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Journal articles on the topic 'Neuromorphic technologies/devices'

Author: Grafiati
Published: 15 June 2024
Last updated: 31 July 2025

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1

Orii, Yasumitsu, Akihiro Horibe, Kuniaki Sueoka, et al. "PERSPECTIVE ON REQUIRED PACKAGING TECHNOLOGIES FOR NEUROMORPHIC DEVICES." International Symposium on Microelectronics 2015, no. 1 (2015): 000561–66. http://dx.doi.org/10.4071/isom-2015-tha15.

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Cognitive computing has capability of machine learning, recognition and proposal. It is essential to make human life richer, more productive and more intelligent. For the realization of the cognitive computing, an efficient and scalable non-von Neumann architecture inspired by the human brain structure has been developed and a device which demonstrates the concept was also built. This device mimics the signal processing of the human brain, packing one million neuron circuits in 4,096 cores. It consumes almost 1,000 times less energy per event compared with a state-of-the-art multiprocessor. Ho
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Diao, Yu, Yaoxuan Zhang, Yanran Li, and Jie Jiang. "Metal-Oxide Heterojunction: From Material Process to Neuromorphic Applications." Sensors 23, no. 24 (2023): 9779. http://dx.doi.org/10.3390/s23249779.

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As technologies like the Internet, artificial intelligence, and big data evolve at a rapid pace, computer architecture is transitioning from compute-intensive to memory-intensive. However, traditional von Neumann architectures encounter bottlenecks in addressing modern computational challenges. The emulation of the behaviors of a synapse at the device level by ionic/electronic devices has shown promising potential in future neural-inspired and compact artificial intelligence systems. To address these issues, this review thoroughly investigates the recent progress in metal-oxide heterostructure
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3

Milo, Valerio, Gerardo Malavena, Christian Monzio Compagnoni, and Daniele Ielmini. "Memristive and CMOS Devices for Neuromorphic Computing." Materials 13, no. 1 (2020): 166. http://dx.doi.org/10.3390/ma13010166.

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Neuromorphic computing has emerged as one of the most promising paradigms to overcome the limitations of von Neumann architecture of conventional digital processors. The aim of neuromorphic computing is to faithfully reproduce the computing processes in the human brain, thus paralleling its outstanding energy efficiency and compactness. Toward this goal, however, some major challenges have to be faced. Since the brain processes information by high-density neural networks with ultra-low power consumption, novel device concepts combining high scalability, low-power operation, and advanced comput
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Jha, Rashmi, Vamshi Kiran Kiran Gogi, and Siddharth Barve. "(Invited) Novel Neuromorphic Computing Paradigms Enabled By Emerging Memory Devices." ECS Meeting Abstracts MA2024-01, no. 57 (2024): 3011. http://dx.doi.org/10.1149/ma2024-01573011mtgabs.

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Implementation of Artificial Intelligence and Machine Learning algorithms on conventional Von Neumann computing architectures are crippled by the memory-wall bottleneck. To overcome these issues, novel computing architectures with high-bandwidth memories, in-memory computing, and near-memory computing capabilities are being developed. Almost all of these architectures will benefit from high-density on-chip non-volatile memories, offered by the emerging non-volatile memory devices. Additionally, emerging memory devices offer rich device physics that can be leveraged for the implementation of no
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Huang, Tianci, Yuxuan Wang, Zhihan Jin, et al. "A Review of Nanowire Devices Applied in Simulating Neuromorphic Computing." Nanomaterials 15, no. 10 (2025): 724. https://doi.org/10.3390/nano15100724.

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With the rapid advancement of artificial intelligence and machine learning technologies, the demand for enhanced device computing capabilities has significantly increased. Neuromorphic computing, an emerging computational paradigm inspired by the human brain, has garnered growing attention as a promising research frontier. Inspired by the human brain’s functionality, this technology mimics the behavior of neurons and synapses to enable efficient, low-power computing. Unlike conventional digital systems, this approach offers a potentially superior alternative. This article delves into the appli
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Meng, Xiaohan, Runsheng Gao, Xiaojian Zhu, and Run-Wei Li. "Ion-modulation optoelectronic neuromorphic devices: mechanisms, characteristics, and applications." Journal of Semiconductors 46, no. 2 (2025): 021402. https://doi.org/10.1088/1674-4926/24100025.

