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Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration

ADVANCED MATERIALS (2023)

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NATURE ELECTRONICS (2022)

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Shi-Jun Liang et al.

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Controlled Optoelectronic Response in van der Waals Heterostructures for In-Sensor Computing

Qiyu Yang et al.

Summary: This paper demonstrates a van der Waals (vdW) heterostructure-based optoelectronic transistor that can integrate photoreceptor, memory, and computation functions. It exploits diverse photoelectric control to achieve versatile photoresponse characteristics and can emulate short- and long-term synaptic plasticity and psychological human memory. This promising hardware system for visual information in-sensor computing offers potential solutions for complex computation tasks.

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Nonlinear decision-making with enzymatic neural networks

S. Okumura et al.

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NATURE (2022)

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An elastic and reconfigurable synaptic transistor based on a stretchable bilayer semiconductor

Hyunseok Shim et al.

Summary: The research team reported an elastic and reconfigurable synaptic transistor that exhibits inhibitory and excitatory characteristics even under mechanical strain. The device uses a stretchable bilayer semiconductor and an encapsulating elastomer, showing memory characteristics and low energy consumption. When applied to an artificial neural network, it achieves recognition accuracy of over 90% even when stretched by 50%.

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Superconducting optoelectronic single-photon synapses

Saeed Khan et al.

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NATURE ELECTRONICS (2022)

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Editorial Material Engineering, Electrical & Electronic

Superconducting circuits that mimic the brain

Alessandro Casaburi et al.

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Neuromorphic devices based on fluorite-structured ferroelectrics

Dong Hyun Lee et al.

Summary: The continuous growth in data storage and processing has led to the need for more efficient computing architectures. Neuromorphic computing, inspired by biological systems, and devices based on emerging nonvolatile memories are gaining attention. This progress report reviews the recent developments in artificial synaptic and spiking neuron devices for neuromorphic computing based on fluorite-structured ferroelectrics.

INFOMAT (2022)

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Summary: Two-dimensional materials have become a focus of research for their potential in achieving carrier polarity, threshold voltage control, and multifunctional configurations in electronic devices. This review discusses the working principles and performance improvement metrics for 2D-material-based devices with diverse reconfigurability.

INFOMAT (2022)

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Two-dimensional In2Se3: A rising advanced material for ferroelectric data storage

Yu-Ting Huang et al.

Summary: This article reviews the atomic structures, ferroelectric switching mechanisms, and performance of the latest devices of 2D In2Se3 material. It emphasizes the breakthrough of maintaining ferroelectricity in 2D In2Se3 at ultrathin scales, which has significant implications for the development of miniaturized ferroelectric memory.

INFOMAT (2022)

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2022 roadmap on neuromorphic computing and engineering

Dennis Christensen et al.

Summary: This article introduces the characteristics and advantages of von Neumann architecture and neuromorphic computing systems. While traditional von Neumann architecture is powerful, it has high power consumption and cannot handle complex data. Neuromorphic computing systems, inspired by biological concepts, can achieve lower power consumption for storing and processing large amounts of digital information. The aim of this article is to provide perspectives on the current state and future challenges in the field of neuromorphic technology, and to provide a concise yet comprehensive introduction and future outlook for readers.

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Programmable van-der-Waals heterostructure-enabled optoelectronic synaptic floating-gate transistors with ultra-low energy consumption

Yilin Sun et al.

Summary: In this study, a programmable optoelectronic synaptic floating-gate transistor based on multilayer graphene/h-BN/MoS2 van der Waals heterostructures was demonstrated. The device achieved both plasticity emulation and modulation, and had a large memory ratio between program and erase states due to the dynamic tunneling process of photogenerated carriers. By applying a program/erase voltage spike, the device could function as a functional or silent synapse, providing programmable operation in optoelectronic synaptic transistors. The program state also resulted in ultra-low energy consumption per light spike event.

INFOMAT (2022)

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Structures, properties, and challenges of emerging 2D materials in bioelectronics and biosensors

Fan Chen et al.

