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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.

Electrohydrodynamic (EHD) jet printing is an emerging technique in the field of additive manufacturing. Due to its versatility in the inks it can print, and most importantly, the printing resolution it can achieve, it is rapidly gaining favor for application in the manufacture of electronic devices, sensors, and displays among others. Although it is an affordable and accessible manufacturing process, it does require excellent operational understanding to achieve high resolution printing of up to 50 nm as reported in literature. In this review, three main aspects are considered, namely, the ink properties, the printer system itself (including design, nozzle dimensions, applied potential, and others), and the substrate onto which printing is being carried out. Knowing how all these factors can be manipulated and brought together allows the users of EHD printing to achieve extraordinary resolution and consistent results. The review is concluded with a brief discussion on where one can see the potential for development in this field of research. Electrohydrodynamic (EHD) jet printing is a high‐resolution additive manufacturing technique which has gained significant attention in recent years. It is an inexpensive tool which has found use in a variety of manufacturing areas. Whilst accessible, EHD printing requires good understanding of factors such as the ink properties and system parameters. This review offers insights into its successful application.

Here stable colour changes induced by solid-state electrical switching of ultrathin films of a germanium–antimony–telluride alloy are demonstrated, adding to its established uses in data storage; possible applications include flexible and transparent displays. Smart thin-film colour displays Phase-change materials such as the alloy germanium antimony tellurium (GST) have found wide practical use in optical storage media such as rewritable DVDs. More recently, such materials are also being investigated as candidates for the next generation of electrically operated non-volatile memories. Harish Bhaskaran and colleagues now consider the possibility of combining both optical and electrical control in ultrathin phase-change films. They first demonstrate that stable colour changes can be achieved in thin films of GST and go on to showcase a range of possible applications such as flexible and transparent displays. The work offers a new type of optoelectronic framework, and while still at an early stage, it offers an intriguing promise for technological applications. 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 1 , 2 , 3 , 4 , 5 . The initial applications of these materials (particularly the germanium antimony tellurium alloy Ge 2 Sb 2 Te 5 ) exploited the reversible change in their optical properties in rewritable optical data storage technologies 6 , 7 . More recently, the change in their electrical conductivity has also been extensively studied in the development of non-volatile phase-change memories 4 , 5 . 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.

Neuromorphic hardware that emulates biological computations is a key driver of progress in AI. For example, memristive technologies, including chalcogenide-based in-memory computing concepts, have been employed to dramatically accelerate and increase the efficiency of basic neural operations. However, powerful mechanisms such as reinforcement learning and dendritic computation require more advanced device operations involving multiple interacting signals. Here we show that nano-scaled films of chalcogenide semiconductors can perform such multi-factor in-memory computation where their tunable electronic and optical properties are jointly exploited. We demonstrate that ultrathin photoactive cavities of Ge-doped Selenide can emulate synapses with three-factor neo-Hebbian plasticity and dendrites with shunting inhibition. We apply these properties to solve a maze game through on-device reinforcement learning, as well as to provide a single-neuron solution to linearly inseparable XOR implementation. Some types of machine learning rely on the interaction between multiple signals, which requires new devices for efficient implementation. Here, Sarwat et al demonstrate a memristor that is both optically and electronically active, enabling computational models such as three factor learning.

Phase-change materials (PCMs) can switch between amorphous and crystalline states permanently yet reversibly. However, the change in their mechanical properties has largely gone unexploited. The most practical configuration using suspended thin-films suffer from filamentation and melt-quenching. Here, we overcome these limitations using nanowires as active nanoelectromechanical systems (NEMS). We achieve active modulation of the Young’s modulus in GeTe nanowires by exploiting a unique dislocation-based route for amorphization. These nanowire NEMS enable power-free tuning of the resonance frequency over a range of 30%. Furthermore, their high quality factors ( Q  > 10 4 ) are retained after phase transformation. We utilize their intrinsic piezoresistivity with unprecedented gauge factors (up to 1100) to facilitate monolithic integration. Our NEMS demonstrate real-time frequency tuning in a frequency-hopping spread spectrum radio prototype. This work not only opens up an entirely new area of phase-change NEMS but also provides a novel framework for utilizing functional nanowires in active mechanical systems. Direct modulation of Young‟s Modulus to affect mechanical resonances in real-time has not been achieved before. Here, the authors leverage the dislocation migration phenomenon in GeTe nanowires to develop nanoelectromechanical systems with powerfree tuning of mechanical resonances within a range of 30%, high and stable quality and gauge factors.