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Abstract The traditional von Neumann architecture faces inherent limitations due to the separation of memory and computation, leading to high energy consumption, significant latency, and reduced operational efficiency. Neuromorphic computing, inspired by the architecture of the human brain, offers a promising alternative by integrating memory and computational functions, enabling parallel, high-speed, and energy-efficient information processing. Among various neuromorphic technologies, ion-modulated optoelectronic devices have garnered attention due to their excellent ionic tunability and the
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Kim, So-Yeon. "Operating Mechanism Principles and Advancements for Halide Perovskite-Based Memristors and Neuromorphic Devices." Journal of Physical Chemistry Letters 15 (September 30, 2024): 10087–103. https://doi.org/10.1021/acs.jpclett.4c02170.

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With the advent of the generation of artificial intelligence (AI) based on big data-processing technologies, next-generation memristor and memristive neuromorphic devices have been actively studied with great interest to overcome the von Neumann bottleneck limits. Among various candidates, halide perovskites (HPs) have been in the spotlight as potential candidates for these devices due to their unique switching characteristics with low energy consumption and flexible integration compatibility across various sources for scalability. We outline the characteristics and operating principles of HP-
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Abbas, Haider, Jiayi Li, and Diing Shenp Ang. "Conductive Bridge Random Access Memory (CBRAM): Challenges and Opportunities for Memory and Neuromorphic Computing Applications." Micromachines 13, no. 5 (2022): 725. http://dx.doi.org/10.3390/mi13050725.

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Due to a rapid increase in the amount of data, there is a huge demand for the development of new memory technologies as well as emerging computing systems for high-density memory storage and efficient computing. As the conventional transistor-based storage devices and computing systems are approaching their scaling and technical limits, extensive research on emerging technologies is becoming more and more important. Among other emerging technologies, CBRAM offers excellent opportunities for future memory and neuromorphic computing applications. The principles of the CBRAM are explored in depth
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9

Allwood, Dan A., Matthew O. A. Ellis, David Griffin, et al. "A perspective on physical reservoir computing with nanomagnetic devices." Applied Physics Letters 122, no. 4 (2023): 040501. http://dx.doi.org/10.1063/5.0119040.

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Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for training advanced models are unsustainable. One promising way to address this pressing issue is by developing low-energy neuromorphic hardware that directly supports the algorithm's requirements. The intrinsic non-volatility, non-linearity, and memory of spintronic devices make them appealing candidates for neuromorphic devices. Here, we focus on the reservoir c
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Della Rocca, Mattia. "Of the Artistic Nude and Technological Behaviorism." Nuncius 32, no. 2 (2017): 376–411. http://dx.doi.org/10.1163/18253911-03202006.

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Neuromorphic technologies lie at the core of 21st century neuroscience, especially in the “big brain science” projects started in 2013 – i.e. the BRAIN Initiative and the Human Brain Project. While neuromorphism and the “reverse engineering” of the brain are often presented as a “methodological revolution” in the brain sciences, these concepts have a long history which is strongly interconnected with the developments in neuroscience and the related field of bioengineering since the end of World War II. In this paper I provide a short review of the first generation of “neuromorphic devices” cre
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Vinuesa, Guillermo, Hector Garcia, Salvador Duenas, and Helena Castan. "(Invited) Thermoelectric Analysis of Dielectric Materials Properties for Neuromorphic Technologies." ECS Meeting Abstracts MA2024-01, no. 21 (2024): 1294. http://dx.doi.org/10.1149/ma2024-01211294mtgabs.

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The effort made in recent years in the development of new memories has led to a significant advance in emerging technologies, which have proven their usefulness not only in the field of memories, but also to obtain devices that perform an artificial synapse and thus emulate biological neurons. Among the novel concepts, resistive-switching random access memory (RRAM), or memristive device, has attracted a great deal of interest for its ability to store multiple states. In fact, its operation is based on the so-called resistive switching, which consists of the formation of conductive filaments t
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Jha, Vagish Kumar. "Sustainable Perovskite Multiferroic Materials for Memristive Memory and Neuromorphic Computing Devices." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 1552–64. https://doi.org/10.22214/ijraset.2025.70439.

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Perovskite multiferroic materials, characterised by their unique coupling of ferroelectricity, magnetism, and additional functionalities, have emerged as promising candidates for next-generation electronic devices. Their potential is particularly significant in memristive memory and neuromorphic computing, where energy efficiency, multifunctionality, and compactness are critical. This abstract explores the role of perovskite multiferroics in enabling sustainable, high-performance memory and computing devices. Emphasis is placed on materials like BiFeO3 and lead-free alternatives, demonstrating
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Kurshan, Eren, Hai Li, Mingoo Seok, and Yuan Xie. "A Case for 3D Integrated System Design for Neuromorphic Computing and AI Applications." International Journal of Semantic Computing 14, no. 04 (2020): 457–75. http://dx.doi.org/10.1142/s1793351x20500063.

