Scalable Non-Volatile Tuning of Photonic Computational Memories by Automated Silicon Ion Implantation.

Photonic integrated circuits (PICs) are revolutionizing the realm of information technology, promising unprecedented speeds and efficiency in data processing and optical communication. However, the nanoscale precision required to fabricate these circuits at scale presents significant challenges, due to the need to maintain consistency across wavelength-selective components, which necessitates individualized adjustments after fabrication. Harnessing spectral alignment by automated silicon ion implantation, in this work scalable and non-volatile photonic computational memories are demonstrated in high-quality resonant devices. Precise spectral trimming of large-scale photonic ensembles from a few picometers to several nanometres is achieved with long-term stability and marginal loss penalty. Based on this approach, spectrally aligned photonic memory and computing systems for general matrix multiplication are demonstrated, enabling wavelength multiplexed integrated architectures at large scales.

Event-driven adaptive optical neural network.

We present an adaptive optical neural network based on a large-scale event-driven architecture. In addition to changing the synaptic weights (synaptic plasticity), the optical neural network's structure can also be reconfigured enabling various functionalities (structural plasticity). Key building blocks are wavelength-addressable artificial neurons with embedded phase-change materials that implement nonlinear activation functions and nonvolatile memory. Using multimode focusing, the activation function features both excitatory and inhibitory responses and shows a reversible switching contrast of 3.2 decibels. We train the neural network to distinguish between English and German text samples via an evolutionary algorithm. We investigate both the synaptic and structural plasticity during the training process. On the basis of this concept, we realize a large-scale network consisting of 736 subnetworks with 16 phase-change material neurons each. Overall, 8398 neurons are functional, highlighting the scalability of the photonic architecture.

High-Quality Amorphous Silicon Carbide for Hybrid Photonic Integration Deposited at a Low Temperature.

Integrated photonic platforms have proliferated in recent years, each demonstrating its unique strengths and shortcomings. Given the processing incompatibilities of different platforms, a formidable challenge in the field of integrated photonics still remains for combining the strengths of different optical materials in one hybrid integrated platform. Silicon carbide is a material of great interest because of its high refractive index, strong second- and third-order nonlinearities, and broad transparency window in the visible and near-infrared range. However, integrating silicon carbide (SiC) has been difficult, and current approaches rely on transfer bonding techniques that are time-consuming, expensive, and lacking precision in layer thickness. Here, we demonstrate high-index amorphous silicon carbide (a-SiC) films deposited at 150 °C and verify the high performance of the platform by fabricating standard photonic waveguides and ring resonators. The intrinsic quality factors of single-mode ring resonators were in the range of Qint = (4.7-5.7) × 105 corresponding to optical losses between 0.78 and 1.06 dB/cm. We then demonstrate the potential of this platform for future heterogeneous integration with ultralow-loss thin SiN and LiNbO3 platforms.

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems.

Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomimetic memory properties, which promise to significantly improve power consumption in computing applications. Here, we provide a comprehensive overview of recent advances in memristive technology, including memristive devices, theory, algorithms, architectures, and systems. In addition, we discuss research directions for various applications of memristive technology including hardware accelerators for artificial intelligence, in-sensor computing, and probabilistic computing. Finally, we provide a forward-looking perspective on the future of memristive technology, outlining the challenges and opportunities for further research and innovation in this field. By providing an up-to-date overview of the state-of-the-art in memristive technology, this review aims to inform and inspire further research in this field.

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/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%.

Scalable High-Precision Trimming of Photonic Resonances by Polymer Exposure to Energetic Beams.

Integrated photonic circuits (PICs) have seen an explosion in interest, through to commercialization in the past decade. Most PICs rely on sharp resonances to modulate, steer, and multiplex signals. However, the spectral characteristics of high-quality resonances are highly sensitive to small variations in fabrication and material constants, which limits their applicability. Active tuning mechanisms are commonly employed to account for such deviations, consuming energy and occupying valuable chip real estate. Readily employable, accurate, and highly scalable mechanisms to tailor the modal properties of photonic integrated circuits are urgently required. Here, we present an elegant and powerful solution to achieve this in a scalable manner during the semiconductor fabrication process using existing lithography tools: by exploiting the volume shrinkage exhibited by certain polymers to permanently modulate the waveguide's effective index. This technique enables broadband and lossless tuning with immediate applicability in wide-ranging applications in optical computing, telecommunications, and free-space optics.

Atomically thin optomemristive feedback neurons.

