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Materials whose optical properties can be reconfigured are crucial for photonic applications such as optical memories. Phase-change materials offer such utility and here recent progress is reviewed. Phase-change materials (PCMs) provide a unique combination of properties. On transformation from the amorphous to crystalline state, their optical properties change drastically. Short optical or electrical pulses can be utilized to switch between these states, making PCMs attractive for photonic applications. We review recent developments in PCMs and evaluate the potential for all-photonic memories. Towards this goal, the progress and existing challenges to realize waveguides with stepwise adjustable transmission are presented. Colour-rendering and nanopixel displays form another interesting application. Finally, nanophotonic applications based on plasmonic nanostructures are introduced. They provide reconfigurable, non-volatile functionality enabling manipulation and control of light. Requirements and perspectives to successfully implement PCMs in emerging areas of photonics are discussed.

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. Computing approaches in the optical domain would allow for ultra-fast signaling and ultra-high bandwidth capabilities. Here, Feldmann et al. demonstrate a photonic abacus, which provides multistate compute-and store operation by integrating phase-change materials with nanophotonic chips.

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.

With the proliferation of ultrahigh-speed mobile networks and internet-connected devices, along with the rise of artificial intelligence (AI) 1 , the world is generating exponentially increasing amounts of data that need to be processed in a fast and efficient way. Highly parallelized, fast and scalable hardware is therefore becoming progressively more important 2 . Here we demonstrate a computationally specific integrated photonic hardware accelerator (tensor core) that is capable of operating at speeds of trillions of multiply-accumulate operations per second (10 12 MAC operations per second or tera-MACs per second). The tensor core can be considered as the optical analogue of an application-specific integrated circuit (ASIC). It achieves parallelized photonic in-memory computing using phase-change-material memory arrays and photonic chip-based optical frequency combs (soliton microcombs 3 ). The computation is reduced to measuring the optical transmission of reconfigurable and non-resonant passive components and can operate at a bandwidth exceeding 14 gigahertz, limited only by the speed of the modulators and photodetectors. Given recent advances in hybrid integration of soliton microcombs at microwave line rates 3 – 5 , ultralow-loss silicon nitride waveguides 6 , 7 , and high-speed on-chip detectors and modulators, our approach provides a path towards full complementary metal–oxide–semiconductor (CMOS) wafer-scale integration of the photonic tensor core. Although we focus on convolutional processing, more generally our results indicate the potential of integrated photonics for parallel, fast, and efficient computational hardware in data-heavy AI applications such as autonomous driving, live video processing, and next-generation cloud computing services. An integrated photonic processor, based on phase-change-material memory arrays and chip-based optical frequency combs, which can operate at speeds of trillions of multiply-accumulate (MAC) operations per second, is demonstrated.

New developments in hardware-based ‘accelerators’ range from electronic tensor cores and memristor-based arrays to photonic implementations. The goal of these approaches is to handle the exponentially growing computational load of machine learning, which currently requires the doubling of hardware capability approximately every 3.5 months. One solution is increasing the data dimensionality that is processable by such hardware. Although two-dimensional data processing by multiplexing space and wavelength has been previously reported, the use of three-dimensional processing has not yet been implemented in hardware. In this paper, we introduce the radio-frequency modulation of photonic signals to increase parallelization, adding an additional dimension to the data alongside spatially distributed non-volatile memories and wavelength multiplexing. We leverage higher-dimensional processing to configure such a system to an architecture compatible with edge computing frameworks. Our system achieves a parallelism of 100, two orders higher than implementations using only the spatial and wavelength degrees of freedom. We demonstrate this by performing a synchronous convolution of 100 clinical electrocardiogram signals from patients with cardiovascular diseases, and constructing a convolutional neural network capable of identifying patients at sudden death risk with 93.5% accuracy.Radio-frequency modulation of optical signals increase the parallelization of photonic processors beyond that afforded by exploiting spatial and wavelength dimensions alone. The approach is then demonstrated on electrocardiogram signals and identifies patients at sudden death risk with 93.5% accuracy.

A Correction to this paper has been published: https://doi.org/10.1038/s41586-021-03216-9.

Additive manufacturing has provided a pathway for inexpensive and flexible manufacturing of specialized components and one-off parts. At the nanoscale, such techniques are less ubiquitous. Manufacturing at the nanoscale is dominated by lithography tools that are too expensive for small- and medium-sized enterprises (SMEs) to invest in. Additive nanomanufacturing (ANM) empowers smaller facilities to design, create, and manufacture on their own while providing a wider material selection and flexible design. This is especially important as nanomanufacturing thus far is largely constrained to 2-dimensional patterning techniques and being able to manufacture in 3-dimensions could open up new concepts. In this review, we outline the state-of-the-art within ANM technologies such as electrohydrodynamic jet printing, dip-pen lithography, direct laser writing, and several single particle placement methods such as optical tweezers and electrokinetic nanomanipulation. The ANM technologies are compared in terms of deposition speed, resolution, and material selection and finally the future prospects of ANM are discussed. This review is up-to-date until April 2014.

Phase change alloys have seen widespread use from rewritable optical discs to the present day interest in their use in emerging neuromorphic computing architectures. In spite of this enormous commercial interest, the physics of carriers in these materials is still not fully understood. Here, we describe the time and space dependence of the coupling between photoexcited carriers and the lattice in both the amorphous and crystalline states of one phase change material, GeTe. We study this using a time-resolved optical technique called picosecond acoustic method to investigate the \\textit{in situ} thermally assisted amorphous to crystalline phase transformation in GeTe. Our work reveals a clear evolution of the electron-phonon coupling during the phase transformation as the spectra of photoexcited acoustic phonons in the amorphous (\\(a\\)-GeTe) and crystalline (\\(\\alpha\\)-GeTe) phases are different. In particular and surprisingly, our analysis of the photoinduced acoustic pulse duration in crystalline GeTe suggests that a part of the energy deposited during the photoexcitation process takes place over a distance that clearly exceeds that defined by the pump light skin depth. In the opposite, the lattice photoexcitation process remains localized within that skin depth in the amorphous state. We then demonstrate that this is due to supersonic diffusion of photoexcited electron-hole plasma in the crystalline state. Consequently these findings prove the existence of a non-thermal transport of energy which is much faster than lattice heat diffusion.

Software-implementation, via neural networks, of brain-inspired computing approaches underlie many important modern-day computational tasks, from image processing to speech recognition, artificial intelligence and deep learning applications. Yet, differing from real neural tissue, traditional computing architectures physically separate the core computing functions of memory and processing, making fast, efficient and low-energy brain-like computing difficult to achieve. To overcome such limitations, an attractive and alternative goal is to design direct hardware mimics of brain neurons and synapses which, when connected in appropriate networks (or neuromorphic systems), process information in a way more fundamentally analogous to that of real brains. Here we present an all-optical approach to achieving such a goal. Specifically, we demonstrate an all-optical spiking neuron device and connect it, via an integrated photonics network, to photonic synapses to deliver a small-scale all-optical neurosynaptic system capable of supervised and unsupervised learning. Moreover, 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 using a photonic system comprising 140 elements. Such optical implementations of neurosynaptic networks promise access to the high speed and bandwidth inherent to optical systems, which would be very attractive for the direct processing of telecommunication and visual data in the optical domain.

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.