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Over the last decade, artificial intelligence (AI) has found many applications areas in the society. As AI solutions have become more sophistication and the use cases grew, they highlighted the need to address performance and energy efficiency challenges faced during the implementation process. To address these challenges, there has been growing interest in neuromorphic chips. Neuromorphic computing relies on non von Neumann architectures as well as novel devices, circuits and manufacturing technologies to mimic the human brain. Among such technologies, three-dimensional (3D) integration is an
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Hajtó, Dániel, Ádám Rák, and György Cserey. "Robust Memristor Networks for Neuromorphic Computation Applications." Materials 12, no. 21 (2019): 3573. http://dx.doi.org/10.3390/ma12213573.

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One of the main obstacles for memristors to become commonly used in electrical engineering and in the field of artificial intelligence is the unreliability of physical implementations. A non-uniform range of resistance, low mass-production yield and high fault probability during operation are disadvantages of the current memristor technologies. In this article, the authors offer a solution for these problems with a circuit design, which consists of many memristors with a high operational variance that can form a more robust single memristor. The proposition is confirmed by physical device meas
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15

Sueoka, Brandon, and Feng Zhao. "Memristive synaptic device based on a natural organic material—honey for spiking neural network in biodegradable neuromorphic systems." Journal of Physics D: Applied Physics 55, no. 22 (2022): 225105. http://dx.doi.org/10.1088/1361-6463/ac585b.

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Abstract Spiking neural network (SNN) in future neuromorphic architectures requires hardware devices to be not only capable of emulating fundamental functionalities of biological synapse such as spike-timing dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP), but also biodegradable to address current ecological challenges of electronic waste. Among different device technologies and materials, memristive synaptic devices based on natural organic materials have emerged as the favourable candidate to meet these demands. The metal–insulator-metal structure is analogous to biolo
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16

Covi, Erika, Halid Mulaosmanovic, Benjamin Max, Stefan Slesazeck, and Thomas Mikolajick. "Ferroelectric-based synapses and neurons for neuromorphic computing." Neuromorphic Computing and Engineering 2, no. 1 (2022): 012002. http://dx.doi.org/10.1088/2634-4386/ac4918.

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Abstract The shift towards a distributed computing paradigm, where multiple systems acquire and elaborate data in real-time, leads to challenges that must be met. In particular, it is becoming increasingly essential to compute on the edge of the network, close to the sensor collecting data. The requirements of a system operating on the edge are very tight: power efficiency, low area occupation, fast response times, and on-line learning. Brain-inspired architectures such as spiking neural networks (SNNs) use artificial neurons and synapses that simultaneously perform low-latency computation and
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17

Zhong, Zekai, Songjie Yuan, Ying Tang, and Haodong Tang. "P‐11.4: PbS Quantum Dots for Memristive Devices." SID Symposium Digest of Technical Papers 56, S1 (2025): 1483–84. https://doi.org/10.1002/sdtp.19121.

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This study investigates PbS Quantum Dots (PbS QDs) as the active material in memristive devices. The devices exhibit hysteretic switching behavior, demonstrating their potential for non‐volatile memory and neuromorphic computing. The charge trapping and migration within the PbS QD layer govern the resistive switching, offering advantages such as low voltage operation and high stability. These findings highlight the promise of PbS QDs in advanced memory technologies.
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18

Schneider, Michael, Emily Toomey, Graham Rowlands, Jeff Shainline, Paul Tschirhart, and Ken Segall. "SuperMind: a survey of the potential of superconducting electronics for neuromorphic computing." Superconductor Science and Technology 35, no. 5 (2022): 053001. http://dx.doi.org/10.1088/1361-6668/ac4cd2.

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Abstract Neuromorphic computing is a broad field that uses biological inspiration to address computing design. It is being pursued in many hardware technologies, both novel and conventional. We discuss the use of superconductive electronics for neuromorphic computing and why they are a compelling technology for the design of neuromorphic computing systems. One example is the natural spiking behavior of Josephson junctions and the ability to transmit short voltage spikes without the resistive capacitive time constants that typically hinder spike-based computing. We review the work that has been
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19

Xiong, Shan, Xue Liang, Xiangjun Xing, and Yan Zhou. "Physical neural network using skyrmion-based spin torque nano-oscillators." Journal of Physics: Conference Series 2803, no. 1 (2024): 012044. http://dx.doi.org/10.1088/1742-6596/2803/1/012044.