Summary: Bioelectronics are powerful tools for monitoring and stimulating biological and biochemical processes. The use of emerging two-dimensional (2D) materials as detection platforms in bioelectronics and biosensors has gained significant attention. This review provides an in-depth analysis of the physicochemical structures of emerging 2D materials and their nanostructures for engineering high-performance bioelectronic and biosensing devices. The applications of emerging 2D materials in bioelectronics, such as neural interface simulation, biomolecular/biomarker detection, and skin sensors, are thoroughly discussed. The review concludes with insights on the challenges and future prospects of utilizing emerging 2D materials in bioelectronics and biosensors, emphasizing their importance in the biomedical field.

INFOMAT (2022)

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Peisong Wu et al.

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INFOMAT (2022)

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Large-Scale and Flexible Optical Synapses for Neuromorphic Computing and Integrated Visible Information Sensing Memory Processing

Ya-Xin Hou et al.

Summary: Optoelectronic synapses combining synaptic and optical-sensing functions have advantages in neuromorphic computing for visual information processing and learning. A novel optical synapse based on a pyrenyl graphdiyne/graphene/PbS quantum dot heterostructure shows linear and symmetric conductance updates with multiple states, allowing accurate pattern recognition. This optical synapse demonstrates strong fault tolerance and is suitable for wearable electronics applications.

ACS NANO (2021)

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Article Chemistry, Multidisciplinary

Magnonic Bending, Phase Shifting and Interferometry in a 2D Reconfigurable Nanodisk Crystal

Kilian D. Stenning et al.

Summary: This study proposes a class of reconfigurable magnonic crystals incorporating nanodisks as functional elements, allowing control of magnetization states by selectively activating or deactivating interelement coupling. By leveraging distinct coupling behaviors and magnonic band structures of bistable nanodisks, programmable magnonic waveguiding, bending, gating, and phase-shifting across a 2D network are achieved.

ACS NANO (2021)

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Article Chemistry, Multidisciplinary

Electrically Reconfigurable 3D Spin-Orbitronics

Yiqing Dong et al.

Summary: The explosive demands of storage capacity and the von Neumann bottleneck in modern computer architectures have led to innovations in information technology, with nonvolatile spintronics gaining attention for embedding computation capability into memory. By creating 3D spin-orbitronic architectures, it is possible to exponentially boost storage capacity and enable in-memory computing, offering a promising platform for 3D nanospintronics.

ADVANCED FUNCTIONAL MATERIALS (2021)

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Article Chemistry, Multidisciplinary

Fully Light-Controlled Memory and Neuromorphic Computation in Layered Black Phosphorus

Taimur Ahmed et al.

Summary: This study introduces a neuromorphic imaging element based on a 2D black phosphorus material fully modulated by light, achieving visual memory, wavelength-selective multibit programming, and erasing functions. It has been successfully applied to machine learning for number classification and image recognition.

ADVANCED MATERIALS (2021)

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Article Physics, Applied

An active dendritic tree can mitigate fan-in limitations in superconducting neurons

Bryce A. Primavera et al.

Summary: Superconducting electronic circuits show promise in neuromorphic hardware applications. SQUIDs can be used as an active element for neuron threshold operations, but require maintaining a monotonically increasing response to effectively drive neurons to threshold. Additionally, an active dendritic tree design can reduce the fraction of active synapses needed to drive the neuron to threshold.

APPLIED PHYSICS LETTERS (2021)

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Article Multidisciplinary Sciences

11 TOPS photonic convolutional accelerator for optical neural networks

Xingyuan Xu et al.

Summary: Inspired by biological visual cortex systems, convolutional neural networks extract hierarchical features of raw data, reducing parameter complexity and improving prediction accuracy. Optical neural networks promise faster computing using broad optical bandwidths, with optical vector convolutional accelerators demonstrating efficient image processing capabilities.

NATURE (2021)

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Article Engineering, Electrical & Electronic

Spintronic Computing-in-Memory Architecture Based on Voltage-Controlled Spin-Orbit Torque Devices for Binary Neural Networks

Haotian Wang et al.

Summary: This article proposes a high-throughput CIM architecture for BNN hardware based on the VC-SOT memory device, which enables parallel programming and computing operations. The simulation results validate the parallel programming/computing functionality and show good performance in terms of power consumption and speed.

IEEE TRANSACTIONS ON ELECTRON DEVICES (2021)

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Article Multidisciplinary Sciences

Spontaneous sparse learning for PCM-based memristor neural networks

Dong-Hyeok Lim et al.