Cognitive functions such as learning in mammalian brains have been attributed to the presence of neuronal circuits with feed-forward and feedback topologies. Such networks have interactions within and between neurons that provide excitory and inhibitory modulation effects. In neuromorphic computing, neurons that combine and broadcast both excitory and inhibitory signals using one nanoscale device are still an elusive goal. Here we introduce a type-II, two-dimensional heterojunction-based optomemristive neuron, using a stack of MoS2, WS2 and graphene that demonstrates both of these effects via optoelectronic charge-trapping mechanisms. We show that such neurons provide a nonlinear and rectified integration of information, that can be optically broadcast. Such a neuron has applications in machine learning, particularly in winner-take-all networks. We then apply such networks to simulations to establish unsupervised competitive learning for data partitioning, as well as cooperative learning in solving combinatorial optimization problems.

Varifocal Metalens Using Tunable and Ultralow-loss Dielectrics.

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 Ge2 Sb2 Te5 , 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 Sb2 Se3 , 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.

AFM Manipulation of EGaIn Microdroplets to Generate Controlled, On-Demand Contacts on Molecular Self-Assembled Monolayers.

Liquid metal droplets, such as eutectic gallium-indium (EGaIn), are important in many research areas, such as soft electronics, catalysis, and energy storage. Droplet contact on solid surfaces is typically achieved without control over the applied force and without optimizing the wetting properties in different environments (e.g., in air or liquid), resulting in poorly defined contact areas. In this work, we demonstrate the direct manipulation of EGaIn microdroplets using an atomic force microscope (AFM) to generate repeated, on-demand making and breaking of contact on self-assembled monolayers (SAMs) of alkanethiols. The nanoscale positional control and feedback loop in an AFM allow us to control the contact force at the nanonewton level and, consequently, tune the droplet contact areas at the micrometer length scale in both air and ethanol. When submerged in ethanol, the droplets are highly nonwetting, resulting in hysteresis-free contact forces and minimal adhesion; as a result, we are able to create reproducible geometric contact areas of 0.8-4.5 µm2 with the alkanethiolate SAMs in ethanol. In contrast, there is a larger hysteresis in the contact forces and larger adhesion for the same EGaIn droplet in air, which reduced the control over the contact area (4-12 µm2). We demonstrate the usefulness of the technique and of the gained insights in EGaIn contact mechanics by making well-defined molecular tunneling junctions based on alkanethiolate SAMs with small geometric contact areas of between 4 and 12 µm2 in air, 1 to 2 orders of magnitude smaller than previously achieved.

A Universal Pick-and-Place Assembly for Nanowires.

With the introduction of techniques to grow highly functional nanowires of exotic materials and demonstrations of their potential in new applications, techniques for depositing nanowires on functional platforms have been an area of active interest. However, difficulties in handling individual nanowires with high accuracy and reliability have so far been a limiting factor in large-scale integration of high-quality nanowires. Here, a technique is demonstrated to transfer single nanowires reliably on virtually any platform, under ambient conditions. Functional nanowires of InP, AlGaAs, and GeTe on various patterned structures such as electrodes, nanophotonic devices, and even ultrathin transmission electron microscopy (TEM) membranes are transferred. It is shown that the versatility of this technique further enables to perform on-chip nano-optomechanical measurements of an InP nanowire for the first time via evanescent field coupling. Thus, this technique facilitates effortless integration of single nanowires into applications that were previously seen as cumbersome or even impractical, spanning a wide range from TEM studies to in situ electrical, optical, and mechanical characterization.

An integrated photonics engine for unsupervised correlation detection.

With more and more aspects of modern life and scientific tools becoming digitized, the amount of data being generated is growing exponentially. Fast and efficient statistical processing, such as identifying correlations in big datasets, is therefore becoming increasingly important, and this, on account of the various compute bottlenecks in modern digital machines, has necessitated new computational paradigms. Here, we demonstrate one such novel paradigm, via the development of an integrated phase-change photonics engine. The computational memory engine exploits the accumulative property of Ge2Sb2Te5 phase-change cells and wavelength division multiplexing property of optics in delivering fully parallelized and colocated temporal correlation detection computations. We investigate this property and present an experimental demonstration of identifying real-time correlations in data streams on the social media platform Twitter and high-traffic computing nodes in data centers. Our results demonstrate the use case of high-speed integrated photonics in accelerating statistical analysis methods.

Polarization-selective reconfigurability in hybridized-active-dielectric nanowires.