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Abstract Due to physical limitations on the miniaturization of traditional electronic devices, architectures based on emerging principles have become the focus of current research to meet the needs of rapidly developing information technologies in the post-Moore era. Neuromorphic devices hold huge potential for use in future artificial intelligence (AI) chips beyond conventional architectures. Benefiting from a wealth of nonlinear dynamic characteristics of spin torque nano-oscillators (STNOs), studies of neuromorphic computations and their applications based on STNOs are attracting growing at
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Jeon, Young Pyo, Yongbin Bang, Hak Ji Lee, Eun Jung Lee, Young Joon Yoo, and Sang Yoon Park. "Short-Term to Long-Term Plasticity Transition Behavior of Memristive Devices with Low Power Consumption via Facilitating Ionic Drift of Implanted Lithium." Electronics 10, no. 21 (2021): 2564. http://dx.doi.org/10.3390/electronics10212564.

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Recent innovations in information technology have encouraged extensive research into the development of future generation memory and computing technologies. Memristive devices based on resistance switching are not only attractive because of their multi-level information storage, but they also display fascinating neuromorphic behaviors. We investigated the basic human brain’s learning and memory algorithm for “memorizing” as a feature for memristive devices based on Li-implanted structures with low power consumption. A topographical and surface chemical functionality analysis of an Li:ITO subst
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Yang, Heejun. "(Invited) Energy Intelligent Computing Devices Based on 2D Materials." ECS Meeting Abstracts MA2024-02, no. 35 (2024): 2464. https://doi.org/10.1149/ma2024-02352464mtgabs.

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Despite the long and crucial role of traditional solid-state physics for current silicon-based technologies, next generation neuromorphic, non-volatile memory, and energy devices that are key components in the era of the internet of things (IOT) require novel working principles with quantum physics emerging in low-dimensional materials1-4. The main research direction for the future devices is to realize ‘ultralow device operation energy’, ‘ultrahigh device operation speed’, and ‘large-scale device integration (up to 1015)’, which calls for exploring diverse quantum phenomena in low dimensional
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Bisquert, Juan. "Recent advances in fluidic neuromorphic computing." Applied Physics Reviews 12, no. 021309 (2025): 1–30. https://doi.org/10.1063/5.0235267.

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Human brain is capable of optimizing information flow and processing without energy-intensive data shuttling between processor andmemory. At the core of this unique capability are billions of neurons connected through trillions of synapses—basic processing units ofthe brain. The action potentials or “spikes” based temporal processing using the regulated flow of ions across ion channels in neuron cellsallows sparse and efficient transmission of data in the brain. Emerging systems based on confined fluidic systems have provided a frameworkfor a new type of neuromorphic computin
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Jha, Rashmi. "Emerging Memory Devices Beyond Conventional Data Storage: Paving the Path for Energy-Efficient Brain-Inspired Computing." Electrochemical Society Interface 32, no. 1 (2023): 49–51. http://dx.doi.org/10.1149/2.f10231if.

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The current state of neuromorphic computing broadly encompasses domain-specific computing architectures designed to accelerate machine learning (ML) and artificial intelligence (AI) algorithms. As is well known, AI/ML algorithms are limited by memory bandwidth. Novel computing architectures are necessary to overcome this limitation. There are several options that are currently under investigation using both mature and emerging memory technologies. For example, mature memory technologies such as high-bandwidth memories (HBMs) are integrated with logic units on the same die to bring memory close
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Im, Jisung, Sangyeon Pak, Sung-Yun Woo, Wonjun Shin, and Sung-Tae Lee. "Flash Memory for Synaptic Plasticity in Neuromorphic Computing: A Review." Biomimetics 10, no. 2 (2025): 121. https://doi.org/10.3390/biomimetics10020121.

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The rapid expansion of data has made global access easier, but it also demands increasing amounts of energy for data storage and processing. In response, neuromorphic electronics, inspired by the functionality of biological neurons and synapses, have emerged as a growing area of research. These devices enable in-memory computing, helping to overcome the “von Neumann bottleneck”, a limitation caused by the separation of memory and processing units in traditional von Neumann architecture. By leveraging multi-bit non-volatility, biologically inspired features, and Ohm’s law, synaptic devices show
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Khajooei, Arash, Mohammad (Behdad) Jamshidi, and Shahriar B. Shokouhi. "A Super-Efficient TinyML Processor for the Edge Metaverse." Information 14, no. 4 (2023): 235. http://dx.doi.org/10.3390/info14040235.