Summary: The research team constructed a 39nm 1Gb phase-change memory memristor array and developed a spontaneous sparse learning scheme to improve PCM-based memristor network training. Experimental results show that this method helps enhance the performance and sparsity controllability of the network without requiring additional computation.

NATURE COMMUNICATIONS (2021)

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Article Engineering, Electrical & Electronic

A CMOS-integrated compute-in-memory macro based on resistive random-access memory for AI edge devices

Cheng-Xin Xue et al.

Summary: The development of small, energy-efficient artificial intelligence edge devices has been limited by data transfer requirements between the processor and memory in traditional computing architectures. Non-volatile compute-in-memory (nvCIM) architectures show potential to overcome these limitations, but challenges still remain in developing configurations for high-bit-precision dot-product operations.

NATURE ELECTRONICS (2021)

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Review Multidisciplinary Sciences

Competing memristors for brain-inspired computing

Seung Ju Kim et al.

Summary: Inspired by the human brain, memristor-based neuromorphic computing systems can store multiple values by changing resistance and simulate artificial synapses in brain-inspired computing. Research has shown that these computing systems can learn, infer, and even create using various artificial neural networks.

ISCIENCE (2021)

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Article Physics, Applied

Superconducting neural networks with disordered Josephson junction array synaptic networks and leaky integrate-and-fire loop neurons

Uday S. Goteti et al.

Summary: In this work, a fully coupled randomly disordered recurrent superconducting network is introduced as a new architecture for neuromorphic computing. The network is designed around disordered array synaptic networks using superconducting devices, enabling unsupervised learning and taking advantage of power efficiency, propagation speed, and scalability. Further, individual disordered array neural networks can be coupled together to form a hierarchical architecture of recurrent neural networks, similar to a biological brain.

JOURNAL OF APPLIED PHYSICS (2021)

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Review Optics

Photonics for artificial intelligence and neuromorphic computing

Bhavin J. Shastri et al.

Summary: Research in photonic computing is thriving due to the proliferation of optoelectronic components in photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, with algorithms running on such hardware having the potential to address the growing demand for machine learning and artificial intelligence. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence.

NATURE PHOTONICS (2021)

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Article Multidisciplinary Sciences

Reconfigurable Stochastic neurons based on tin oxide/MoS2 hetero-memristors for simulated annealing and the Boltzmann machine

Xiaodong Yan et al.

Summary: Boltzmann Machines and hardware implementations with stochastic neurons are effective in solving combinatorial optimization problems, but require dynamically tunable statistical parameters. The authors demonstrate a reconfigurable heterogeneous memristive device with tunable stochastic dynamics in output sampling characteristics.

NATURE COMMUNICATIONS (2021)

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Article Chemistry, Multidisciplinary

Giant Ferroelectric Resistance Switching Controlled by a Modulatory Terminal for Low-Power Neuromorphic In-Memory Computing

Fei Xue et al.

Summary: Van der Waals ferroelectric alpha-In2Se3 has demonstrated successful implementation of heterosynaptic plasticity and achieved high resistance switching ratio in memristors for associative heterosynaptic learning. This material has potential applications in energy-efficient computing systems and logic-in-memory computers.

ADVANCED MATERIALS (2021)

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Article Physics, Applied

Optoelectronic intelligence

Jeffrey M. Shainline

Summary: General intelligence involves integrating multiple sources of information into a coherent, adaptive model of the world. Designing hardware for general intelligence requires consideration of principles from neuroscience and very-large-scale integration. Photonics for communication and electronics for computation are complementary and interdependent attributes in large neural systems capable of general intelligence.

APPLIED PHYSICS LETTERS (2021)

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Article Chemistry, Physical

Highly transparent reconfigurable non-volatile multilevel optoelectronic memory for integrated self-powered brain-inspired perception

Mohit Kumar et al.

Summary: This study presents a photodetector with a programmable non-volatile manifold memory, offering significant breakthroughs for optoelectronic memory advancement. The photodetector features high transparency, self-powered characteristics, and customizable photoresponse through electric pulse tuning, as well as versatile bio-synapse features.

NANO ENERGY (2021)

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Article Chemistry, Physical

Reconfigurable optoelectronic memristor for in-sensor computing applications

Tian-Yu Wang et al.