Wavelength and polarization are two fundamental properties of light within which information can be encoded and (de)multiplexed. While wavelength-selective systems have widely proliferated, polarization-addressable active photonics has not seen notable progress, primarily because tunable and polarization-selective nanostructures have been elusive. Here, we introduce hybridized-active-dielectric (HAD) nanowires to achieve polarization-selective tunability. We then demonstrate the ability to use polarization as a parameter to selectively modulate the conductance of individual nanowires within a multi-nanowire system. By using polarization as the tunable vector, we show matrix-vector multiplication in a nanowire device configuration. While our HAD nanowires use phase-change materials as the active material, this concept is readily generalized to other active materials hybridized with dielectrics and thus has the potential in a broad range of applications from photonic memories and routing to polarization-multiplexed computing.

Chalcogenide optomemristors for multi-factor neuromorphic computation.

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.

Antimony as a Programmable Element in Integrated Nanophotonics.

The use of nonlinear elements with memory as photonic computing components has seen a huge surge in interest in recent years with the rise of artificial intelligence and machine learning. A key component is the nonlinear element itself. A class of materials known as phase change materials has been extensively used to demonstrate the viability of such computing. However, such materials continue to have relatively slow switching speeds, and issues with cyclability related to phase segregation of phase change alloys. Here, using antimony (Sb) thin films with thicknesses less than 5 nm we demonstrate reversible, ultrafast switching on an integrated photonic platform with retention time of tens of seconds. We use subpicosecond pulses, the shortest used to switch such elements, to program seven distinct memory levels. This portends their use in ultrafast nanophotonic applications ranging from nanophotonic beam steerers to nanoscale integrated elements for photonic computing.

Electronically Reconfigurable Photonic Switches Incorporating Plasmonic Structures and Phase Change Materials.

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.

Real-time nanomechanical property modulation as a framework for tunable NEMS.

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 ([Formula: see text] > 104) 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.

GaS:WS2 Heterojunctions for Ultrathin Two-Dimensional Photodetectors with Large Linear Dynamic Range across Broad Wavelengths.

Two-dimensional (2D) photodetectors based on photovoltaic effect or photogating effect can hardly achieve both high photoresponsivity and large linear dynamic range at the same time, which greatly limits many practical applications such as imaging sensors. Here, the conductive-sensitizer strategy, a general design for improving photoresponsivity and linear dynamic range in 2D photodetectors is provided and experimentally demonstrated on vertically stacked bilayer WS2/GaS0.87 under a parallel circuit mode. Owing to successful band alignment engineering, the isotype type-II heterojunction enables efficient charge carrier transfer from WS2, the high-mobility sensitizer, to GaS0.87, the low-mobility channel, under illumination from a broad visible spectrum. The transferred electron charges introduce a reverse electric field which efficiently lowers the band offset between the two materials, facilitating a transition from low-mobility photocarrier transport to high-mobility photocarrier transport with increasing illumination power. We achieved a large linear dynamic range of 73 dB as well as a high and constant photoresponsivity of 13 A/W under green light. X-ray photoelectron spectroscopy, cathodoluminescence, and Kelvin probe force microscopy further identify the key role of defects in monolayer GaS0.87 in engineering the band alignment with monolayer WS2. This work proposes a design route based on band and interface modulation for improving performance of 2D photodetectors and provides deep insights into the important role of strong interlayer coupling in offering heterostructures with desired properties and functions.

Exploiting rotational asymmetry for sub-50 nm mechanical nanocalligraphy.

Nanofabrication has experienced extraordinary progress in the area of lithography-led processes over the last decades, although versatile and adaptable techniques addressing a wide spectrum of materials are still nascent. Scanning probe lithography (SPL) offers the capability to readily pattern sub-100 nm structures on many surfaces; however, the technique does not scale to dense and multi-lengthscale structures. Here, we demonstrate a technique, which we term nanocalligraphy scanning probe lithography (nc-SPL), that overcomes these limitations. Nc-SPL employs an asymmetric tip and exploits its rotational asymmetry to generate structures spanning the micron to nanometer lengthscales through real-time linewidth tuning. Using specialized tip geometries and by precisely controlling the patterning direction, we demonstrate sub-50 nm patterns while simultaneously improving on throughput, tip longevity, and reliability compared to conventional SPL. We further show that nc-SPL can be employed in both positive and negative tone patterning modes, in contrast to conventional SPL. This underlines the potential of this technique for processing sensitive surfaces such as 2D materials, which are prone to tip-induced shear or beam-induced damage.