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Although the Metaverse is becoming a popular technology in many aspects of our lives, there are some drawbacks to its implementation on clouds, including long latency, security concerns, and centralized infrastructures. Therefore, designing scalable Metaverse platforms on the edge layer can be a practical solution. Nevertheless, the realization of these edge-powered Metaverse ecosystems without high-performance intelligent edge devices is almost impossible. Neuromorphic engineering, which employs brain-inspired cognitive architectures to implement neuromorphic chips and Tiny Machine Learning (
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Gao, Zhan, Yan Wang, Ziyu Lv, et al. "Ferroelectric coupling for dual-mode non-filamentary memristors." Applied Physics Reviews 9, no. 2 (2022): 021417. http://dx.doi.org/10.1063/5.0087624.

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Memristive devices and systems have emerged as powerful technologies to fuel neuromorphic chips. However, the traditional two-terminal memristor still suffers from nonideal device characteristics, raising challenges for its further application in versatile biomimetic emulation for neuromorphic computing owing to insufficient control of filament forming for filamentary-type cells and a transport barrier for interfacial switching cells. Here, we propose three-terminal memristors with a top-gate field-effect geometry by employing a ferroelectric material, poly(vinylidene fluoride–trifluoroethylen
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Chiappalone, Michela, Vinicius R. Cota, Marta Carè, et al. "Neuromorphic-Based Neuroprostheses for Brain Rewiring: State-of-the-Art and Perspectives in Neuroengineering." Brain Sciences 12, no. 11 (2022): 1578. http://dx.doi.org/10.3390/brainsci12111578.

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Neuroprostheses are neuroengineering devices that have an interface with the nervous system and supplement or substitute functionality in people with disabilities. In the collective imagination, neuroprostheses are mostly used to restore sensory or motor capabilities, but in recent years, new devices directly acting at the brain level have been proposed. In order to design the next-generation of neuroprosthetic devices for brain repair, we foresee the increasing exploitation of closed-loop systems enabled with neuromorphic elements due to their intrinsic energy efficiency, their capability to
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Sheetal Kaul. "Convergence of low-power processing technologies and telemedicine applications: Enabling the future of remote healthcare." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 803–11. https://doi.org/10.30574/wjaets.2025.15.3.1010.

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Telemedicine has revolutionized healthcare delivery by enabling remote patient care and continuous monitoring, with adoption dramatically accelerating during the COVID-19 pandemic. At the core of this transformation are low-power processors, which address critical energy constraints in wearable health devices, portable diagnostics, and remote monitoring systems. This article explores the symbiotic relationship between low-power processing technologies and telemedicine applications, examining how energy-efficient architectures, ultra-low-power microcontrollers, and specialized AI accelerators e
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Banerjee, Writam. "Challenges and Applications of Emerging Nonvolatile Memory Devices." Electronics 9, no. 6 (2020): 1029. http://dx.doi.org/10.3390/electronics9061029.

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Emerging nonvolatile memory (eNVM) devices are pushing the limits of emerging applications beyond the scope of silicon-based complementary metal oxide semiconductors (CMOS). Among several alternatives, phase change memory, spin-transfer torque random access memory, and resistive random-access memory (RRAM) are major emerging technologies. This review explains all varieties of prototype and eNVM devices, their challenges, and their applications. A performance comparison shows that it is difficult to achieve a “universal memory” which can fulfill all requirements. Compared to other emerging alte
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Rodrigues, Julia, Michael Liang, Pranav Choori, and Ethan Ahn. "Re-Looking at Silicon Oxide for Neuromorphic Computing." ECS Meeting Abstracts MA2025-01, no. 63 (2025): 3079. https://doi.org/10.1149/ma2025-01633079mtgabs.

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Backgrounds/Introduction: The convergence of neuromorphic computing with IoT will drive intelligence at the edge, bringing a completely new landscape for emerging materials technologies. The memory devices in the IoT network need to be designed and manufactured in a sustainable way while fulfilling the specific performance requirements such as low power and immunity to read disturbance. In this work, sputter-deposited silicon oxide (SiOx) was investigated as a promising material platform for conductive-bridge RAM (CBRAM) and resistive RAM (RRAM). In the field, metal oxides have been the most w
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El-Atab, Nazek. "(Invited) 2D Materials Based in-Memory Optical Sensing and Computing for Bionic Vision." ECS Meeting Abstracts MA2025-01, no. 37 (2025): 1776. https://doi.org/10.1149/ma2025-01371776mtgabs.