Summary: Optoelectronic memristors, inspired by the human brain and visual system, offer advantages of highly parallel computing and massive interconnection for neuromorphic computing tasks, as well as a promising path for reconfigurable logic operations. By integrating photoelectric perception, storage, and in situ computing functions in optoelectronic memristors array, it significantly improves work efficiency and achieves an accuracy of 86.7% in face image recognition.

NANO ENERGY (2021)

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Article Neurosciences

Toward Learning in Neuromorphic Circuits Based on Quantum Phase Slip Junctions

Ran Cheng et al.

Summary: Superconducting quantum phase slip junctions show promising characteristics for neuromorphic circuits, such as ultra-low energy consumption, high speed, dense integration, and natural spiking responses. They have the potential to be key components in realizing machine learning applications.

FRONTIERS IN NEUROSCIENCE (2021)

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Article Engineering, Electrical & Electronic

A silicon photonic-electronic neural network for fibre nonlinearity compensation

Chaoran Huang et al.

Summary: The study presents a silicon photonic-electronic neural network for compensating fiber nonlinearity in submarine optical-fiber transmission systems. Utilizing photonic devices to process optical signals in the analogue domain, the platform effectively improves signal quality over long distances. With the potential for predicting and enhancing submarine fiber communications, a neural network incorporating photonic components shows promise in overcoming fiber nonlinearity challenges.

NATURE ELECTRONICS (2021)

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Review Engineering, Electrical & Electronic

Transistors based on two-dimensional materials for future integrated circuits

Saptarshi Das et al.

Summary: This paper examines the development of field-effect transistors based on two-dimensional materials for VLSI technology, highlighting the challenges that need to be addressed such as reducing contact resistance, stable doping schemes, mobility engineering, and high-k dielectric integration. The review emphasizes the importance of large-area growth of uniform 2D layers in ensuring low defect density and clean interfaces. Furthermore, potential applications of 2D transistors in various futuristic technologies are discussed.

NATURE ELECTRONICS (2021)

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Article Engineering, Electrical & Electronic

A four-megabit compute-in-memory macro with eight-bit precision based on CMOS and resistive random-access memory for AI edge devices

Je-Min Hung et al.

Summary: Advanced complementary metal-oxide-semiconductor technology and resistive random-access memory have been used to create a high-bit-precision compute-in-memory macro for efficient edge computing. The non-volatile computing-in-memory architecture reduces latency and energy consumption of artificial intelligence computation. The macro offers low latency and high energy efficiency for binary to 8-bit-input-8-bit-weight dot-product operations.

NATURE ELECTRONICS (2021)

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Article Nanoscience & Nanotechnology

Energy-efficient Mott activation neuron for full-hardware implementation of neural networks

Sangheon Oh et al.

Summary: The research introduces an energy-efficient and compact Mott activation neuron based on vanadium dioxide, successfully integrated with a CBRAM crossbar array to achieve efficient in-memory computing. The Mott activation neuron outperforms analogue complementary metal-oxide semiconductor implementations and enables the implementation of activation functions in neural networks.

NATURE NANOTECHNOLOGY (2021)

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Article Optics

Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit

Tiankuang Zhou et al.

Summary: Researchers propose a reconfigurable diffractive processing unit based on the diffraction of light, which supports different neural networks and achieves high model complexity with millions of neurons. By developing an adaptive training approach to overcome system errors, they achieve excellent experimental accuracies for high-speed image and video recognition.

NATURE PHOTONICS (2021)

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Article Multidisciplinary Sciences

Reconfigurable magnonic mode-hybridisation and spectral control in a bicomponent artificial spin ice

Jack C. Gartside et al.

Summary: This article presents a scheme for modifying square artificial spin ice to prepare any ordered vertex state through simple global-field protocols, resulting in rich and distinct microstate-specific magnon spectra. Microstate control allows fine mode-frequency shifting, gap creation and closing, and active mode number selection.

NATURE COMMUNICATIONS (2021)

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Article Chemistry, Multidisciplinary

Purely Electrical Controllable Complete Spin Logic in a Single Magnetic Heterojunction

Xiaonan Zhao et al.

