An optoelectronic framework enabled by low-dimensional phase-change films.

The development of materials whose refractive index can be optically transformed as desired, such as chalcogenide-based phase-change materials, has revolutionized the media and data storage industries by providing inexpensive, high-speed, portable and reliable platforms able to store vast quantities of data. Phase-change materials switch between two solid states--amorphous and crystalline--in response to a stimulus, such as heat, with an associated change in the physical properties of the material, including optical absorption, electrical conductance and Young's modulus. The initial applications of these materials (particularly the germanium antimony tellurium alloy Ge2Sb2Te5) exploited the reversible change in their optical properties in rewritable optical data storage technologies. More recently, the change in their electrical conductivity has also been extensively studied in the development of non-volatile phase-change memories. Here we show that by combining the optical and electronic property modulation of such materials, display and data visualization applications that go beyond data storage can be created. Using extremely thin phase-change materials and transparent conductors, we demonstrate electrically induced stable colour changes in both reflective and semi-transparent modes. Further, we show how a pixelated approach can be used in displays on both rigid and flexible films. This optoelectronic framework using low-dimensional phase-change materials has many likely applications, such as ultrafast, entirely solid-state displays with nanometre-scale pixels, semi-transparent 'smart' glasses, 'smart' contact lenses and artificial retina devices

Non-volatile silicon photonic memory with more than 4-bit per cell capability

We present the first demonstration of an integrated photonic phase-change memory using GeSbTe-225 on silicon-on-insulator and demonstrate reliable multilevel operation with a single programming pulse. We also compare our results on silicon with previous demonstrations on silicon nitride. Crucially, achieving this on silicon enables tighter integration of traditional electronics with photonic memories in future, making phase-change photonic memory a viable and integrable technology

Single-step fabrication of high performance extraordinary transmission plasmonic metasurfaces employing ultrafast lasers

Plasmonic metasurfaces based on the extraordinary optical transmission effect (EOT) can be designed to efficiently transmit specific spectral bands from the visible to the far-infrared regimes, offering numerous applications in im-portant technological fields such as compact multispectral imaging, biological and chemical sensing, or color displays. However, due to their subwavelength nature, EOT metasurfaces are nowadays fabricated with nano- and micro-lithographic techniques, requiring many processing steps and carried out in expensive cleanroom environments. In this work, we propose and experimentally demonstrate a novel, single-step process for the rapid fabrication of high performance mid- and long-wave infrared EOT metasurfaces employing ultrafast direct laser writing (DLW). Micro-hole arrays composing extraordinary transmission metasurfaces were fabricated over areas of 4 mm2 in timescales of units of minutes, employing single pulse ablation of 40 nm thick Au films on dielectric substrates mounted on a high-precision motorized stage. We show how by carefully characterizing the influence of only three key experimental pa-rameters on the processed micro-morphologies (namely laser pulse energy, scan velocity and beam shaping slit), we can have on-demand control of the optical characteristics of the extraordinary transmission effect in terms of transmission wavelength, quality factor and polarization sensitivity of the resonances. To illustrate this concept, a set of EOT metasurfaces having different performances and operating in different spectral regimes has been successfully designed, fabricated and tested. Comparison between transmittance measurements and numerical simulations have revealed that all the fabricated devices behave as expected, thus demonstrating the high performance, flexibility and reliability of the proposed fabrication method

Emergent self-adaptation in an integrated photonic neural network for backpropagation-free learning

Plastic self-adaptation, nonlinear recurrent dynamics and multi-scale memory are desired features in hardware implementations of neural networks, because they enable them to learn, adapt and process information similarly to the way biological brains do. In this work, we experimentally demonstrate these properties occurring in arrays of photonic neurons. Importantly, this is realised autonomously in an emergent fashion, without the need for an external controller setting weights and without explicit feedback of a global reward signal. Using a hierarchy of such arrays coupled to a backpropagation-free training algorithm based on simple logistic regression, we are able to achieve a performance of 98.2% on the MNIST task, a popular benchmark task looking at classification of written digits. The plastic nodes consist of silicon photonics microring resonators covered by a patch of phase-change material that implements nonvolatile memory. The system is compact, robust, and straightforward to scale up through the use of multiple wavelengths. Moreover, it constitutes a unique platform to test and efficiently implement biologically plausible learning schemes at a high processing speed

Single-Step Fabrication of High-Performance Extraordinary Transmission Plasmonic Metasurfaces Employing Ultrafast Lasers