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The digital revolution, driven by advancements in IoT and AI, is reshaping industries and societies. As the volume of data generated by IoT devices continues to grow exponentially, the need for efficient and intelligent data processing at the edge becomes increasingly critical [1-3]. In-memory computing and in-sensor computing offer promising solutions by integrating memory, logic, and sensing functions into a single device. This integration enables devices to process data locally, reducing reliance on cloud-based systems and improving latency and energy efficiency. Furthermore, these technolo
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Han, Youngmin, Juhyung Seo, Dong Hyun Lee, and Hocheon Yoo. "IGZO-Based Electronic Device Application: Advancements in Gas Sensor, Logic Circuit, Biosensor, Neuromorphic Device, and Photodetector Technologies." Micromachines 16, no. 2 (2025): 118. https://doi.org/10.3390/mi16020118.

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Metal oxide semiconductors, such as indium gallium zinc oxide (IGZO), have attracted significant attention from researchers in the fields of liquid crystal displays (LCDs) and organic light-emitting diodes (OLEDs) for decades. This interest is driven by their high electron mobility of over ~10 cm2/V·s and excellent transmittance of more than ~80%. Amorphous IGZO (a-IGZO) offers additional advantages, including compatibility with various processes and flexibility making it suitable for applications in flexible and wearable devices. Furthermore, IGZO-based thin-film transistors (TFTs) exhibit hi
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Zhou, Kui, Ziqi Jia, Xin-Qi Ma, et al. "Manufacturing of graphene based synaptic devices for optoelectronic applications." International Journal of Extreme Manufacturing, August 8, 2023. http://dx.doi.org/10.1088/2631-7990/acee2e.

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Abstract Neuromorphic computing systems can perform memory and computing tasks in parallel on artificial synaptic devices through simulating synaptic functions, which is promising for breaking through the limitations of conventional von Neumann bottlenecks at hardware level. Artificial optoelectronic synapses enable the coupling of optical and electrical signals in synaptic modulation, which opens up an innovative path for effective neuromorphic systems. With the advantages of high mobility, optical transparency, ultrawideband tunability, and environmental stability, graphene has attracted tre
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Wan, Changjin, Mengjiao Pei, Kailu Shi, et al. "Toward a Brain‐Neuromorphics Interface." Advanced Materials, February 10, 2024. http://dx.doi.org/10.1002/adma.202311288.

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AbstractBrain‐computer interfaces (BCIs) that enable human‐machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal‐oxide‐semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain‐neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape our interactions with machineries. Neuromorphic devices and systems are able to provide substantial computati
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Li, Yan, Guanglong Ding, Yongbiao Zhai, et al. "MXene‐Based Flexible Memory and Neuromorphic Devices." Small, January 31, 2025. https://doi.org/10.1002/smll.202410914.

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AbstractAs the age of the Internet of Things (IoTs) unfolds, along with the rapid advancement of artificial intelligence (AI), traditional von Neumann‐based computing systems encounter significant challenges in handling vast amounts of data storage and processing. Bioinspired neuromorphic computing strategies offer a promising solution, characterized by features of in‐memory computing, massively parallel processing, and event‐driven operations. Compared to traditional rigid silicon‐based devices, flexible neuromorphic devices are lightweight, thin, and highly stretchable, garnering considerabl
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Greatorex, Hugh, Ole Richter, Michele Mastella, et al. "A neuromorphic processor with on-chip learning for beyond-CMOS device integration." Nature Communications 16, no. 1 (2025). https://doi.org/10.1038/s41467-025-61576-6.

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Abstract Recent advances in memory technologies, devices, and materials have shown great potential for integration into neuromorphic electronic systems. However, a significant gap remains between the development of these materials and the realization of large-scale, fully functional systems. One key challenge is determining which devices and materials are best suited for specific functions and how they can be paired with complementary metal-oxide-semiconductor circuitry. To address this, we present a mixed-signal neuromorphic architecture designed to explore the integration of on-chip learning
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You, Zhou, and Shriram Ramanathan. "Mott Memory and Neuromorphic Devices." August 1, 2015. https://doi.org/10.1109/jproc.2015.2431914.