Summary: This study demonstrates the realization of all 16 Boolean logic functions through purely electrical manipulation in a Pt/IrMn/Co/Ru/CoPt heterojunction, achieving internal spin logic. This research is a significant step towards practical electrical programmable spin logic devices.

ADVANCED FUNCTIONAL MATERIALS (2021)

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Article Engineering, Biomedical

A Neuromorphic Proto-Object Based Dynamic Visual Saliency Model With a Hybrid FPGA Implementation

Jamal Lottier Molin et al.

Summary: Computing and attending to salient regions of a visual scene is crucial for both biological and engineered systems performing high-level visual tasks. A neuromorphic dynamic saliency model outperforms existing models in predicting human eye fixations, and a FPGA implementation of the model achieves a significant speedup compared to software implementation, while maintaining comparable results.

IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS (2021)

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Article Engineering, Electrical & Electronic

Efficient Design of Spiking Neural Network With STDP Learning Based on Fast CORDIC

Jiajun Wu et al.

Summary: This study presents a novel SNN design using fast CORDIC algorithm for efficient STDP learning, with a system design and evaluation method to find optimal CORDIC type and precision, resulting in a reconfigurable SNN design based on fast-convergence CORDIC. The proposed design outperforms state-of-the-art methods in terms of learning speed and energy efficiency on the MNIST benchmark.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS (2021)

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Article Chemistry, Physical

Halide Perovskites for Memristive Data Storage and Artificial Synapses

Kyung Ju Kwak et al.

Summary: Halide perovskites are noted for their exotic properties and have shown promise for use in memristors, particularly in resistive switching memory devices and artificial synapses for neuromorphic computing. Current research focuses on state-of-the-art perovskite-based memristive devices and opportunities to improve their commercial viability.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2021)

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Article Chemistry, Multidisciplinary

Reconfigurable MoS2 Memtransistors for Continuous Learning in Spiking Neural Networks

Jiangtan Yuan et al.

Summary: This study introduces a memtransistor with gate-tunable dynamic learning behavior, enhancing reconfigurability of device response for diverse learning curves and simplified plasticity. Inspired by biological systems, gate pulses are used to modulate unsupervised and continuous learning in simulated neural networks.

NANO LETTERS (2021)

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Article Chemistry, Multidisciplinary

CMOS-Compatible Protonic Programmable Resistor Based on Phosphosilicate Glass Electrolyte for Analog Deep Learning

Murat Onen et al.

Summary: Ion intercalation based programmable resistors utilizing phosphosilicate glass as the proton solid electrolyte layer have shown desirable modulation characteristics for nanoscale analog crossbar processors. This technology represents promising candidates for monolithic CMOS integration due to its high modulation speed, low energy consumption, and enhanced endurance.

NANO LETTERS (2021)

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Article Multidisciplinary Sciences

The rise of intelligent matter

C. Kaspar et al.

Summary: Artificial Intelligence is driving the development of unconventional computing paradigms inspired by the capabilities of the brain, aiming to create intelligent matter capable of non-localized information processing. This matter will interact with the environment, adapt its structure internally, and store information efficiently.

NATURE (2021)

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Article Multidisciplinary Sciences

Low-temperature emergent neuromorphic networks with correlated oxide devices

S. Uday Gotetia et al.

Summary: Neuromorphic computing is a new approach to simulate the behavior of neurons and synapses, where networks based on superconducting and Mott-insulating oxides can achieve multiple emergent states.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2021)

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Article Multidisciplinary Sciences

2D materials-based homogeneous transistor-memory architecture for neuromorphic hardware

Lei Tong et al.

Summary: In this study, a novel device architecture combining tungsten diselenide and lithium niobate is proposed for use as a nonlinear transistor and nonvolatile memory. Based on this homogeneous architecture, an integrated system for binary classification and ternary content-addressable memory design for neuromorphic hardware is also investigated.

SCIENCE (2021)

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Article Chemistry, Multidisciplinary

Reconfigurable 2D WSe2-Based Memtransistor for Mimicking Homosynaptic and Heterosynaptic Plasticity

Guanglong Ding et al.

Summary: In this study, a WSe2-based memtransistor is fabricated for mimicking both homosynaptic and heterosynaptic plasticity. By optimizing input conditions, the number and linearity of resistance states can be improved. The device offers highly adjustable and reconfigurable characteristics, providing more freedom for tuning synaptic weight, optimizing circuit design, and building artificial neuromorphic computing systems.

