matrix-vector photonic crossbar array (photonic tensor core). Results show that use of our behavioral model can significantly facilitate the design and optimization at the system level, as well as expediting exploration of the capabilities of novel phase-change computing architectures

A plasmonically enhanced route to faster and more energy-efficient phase-change integrated photonic memory and computing devices

This is the author accepted manuscript. The final version is available from AIP Publishing via the DOI in this recordData availability: The data that support the findings of this study are available from the corresponding author upon reasonable request.Over the past 30 years or more, chalcogenide phase-change materials and devices have generated much scientific and industrial interest, particularly as a platform for non-volatile optical and electronic storage devices. More recently, the combination of chalcogenide phase-change materials with photonic integrated circuits has begun to be enthusiastically explored, and among many proposals, the all-photonic phase-change memory brings the memristor-type device concept to the integrated photonic platform, opening up the route to new forms of unconventional (e.g., in-memory and neuromorphic) yet practicable optical computing. For any memory or computing device, fast switching speed and low switching energy are most attractive attributes, and approaches by which speed and energy efficiency can be improved are always desirable. For phase-change material-based devices, speed and energy consumption are both enhanced the smaller the volume of phase-change material that is required to be switched between its amorphous and crystalline phases. However, in conventional integrated photonic systems, the optical readout of nanometric-sized volumes of phase-change material is problematic. Plasmonics offers a way to bypass such limitations: plasmonic resonant structures are inherently capable of harnessing and focussing optical energy on sub-wavelength scales, far beyond the capabilities of conventional optical and photonic elements. In this work, we explore various approaches to combine the three building blocks of Si-photonics, resonant plasmonic structures, and phase-change materials to deliver plasmonically enhanced integrated phase-change photonic memory and computing devices and systems, underlining the inherent technical and theoretical challenges therein.European Union Horizon 2020Engineering and Physical Sciences Research Council (EPSRC