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Orbital occupancy control in correlated oxides allows the realization of new electronic phases and collective state switching under external stimuli. The resultant structural and electronic phase transitions provide an elegant way to encode, store, and process information. In this review, we examine the utilization of Mott metal-to-insulator transitions, for memory and neuromorphic devices. We emphasize the overarching electron–phonon coupling and electron–electron interaction- driven transition mechanisms and kinetics, which renders a general description of Mott memories from aspects such as
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Shen, Jiabin, Zengguang Cheng, and Peng Zhou. "Optical and optoelectronic neuromorphic devices based on emerging memory technologies." Nanotechnology, May 23, 2022. http://dx.doi.org/10.1088/1361-6528/ac723f.

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Abstract As artificial intelligence continues its rapid development, inevitable challenges arise for the mainstream computing hardware to process voluminous data (Big data). The conventional computer system based on von Neumann architecture with separated processor unit and memory is approaching the limit of computational speed and energy efficiency. Thus, novel computing architectures such as in-memory computing and neuromorphic computing based on emerging memory technologies have been proposed. In recent years, light is incorporated into computational devices, beyond the data transmission in
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39

Decastri, Davide, and Francesca Borghi. "Advances in Neuromorphic Computing Devices: Insights on Both Conventional and Unconventional Architectures." Recent Patents on Nanotechnology 19 (February 10, 2025). https://doi.org/10.2174/0118722105335459241210043513.

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Abstract: Neuromorphic circuits and devices have been introduced in the last decades as elements of a key strategy for developing of new paradigms of computation, inspired by the intent to mimic elementary neuron structure and biological mechanisms, for the overcoming of energy and timeconsuming bottlenecks achieved by digital computing (DC) technologies. Although the term “neuromorphic” is in common use, its meaning is often misunderstood and indistinctly associated with many different technologies, based on both conventional and unconventional electronic components and architectures. Here an
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Kim, Sungho, Hee-Dong Kim, and Sung-Jin Choi. "Impact of Synaptic Device Variations on Classification Accuracy in a Binarized Neural Network." Scientific Reports 9, no. 1 (2019). http://dx.doi.org/10.1038/s41598-019-51814-5.

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Abstract Brain-inspired neuromorphic systems (hardware neural networks) are expected to be an energy-efficient computing architecture for solving cognitive tasks, which critically depend on the development of reliable synaptic weight storage (i.e., synaptic device). Although various nanoelectronic devices have successfully reproduced the learning rules of biological synapses through their internal analog conductance states, the sustainability of such devices is still in doubt due to the variability common to all nanoelectronic devices. Alternatively, a neuromorphic system based on a relatively
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Covi, Erika, Elisa Donati, Xiangpeng Liang, et al. "Adaptive Extreme Edge Computing for Wearable Devices." Frontiers in Neuroscience 15 (May 11, 2021). http://dx.doi.org/10.3389/fnins.2021.611300.

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Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in t
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42

Donati, Elisa, and Giacomo Valle. "Neuromorphic hardware for somatosensory neuroprostheses." Nature Communications 15, no. 1 (2024). http://dx.doi.org/10.1038/s41467-024-44723-3.

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AbstractIn individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation
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Gupta, Shubham Umeshkumar, Malkeshkumar Patel, Naveen Kumar, et al. "Toward Advancement of Fabrication Techniques of Neuromorphic Computing Devices Based on 2D Materials." Advanced Materials Technologies, July 12, 2025. https://doi.org/10.1002/admt.202500786.

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AbstractThe growing necessity for power‐efficient and cognitive computation mechanisms has driven progress in neuromorphic computing which seeks to imitate the synaptic mechanisms underlying human brain functionality. The drawbacks of traditional computation paradigms which involve a large amount of power utilization and restricted data communication drive the quest for alternative materials and technologies. In this context, 2D materials have proven themselves an especially valuable class of materials for new‐generation neuromorphic devices because of their atomic thickness and distinct elect
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44

Deng, Sunbin, Haoming Yu, Tae Joon Park, et al. "Selective area doping for Mott neuromorphic electronics." Science Advances 9, no. 11 (2023). http://dx.doi.org/10.1126/sciadv.ade4838.