SMALL (2021)

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Article Neurosciences

Considerations for Neuromorphic Supercomputing in Semiconducting and Superconducting Optoelectronic Hardware

Bryce A. Primavera et al.

Summary: The article discusses the necessity for large-scale spiking neuromorphic systems to be co-optimized for communication and computation, leading to the proposal for optoelectronic neuromorphic platforms. Two possible paths towards achieving this vision are considered: a semiconductor platform and a superconducting approach. The article discusses available devices, scaling potential, and key metrics for each platform.

FRONTIERS IN NEUROSCIENCE (2021)

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Article Engineering, Electrical & Electronic

Logic gates based on neuristors made from two-dimensional materials

Huawei Chen et al.

Summary: By leveraging the intrinsic polarity of two-dimensional materials, single neuristors can function as XNOR, NOR, OR, and AND logic gates, showcasing a versatile capability for logic operations.

NATURE ELECTRONICS (2021)

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Article Chemistry, Multidisciplinary

Bi-mode electrolyte-gated synaptic transistor via additional ion doping and its application to artificial nociceptors

Rengjian Yu et al.

Summary: The proposed bi-mode electrolyte-gated synaptic transistor allows for volatile and non-volatile behavior by tuning dynamic processes, leading to the discovery of a third state in the transfer curves. This unique property enables the realization of an artificial nociceptor and a haptic sensory system, showing promising prospects in achieving multiple-mode integrated devices for artificial intelligence development.

MATERIALS HORIZONS (2021)

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Correction Physics, Applied

Physics for neuromorphic computing (vol 2, pg 499, 2020)

Danijela Markovic et al.

NATURE REVIEWS PHYSICS (2021)

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Article Materials Science, Multidisciplinary

Band-tailored van der Waals heterostructure for multilevel memory and artificial synapse

Yanan Wang et al.

Summary: The study demonstrates a three-terminal floating gate device based on two-dimensional van der Waals heterostructure, showing great potential for next-generation nonvolatile and multilevel data storage memory. The device exhibits large on/off current ratio, good retention and robust endurance, can function as an artificial synapse, and achieves low energy consumption. High linearity and conductance ratio in long-term potentiation and depression further contribute to high pattern recognition accuracy in artificial neural network simulation. The proposed device with band engineering can promote the development of energy-efficient memory and neuromorphic devices in the future.

INFOMAT (2021)

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Article Computer Science, Information Systems

Low-Energy Acceleration of Binarized Convolutional Neural Networks Using a Spin Hall Effect Based Logic-in-Memory Architecture

Ashkan Samiee et al.

Summary: Deep Learning (DL) offers high accuracy performance in tasks such as image recognition and intelligent behavior learning, but requires high computational and energy consumption demands. Hardware implementations can substantially improve the performance of DL. This paper introduces a new Spintronic Logic-in-Memory (S-LIM) XNOR neural network design, which achieves significant improvements in energy consumption, throughput, and accuracy compared to the state-of-the-art hardware implementations.

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING (2021)

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Article Computer Science, Hardware & Architecture

Superconductor Computing for Neural Networks

Koki Ishida et al.

Summary: The superconductor single-flux-quantum (SFQ) logic family is considered a promising solution for the post-Moore era, but there has been little progress in designing convincing SFQ-based architectural units. This article introduces SuperNPU, a high-performance SFQ-based NPU that outperforms conventional NPUs in terms of computing performance and power efficiency.

IEEE MICRO (2021)

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Article Materials Science, Multidisciplinary

An artificial olfactory inference system based on memristive devices

Tong Wang et al.

Summary: An artificial olfactory inference system based on memristive devices has been developed to classify four gases with 10 different concentrations, achieving a high accuracy of 95%. Three strategies are applied to reduce the extracted features from the reservoir computing system in order to reduce device number and power consumption.

INFOMAT (2021)

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Review Physics, Applied

Chiral spintronics

See-Hun Yang et al.

Summary: Spin currents generated by chiral structures can manipulate chiral topological magnetic excitations through chirality acquired in a spin-orbit interaction. When chiral domain walls form composite walls via antiferromagnetic exchange coupling, novel phenomena such as exchange coupling torque and domain wall drag are observed. This review discusses the recent progress in spin currents derived from or acting on chiral structures and their potential applications.

