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

Plasmonic nanogap enhanced phase change devices with dual electrical-optical functionality

Modern-day computers use electrical signaling for processing and storing data which is bandwidth limited and power-hungry. These limitations are bypassed in the field of communications, where optical signaling is the norm. To exploit optical signaling in computing, however, new on-chip devices that work seamlessly in both electrical and optical domains are needed. Phase change devices can in principle provide such functionality, but doing so in a single device has proved elusive due to conflicting requirements of size-limited electrical switching and diffraction-limited photonic devices. Here, we combine plasmonics, photonics and electronics to deliver a novel integrated phase-change memory and computing cell that can be electrically or optically switched between binary or multilevel states, and read-out in either mode, thus merging computing and communications technologies

Photonic Neural Networks: A Compact Review

It has long been known that photonic science and especially photonic communications can raise the speed of technologies and producing manufacturing. More recently, photonic science has also been interested in its capabilities to implement low-precision linear operations, such as matrix multiplications, fast and effciently. For a long time most scientists taught that Electronics is the end of science but after many years and about 35 years ago had been understood that electronics do not answer alone and should have a new science. Today we face modern ways and instruments for doing tasks as soon as possible in proportion to many decays before. The velocity of progress in science is very fast. All our progress in science area is dependent on modern knowledge about new methods. In this research, we want to review the concept of a photonic neural network. For this research was selected 18 main articles were among the main 30 articles on this subject from 2015 to the 2022 year. These articles noticed three principles: 1- Experimental concepts, 2- Theoretical concepts, and, finally 3- Mathematic concepts. We should be careful with this research because mathematics has a very important and constructive role in our topics! One of the topics that are very valid and also new, is simulation. We used to work with simulation in some parts of this research. First, briefly, we start by introducing photonics and neural networks. In the second we explain the advantages and disadvantages of a combination of both in the science world and industries and technologies about them. Also, we are talking about the achievements of a thin modern science. Third, we try to introduce some important and valid parameters in neural networks. In this manner, we use many mathematic tools in some portions of this article

Fast and reliable storage using a 5 bit, nonvolatile photonic memory cell

This is the final version. Available from Optical Society of America via the DOI in this record.Optically storing and addressing data on photonic chips is of particular interest as such capability would eliminate optoelectronic conversion losses in data centers. It would also enable on-chip non-von Neumann photonic computing by allowing multinary data storage with high fidelity. Here, we demonstrate such an optically addressed, multilevel memory capable of storing up to 34 nonvolatile reliable and repeatable levels (over 5 bits) using the phase change material Ge2Sb2Te5 integrated on a photonic waveguide. Crucially, we demonstrate for the first time, to the best of our knowledge, a technique that allows us to program the device with a single pulse regardless of the previous state of the material, providing an order of magnitude improvement over previous demonstrations in terms of both time and energy consumption. We also investigate the influence of write-and-erase pulse parameters on the single-pulse recrystallization, amorphization, and readout error in our multilevel memory, thus tailoring pulse properties for optimum performance. Our work represents a significant step in the development of photonic memories and their potential for novel integrated photonic applications.Engineering and Physical Sciences Research Council (EPSRC)European CommissionDeutsche Forschungsgemeinschaft (DFG)Horizon 2020 Framework Programme (H2020

Spatio-spectral control of coherent nanophotonics

Fast modulation of optical signals that carry multidimensional information in the form of wavelength, phase or polarization has fueled an explosion of interest in integrated photonics. This interest however masks a significant challenge which is that independent modulation of multi-wavelength carrier signals in a single waveguide is not trivial. Such challenge is attributed to the longitudinal direction of guided-mode propagation, limiting the spatial separation and modulation of electric-field. Here, we overcome this using a single photonic element that utilizes active coherent (near) perfect absorption. We make use of standing wave patterns to exploit the spatial-degrees-of-freedom of in-plane modes and individually address elements according to their mode number. By combining the concept of coherent absorption in spatio-spectral domain with active phase-change nanoantennas, we engineer and test an integrated, reconfigurable and multi-spectral modulator operating within a single element. Our approach demonstrates for the first time, a non-volatile, wavelength-addressable element, providing a pathway for exploring the tunable capabilities in both spatial and spectral domains of coherent nanophotonics

Impact of GST thickness on GST-loaded silicon waveguides for optimal optical switching

[EN] Phase-change integrated photonics has emerged as a new platform for developing photonic integrated circuits by integrating phase-change materials like GeSbTe (GST) onto the silicon photonics platform. The thickness of the GST patch that is usually placed on top of the waveguide is crucial for ensuring high optical performance. In this work, we investigate the impact of the GST thickness in terms of optical performance through numerical simulation and experiment. We show that higher-order modes can be excited in a GST-loaded silicon waveguide with relatively thin GST thicknesses (<100 nm), resulting in a dramatic reduction in the extinction ratio. Our results would be useful for designing high-performance GST/Si-based photonic devices such as non-volatile memories that could find utility in many emerging applications.This work is supported by grants PID2019-111460GB-I00, ICTS-2017-28-UPV-9F, and FPU17/04224 funded by MCIN/AEI/ 10.13039/501100011033, by "ERDF A way of making Europe" and "ESF Investing in your future". Funding from Generalitat Valenciana (PROMETEO/2019/123). Funding for open access charge: Universitat Politecnica de Valencia. The authors would like to thank Helen Urgelles for her help with the experimental measurements.Parra Gómez, J.; Navarro-Arenas, J.; Kovylina-Zabyako, M.; Sanchis Kilders, P. (2022). Impact of GST thickness on GST-loaded silicon waveguides for optimal optical switching. Scientific Reports. 12(1):1-9. https://doi.org/10.1038/s41598-022-13848-01912