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The cointegration of artificial neuronal and synaptic devices with homotypic materials and structures can greatly simplify the fabrication of neuromorphic hardware. We demonstrate experimental realization of vanadium dioxide (VO 2 ) artificial neurons and synapses on the same substrate through selective area carrier doping. By locally configuring pairs of catalytic and inert electrodes that enable nanoscale control over carrier density, volatility or nonvolatility can be appropriately assigned to each two-terminal Mott memory device per lithographic design, and both neuron- and synapse-like de
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Liu, Xuerong, Cui Sun, Xiaoyu Ye, et al. "Neuromorphic Nanoionics for human‐machine Interaction: from Materials to Applications." Advanced Materials, February 29, 2024. http://dx.doi.org/10.1002/adma.202311472.

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AbstractHuman‐machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensiv
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Ivanov, Dmitry, Aleksandr Chezhegov, Mikhail Kiselev, Andrey Grunin, and Denis Larionov. "Neuromorphic artificial intelligence systems." Frontiers in Neuroscience 16 (September 14, 2022). http://dx.doi.org/10.3389/fnins.2022.959626.

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Modern artificial intelligence (AI) systems, based on von Neumann architecture and classical neural networks, have a number of fundamental limitations in comparison with the mammalian brain. In this article we discuss these limitations and ways to mitigate them. Next, we present an overview of currently available neuromorphic AI projects in which these limitations are overcome by bringing some brain features into the functioning and organization of computing systems (TrueNorth, Loihi, Tianjic, SpiNNaker, BrainScaleS, NeuronFlow, DYNAP, Akida, Mythic). Also, we present the principle of classify
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47

Schulman, Alejandro, Hannu Huhtinen, and Petriina Paturi. "Manganite Memristive Devices: Recent Progress and Emerging Opportunities." Journal of Physics D: Applied Physics, July 19, 2024. http://dx.doi.org/10.1088/1361-6463/ad6575.

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Abstract Manganite-based memristive devices have emerged as promising candidates for next-generation non-volatile memory and neuromorphic computing applications, owing to their unique resistive switching behavior and tunable electronic properties. This review explores recent innovations in manganite-based memristive devices, with a focus on materials engineering, device architectures, and fabrication techniques. We delve into the underlying mechanisms governing resistive switching in manganite thin films, elucidating the intricate interplay of oxygen vacancies, charge carriers, and structural
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Kang, Kyowon, Kiho Kim, Junhyeong Baek, Doohyun J. Lee, and Ki Jun Yu. "Biomimic and bioinspired soft neuromorphic tactile sensory system." Applied Physics Reviews 11, no. 2 (2024). http://dx.doi.org/10.1063/5.0204104.

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The progress in flexible and neuromorphic electronics technologies has facilitated the development of artificial perception systems. By closely emulating biological functions, these systems are at the forefront of revolutionizing intelligent robotics and refining the dynamics of human–machine interactions. Among these, tactile sensory neuromorphic technologies stand out for their ability to replicate the intricate architecture and processing mechanisms of the brain. This replication not only facilitates remarkable computational efficiency but also equips devices with efficient real-time data-p
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Yadav, Madhu, Prabana Jetty та S. Narayana Jammalamadaka. "Neuromorphic Engineering: Remote Control and Pavlovian Conditioning via Ag/α‐Fe2O3/Fluorine‐Doped Tin Oxide based Synaptic Memristor Device". physica status solidi (a), 12 червня 2025. https://doi.org/10.1002/pssa.202500314.

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Neuromorphic devices and their technologies have emerged as a novel approach to meet the growing demands of data‐intensive applications. It offers substantially higher energy efficiency compared to traditional computers which work based on von Neumann architecture. In this respect, tuning synaptic characteristics like long‐term potentiation (LTP) and long‐term depression (LTD) by external stimuli in a remote way can be an alternative energy efficient approach. Hence, the efforts in manifesting remote control of neuromorphic functionalities in α‐Fe2O3‐based synaptic device using magnetic field
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Li, Shen-Yi, Ji-Tuo Li, Kui Zhou, et al. "In-sensor neuromorphic computing using perovskites and transition metal dichalcogenides." Journal of Physics: Materials, May 30, 2024. http://dx.doi.org/10.1088/2515-7639/ad5251.

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Abstract With the advancements in Web of Things, Artificial Intelligence, and other emerging technologies, there is an increasing demand for artificial visual systems to perceive and learn about external environments. However, traditional sensing and computing systems are limited by the physical separation of sense, processing, and memory units that results in the challenges such as high energy consumption, large additional hardware costs, and long latency time. Integrating neuromorphic computing functions into the sensing unit is an effective way to overcome these challenges. Therefore, it is
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