NATURE REVIEWS PHYSICS (2021)

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Article Nanoscience & Nanotechnology

Observation of single-defect memristor in an MoS2 atomic sheet

Saban M. Hus et al.

Summary: Non-volatile resistive switching, also known as memristor effect, has emerged as an important concept in high-density information storage, computing, and reconfigurable systems. Recent discoveries in two-dimensional monolayers have refuted previous beliefs and added a new dimension to materials science.

NATURE NANOTECHNOLOGY (2021)

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Article Materials Science, Multidisciplinary

Flexible3Dmemristor array for binary storage and multi-states neuromorphic computing applications

Tian-Yu Wang et al.

Summary: This paper explores a 3D flexible memristors network fabricated via low-temperature atomic layer deposition, demonstrating its potential for high-density storage and neuromorphic computing. The network exhibits typical bipolar switching characteristics and multibit storage capability, enhancing storage density. The study indicates the significant potential of the 3D flexible memristors network in high-performance, high-density, and reliable wearable neuromorphic computing systems.

INFOMAT (2021)

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Article Nanoscience & Nanotechnology

Design and characterization of superconducting nanowire-based processors for acceleration of deep neural network training

Murat Onen et al.

NANOTECHNOLOGY (2020)

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Article Chemistry, Multidisciplinary

Optically Modulated Threshold Switching in Core-Shell Quantum Dot Based Memristive Device

Junjie Wang et al.

ADVANCED FUNCTIONAL MATERIALS (2020)

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Article Chemistry, Multidisciplinary

Reconfigurable Logic-in-Memory and Multilingual Artificial Synapses Based on 2D Heterostructures

Xiong Xiong et al.

ADVANCED FUNCTIONAL MATERIALS (2020)

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Article Chemistry, Multidisciplinary

Retina-Inspired Carbon Nitride-Based Photonic Synapses for Selective Detection of UV Light

Hea-Lim Park et al.

ADVANCED MATERIALS (2020)

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Article Chemistry, Multidisciplinary

Magnetic Domain Wall Based Synaptic and Activation Function Generator for Neuromorphic Accelerators

Saima A. Siddiqui et al.

NANO LETTERS (2020)

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Article Multidisciplinary Sciences

Heterogeneous integration of single-crystalline complex-oxide membranes

Hyun S. Kum et al.

NATURE (2020)

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Article Multidisciplinary Sciences

Ultrafast machine vision with 2D material neural network image sensors

Lukas Mennel et al.

NATURE (2020)

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Article Multidisciplinary Sciences

Fully hardware-implemented memristor convolutional neural network

Peng Yao et al.

NATURE (2020)

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Review Nanoscience & Nanotechnology

Neuromorphic nanoelectronic materials

Vinod K. Sangwan et al.

NATURE NANOTECHNOLOGY (2020)

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Review Nanoscience & Nanotechnology

Resistive switching materials for information processing

Zhongrui Wang et al.

NATURE REVIEWS MATERIALS (2020)

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Article Multidisciplinary Sciences

Ultrasensitive and ultrathin phototransistors and photonic synapses using perovskite quantum dots grown from graphene lattice

Basudev Pradhan et al.

SCIENCE ADVANCES (2020)

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Article Chemistry, Multidisciplinary

Bidirectional All-Optical Synapses Based on a 2D Bi2O2Se/Graphene Hybrid Structure for Multifunctional Optoelectronics

Chia-Ming Yang et al.

ADVANCED FUNCTIONAL MATERIALS (2020)

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Review Chemistry, Multidisciplinary

Current-Induced Spin-Orbit Torques for Spintronic Applications

Jeongchun Ryu et al.

ADVANCED MATERIALS (2020)

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Review Physics, Applied

A comprehensive review on emerging artificial neuromorphic devices

Jiadi Zhu et al.

APPLIED PHYSICS REVIEWS (2020)

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Article Multidisciplinary Sciences

Sub-nanosecond memristor based on ferroelectric tunnel junction

Chao Ma et al.

NATURE COMMUNICATIONS (2020)

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Article Multidisciplinary Sciences

State dependence and temporal evolution of resistance in projected phase change memory

Benedikt Kersting et al.