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/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 (≥87.36) that leads to an enhanced computing accuracy (standard deviation σ ≤ 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%. Hybrid photonic–electronic systems are essential for high-throughput neuromorphic computing. Here, the authors report an in-memory photonic–electronic dot-product engine with decoupled electronic programming of the phase-change memory cells and parallel photonic computation with high-bit operation, low energy consumption, and high accuracy.

The memristor, in which an external electric field controls the formation and annihilation of conductive channels, has been described both as a missing electronic element and a memory and computational element. Here, their utility as building blocks for promising reflective and energy-efficient colour technology is described.

Biological neural networks effortlessly tackle complex computational problems and excel at predicting outcomes from noisy, incomplete data. Artificial neural networks (ANNs), inspired by these biological counterparts, have emerged as powerful tools for deciphering intricate data patterns and making predictions. However, conventional ANNs can be viewed as “point estimates” that do not capture the uncertainty of prediction, which is an inherently probabilistic process. In contrast, treating an ANN as a probabilistic model derived via Bayesian inference poses significant challenges for conventional deterministic computing architectures. Here, we use chaotic light in combination with incoherent photonic data processing to enable high-speed probabilistic computation and uncertainty quantification. We exploit the photonic probabilistic architecture to simultaneously perform image classification and uncertainty prediction via a Bayesian neural network. Our prototype demonstrates the seamless cointegration of a physical entropy source and a computational architecture that enables ultrafast probabilistic computation by parallel sampling. A photonic computing platform using chaotic light for probabilistic arithmetic enables ultrafast, parallel processing. The system predicts classification and uncertainty simultaneously. The optical architecture allows efficient distribution evaluations at each output in a single time step.

The field of flat optics that uses nanostructured, so‐called metasurfaces, has seen remarkable progress over the last decade. Chalcogenide phase‐change materials (PCMs) offer a promising platform for realizing reconfigurable metasurfaces, as their optical properties can be reversibly tuned. Yet, demonstrations of phase‐change metalenses to date have employed material compositions such as Ge2Sb2Te5, which show high absorption in the visible to near‐IR wavelengths particularly in their crystalline state, limiting the applicability. Here, by using a low‐loss PCM Sb2Se3, for the first time, active polarization‐insensitive phase‐change metalenses at near‐IR wavelengths with comparable efficiencies in both material states are shown. An active metalens with a tunable focusing intensity of 95% and a focusing efficiency of 23% is demonstrated. A varifocal metalens is then demonstrated with a tunable focal length from 41 to 123 µm with comparable focusing efficiency (5.7% and 3%). The ultralow‐loss nature of the material introduces exciting new possibilities for optical communications, multi‐depth imaging, beam steering, optical routing, and holography. Sb2Se3 is a remarkable, highly efficient (κ = 0), and tunable (∆n = 0.75) dielectric material at near‐IR wavelengths. This ultralow‐loss material is utilized to realize reconfigurable metalenses at near‐IR. Taking advantage of the low‐loss nature of the material, both single focal metalens with tunable focusing intensity as well as varifocal metalens with comparable efficiencies are demonstrated.

The ever‐increasing demands for data processing and storage will require seamless monolithic co‐integration of electronics and photonics. Phase‐change materials are uniquely suited to fulfill this function due to their dual electro‐optical sensitivity, nonvolatile retention properties, and fast switching dynamics. The extreme size disparity however between CMOS electronics and dielectric photonics inhibits the realization of efficient and compact electrically driven photonic switches, logic and routing elements. Here, the authors achieve an important milestone in harmonizing the two domains by demonstrating an electrically reconfigurable, ultra‐compact and nonvolatile memory that is optically accessible. The platform relies on localized heat, generated within a plasmonic structure; this uniquely allows for both optical and electrical readout signals to be interlocked with the material state of the PCM while still ensuring that the writing operation is electrically decoupled. Importantly, by miniaturization and effective thermal engineering, the authors achieve unprecedented energy efficiency, opening up a path towards low‐energy optoelectronic hardware for neuromorphic and in‐memory computing. Light is uniquely suited to transport parallelized information and perform high‐speed computations. In this article, the authors have engineered a path for optical signal modulation, data storage and computation driven by electronics. By confining light to a nanoscale volume through plasmonic structures and by employing active phase‐change materials the authors demonstrate ultra‐low energy, non‐volatile switching with electrical and optical readout.