Plasmonic metasurfaces based on the extraordinary optical transmission (EOT) effect can be designed to efficiently transmit specific spectral bands from the visible to the far-infrared regimes, offering numerous applications in important technological fields such as compact multispectral imaging, biological and chemical sensing, or color displays. However, due to their subwavelength nature, EOT metasurfaces are nowadays fabricated with nano- and micro-lithographic techniques, requiring many processing steps and carrying out in expensive cleanroom environments. In this work, we propose and experimentally demonstrate a novel, single-step process for the rapid fabrication of high-performance mid- and long-wave infrared EOT metasurfaces employing ultrafast direct laser writing. Microhole arrays composing extraordinary transmission metasurfaces were fabricated over an area of 4 mm2 in timescales of units of minutes, employing single pulse ablation of 40 nm thick Au films on dielectric substrates mounted on a high-precision motorized stage. We show how by carefully characterizing the influence of only three key experimental parameters on the processed micro-morphologies (namely, laser pulse energy, scan velocity, and beam shaping slit), we can have on-demand control of the optical characteristics of the extraordinary transmission effect in terms of transmission wavelength, quality factor, and polarization sensitivity of the resonances. To illustrate this concept, a set of EOT metasurfaces having different performances and operating in different spectral regimes has been successfully designed, fabricated, and tested. Comparison between transmittance measurements and numerical simulations has revealed that all the fabricated devices behave as expected, thus demonstrating the high performance, flexibility, and reliability of the proposed fabrication method. We believe that our findings provide the pillars for mass production of EOT metasurfaces with on-demand optical properties and create new research trends toward single-step laser fabrication of metasurfaces with alternative geometries and/or functionalities

New 'Mixed-Mode' Optoelectronic Applications Possibilities using Phase-Change Materials and Devices

To date the main applications of phase-change materials and devices have been limited to the provision of non-volatile memories. Recently, however, the potential has been demonstrated for using a phase-change approach for the provision of entirely new concepts in optoelectronics, including phase-change displays, integrated phase-change photonic memories, optical modulation and optical computing [1-3]. Such novel applications are enabled by the ability of phase-change devices to operate in a 'mixed-mode' configuration, where the excitation is provided electrically and the sensing is carried out optically, or vice-versa. Exploitation of this mixed-mode is made possible in phase-change materials due to the large and simultaneous changes that occur in both refractive index and electrical resistivity on transformation between amorphous and crystalline states. In this paper, based on studies part-funded by the NSF Materials World Network, we present recent results of the use of such mixed-mode operation to provide new applications, including a demonstration of phase-change optoelectronics devices that can be used to make ultrathin all-solid-state colour displays of ultrahigh resolution [1], and hybrid integrated phase-change photonic circuits that offer both a low-power, multi-level memory capability and a computing functionality [2,3]. As so often mentioned by the late (and sadly missed) Stanford Ovhinsky at previous MRS meetings [4], phase-change materials have the potential to provide us with so much more than simple digital memory - a potential that we are now beginning to realize and exploit. [1] P Hosseini, C D Wright and H Bhaskaran, Nature 511, 206 (2014) [2] C Rios , P Hosseini , C D Wright , H Bhaskaran and W H P Pernice, Advanced Materials 26, 1372 (2014) [3] C D Wright, Y Liu, K I Kohary, M M Aziz, R J Hicken, Advanced Materials 23, 3408 (2011) [4] S R Ovshinsky and B Pashmakov, MRS Proceedings 803, 49 (2004

Photonics for artificial intelligence and neuromorphic computing

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, in particular, 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

In-memory photonic dot-product engine with electrically programmable weight banks

Electronically reprogrammable photonic circuits based on phase-change chalcogenides present an avenue to resolve the von-Neumann bottleneck; however, implementation of such hybrid photonic-electronic processing has not achieved computational success. Here, we achieve this milestone by demonstrating an in-memory photonic-electronic dot-product engine, one that decouples electronic programming of phase-change materials (PCMs) and photonic computation. Specifically, we develop non-volatile electronically reprogrammable PCM memory cells with a record-high 4-bit weight encoding, the lowest energy consumption per unit modulation depth (1.7 nJ per dB) for Erase operation (crystallization), and a high switching contrast (158.5%) using non-resonant silicon-on-insulator waveguide microheater devices. This enables us to perform parallel multiplications for image processing with a superior contrast-to-noise ratio (greater than 87.36) that leads to an enhanced computing accuracy (standard deviation less than 0.007). An in-memory hybrid computing system is developed in hardware for convolutional processing for recognizing images from the MNIST database with inferencing accuracies of 86% and 87%

Monadic Pavlovian associative learning in a backpropagation-free photonic network

Over a century ago, Ivan P. Pavlov, in a classic experiment, demonstrated how dogs can learn to associate a ringing bell with food, thereby causing a ring to result in salivation. Today, it is rare to find the use of Pavlovian type associative learning for artificial intelligence (AI) applications even though other learning concepts, in particular backpropagation on artificial neural networks (ANNs) have flourished. However, training using the backpropagation method on 'conventional' ANNs, especially in the form of modern deep neural networks (DNNs), is computationally and energy intensive. Here we experimentally demonstrate a form of backpropagation-free learning using a single (or monadic) associative hardware element. We realize this on an integrated photonic platform using phase-change materials combined with on-chip cascaded directional couplers. We then develop a scaled-up circuit network using our monadic Pavlovian photonic hardware that delivers a distinct machine-learning framework based on single-element associations and, importantly, using backpropagation-free architectures to address general learning tasks. Our approach reduces the computational burden imposed by learning in conventional neural network approaches, thereby increasing speed, whilst also offering higher bandwidth inherent to our photonic implementation