SCIENTIFIC REPORTS (2020)

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Article Physics, Applied

Energy-Efficient Stochastic Computing with Superparamagnetic Tunnel Junctions

Matthew W. Daniels et al.

PHYSICAL REVIEW APPLIED (2020)

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Article Multidisciplinary Sciences

Permafrost thawing puts the frozen carbon at risk over the Tibetan Plateau

Taihua Wang et al.

SCIENCE ADVANCES (2020)

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Article Multidisciplinary Sciences

Current-driven magnetic domain-wall logic

Zhaochu Luo et al.

NATURE (2020)

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Article Chemistry, Multidisciplinary

Nonvolatile Electrically Reconfigurable Integrated Photonic Switch Enabled by a Silicon PIN Diode Heater

Jiajiu Zheng et al.

ADVANCED MATERIALS (2020)

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Review Optics

Principles, fundamentals, and applications of programmable integrated photonics

Daniel Perez et al.

ADVANCES IN OPTICS AND PHOTONICS (2020)

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Article Nanoscience & Nanotechnology

Controlled Memory and Threshold Switching Behaviors in a Heterogeneous Memristor for Neuromorphic Computing

Hao-Yang Li et al.

ADVANCED ELECTRONIC MATERIALS (2020)

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Article Nanoscience & Nanotechnology

Complementary Lateral-Spin-Orbit Building Blocks for Programmable Logic and In-Memory Computing

Nan Zhang et al.

ADVANCED ELECTRONIC MATERIALS (2020)

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Article Engineering, Electrical & Electronic

Fabrication of carbon nanotube field-effect transistors in commercial silicon manufacturing facilities

Mindy D. Bishop et al.

NATURE ELECTRONICS (2020)

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Article Engineering, Electrical & Electronic

Reconfigurable logic and neuromorphic circuits based on electrically tunable two-dimensional homojunctions

Chen Pan et al.

NATURE ELECTRONICS (2020)

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Article Engineering, Electrical & Electronic

Reconfigurable frequency multiplication with a ferroelectric transistor

Halid Mulaosmanovic et al.

NATURE ELECTRONICS (2020)

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Review Engineering, Electrical & Electronic

Neuro-inspired computing chips

Wenqiang Zhang et al.

NATURE ELECTRONICS (2020)

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Article Chemistry, Multidisciplinary

Large-Scale and Robust Multifunctional Vertically Aligned MoS2Photo-Memristors

Kamalakannan Ranganathan et al.

ADVANCED FUNCTIONAL MATERIALS (2020)

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Article Chemistry, Multidisciplinary

Filament-Free Bulk Resistive Memory Enables Deterministic Analogue Switching

Yiyang Li et al.

ADVANCED MATERIALS (2020)

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Article Chemistry, Multidisciplinary

Memristors Based on 2D Materials as an Artificial Synapse for Neuromorphic Electronics

Woong Huh et al.

ADVANCED MATERIALS (2020)

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Review Nanoscience & Nanotechnology

Two-dimensional materials for next-generation computing technologies

Chunsen Liu et al.

NATURE NANOTECHNOLOGY (2020)

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Article Nanoscience & Nanotechnology

Power-efficient neural network with artificial dendrites

Xinyi Li et al.

NATURE NANOTECHNOLOGY (2020)

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Article Chemistry, Multidisciplinary

Superconducting Nanowire Spiking Element for Neural Networks

E. Toomey et al.

NANO LETTERS (2020)

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Article Multidisciplinary Sciences

Third-order nanocircuit elements for neuromorphic engineering

Suhas Kumar et al.

NATURE (2020)

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Article Engineering, Electrical & Electronic

Wafer-scale integration of two-dimensional materials in high-density memristive crossbar arrays for artificial neural networks

Shaochuan Chen et al.

NATURE ELECTRONICS (2020)

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Article Automation & Control Systems

Memristive Devices for New Computing Paradigms

In Hyuk Im et al.

ADVANCED INTELLIGENT SYSTEMS (2020)

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Article Multidisciplinary Sciences

Logic-in-memory based on an atomically thin semiconductor

Guilherme Migliato Marega et al.

NATURE (2020)

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Review Chemistry, Multidisciplinary

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