Статті в журналах з теми "Neuromorphic technologies/devices"
Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Neuromorphic technologies/devices.
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-50 статей у журналах для дослідження на тему "Neuromorphic technologies/devices".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте статті в журналах для різних дисциплін та оформлюйте правильно вашу бібліографію.
1
Orii, Yasumitsu, Akihiro Horibe, Kuniaki Sueoka, Keiji Matsumoto, Toyohiro Aoki, Hirokazu Noma, Sayuri Kohara, et al. "PERSPECTIVE ON REQUIRED PACKAGING TECHNOLOGIES FOR NEUROMORPHIC DEVICES." International Symposium on Microelectronics 2015, no. 1 (October 1, 2015): 000561–66. http://dx.doi.org/10.4071/isom-2015-tha15.
Анотація:
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. However, one million neurons only correspond to those of the bee's brain, and to mimic the brains of higher order animals, the inter-chip wiring becomes much more important, because this kind of neuromorphic device requires a large number of parallel signal lines for massive parallel signal operations. 3D chip stacking is, of course, a crucial technology in achieving the device. Technologies associated with 3D stacking such as low cost TSV formation and fine-pitch interconnection, smaller than 10μm pitch technology are required. From the reliability point of view, the optimization of solder composition is also important. Injection Molded Solder (IMS) is well fit to this fine pitch interconnection, in terms of material optimization and low cost joints. As for the interposer, the build-up organic interposer is the most attractive candidates for the cost issue, but in the most top layer, ultra-fine pitch wiring with the line and space widths smaller than 1μm should be prepared. Lots of material and process innovations are necessary for the inter-chip connection for neuromorphic devices.
2
Diao, Yu, Yaoxuan Zhang, Yanran Li, and Jie Jiang. "Metal-Oxide Heterojunction: From Material Process to Neuromorphic Applications." Sensors 23, no. 24 (December 12, 2023): 9779. http://dx.doi.org/10.3390/s23249779.
Анотація:
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 heterostructures for neuromorphic applications. These heterostructures not only offer low power consumption and high stability but also possess optimized electrical characteristics via interface engineering. The paper first outlines various synthesis methods for metal oxides and then summarizes the neuromorphic devices using these materials and their heterostructures. More importantly, we review the emerging multifunctional applications, including neuromorphic vision, touch, and pain systems. Finally, we summarize the future prospects of neuromorphic devices with metal-oxide heterostructures and list the current challenges while offering potential solutions. This review provides insights into the design and construction of metal-oxide devices and their applications for neuromorphic systems.
3
Milo, Valerio, Gerardo Malavena, Christian Monzio Compagnoni, and Daniele Ielmini. "Memristive and CMOS Devices for Neuromorphic Computing." Materials 13, no. 1 (January 1, 2020): 166. http://dx.doi.org/10.3390/ma13010166.
Анотація:
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 computing functionality must be developed. This work provides an overview of the most promising device concepts in neuromorphic computing including complementary metal-oxide semiconductor (CMOS) and memristive technologies. First, the physics and operation of CMOS-based floating-gate memory devices in artificial neural networks will be addressed. Then, several memristive concepts will be reviewed and discussed for applications in deep neural network and spiking neural network architectures. Finally, the main technology challenges and perspectives of neuromorphic computing will be discussed.
4
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 (April 30, 2022): 725. http://dx.doi.org/10.3390/mi13050725.
Анотація:
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 in this review, including the materials and issues associated with various materials, as well as the basic switching mechanisms. Furthermore, the opportunities that CBRAMs provide for memory and brain-inspired neuromorphic computing applications, as well as the challenges that CBRAMs confront in those applications, are thoroughly discussed. The emulation of biological synapses and neurons using CBRAM devices fabricated with various switching materials and device engineering and material innovation approaches are examined in depth.
5
Allwood, Dan A., Matthew O. A. Ellis, David Griffin, Thomas J. Hayward, Luca Manneschi, Mohammad F. KH Musameh, Simon O'Keefe, et al. "A perspective on physical reservoir computing with nanomagnetic devices." Applied Physics Letters 122, no. 4 (January 23, 2023): 040501. http://dx.doi.org/10.1063/5.0119040.
Анотація:
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 computing paradigm, a recurrent network with a simple training algorithm suitable for computation with spintronic devices since they can provide the properties of non-linearity and memory. We review technologies and methods for developing neuromorphic spintronic devices and conclude with critical open issues to address before such devices become widely used.
6
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.
Анотація:
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” created in the 1960s, by focusing on the work of Leon Harmon and his “neuromime,” whose material history overlapped in a very interesting sense with the visual and artistic culture of the second half of the 20th century.
7
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 (December 2020): 457–75. http://dx.doi.org/10.1142/s1793351x20500063.
Анотація:
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 important enabler for AI hardware and the continuation of the scaling laws. In this paper, we overview the unique opportunities 3D integration provides in neuromorphic chip design, discuss the emerging opportunities in next generation neuromorphic architectures and review the obstacles. Neuromorphic architectures, which relied on the brain for inspiration and emulation purposes, face grand challenges due to the limited understanding of the functionality and the architecture of the human brain. Yet, high-levels of investments are dedicated to develop neuromorphic chips. We argue that 3D integration not only provides strategic advantages to the cost-effective and flexible design of neuromorphic chips, it may provide design flexibility in incorporating advanced capabilities to further benefit the designs in the future.
8
Hajtó, Dániel, Ádám Rák, and György Cserey. "Robust Memristor Networks for Neuromorphic Computation Applications." Materials 12, no. 21 (October 31, 2019): 3573. http://dx.doi.org/10.3390/ma12213573.
Анотація:
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 measurements, by gaining similar results as in previous simulations. These results can lead to more stable devices, which are a necessity for neuromorphic computation, artificial intelligence and neural network applications.
9
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 (February 7, 2022): 012002. http://dx.doi.org/10.1088/2634-4386/ac4918.
Анотація:
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 internal-state storage with very low power consumption. Still, they mainly rely on standard complementary metal-oxide-semiconductor (CMOS) technologies, making SNNs unfit to meet the aforementioned constraints. Recently, emerging technologies such as memristive devices have been investigated to flank CMOS technology and overcome edge computing systems’ power and memory constraints. In this review, we will focus on ferroelectric technology. Thanks to its CMOS-compatible fabrication process and extreme energy efficiency, ferroelectric devices are rapidly affirming themselves as one of the most promising technologies for neuromorphic computing. Therefore, we will discuss their role in emulating neural and synaptic behaviors in an area and power-efficient way.
10
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 (March 7, 2022): 225105. http://dx.doi.org/10.1088/1361-6463/ac585b.
Анотація:
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 biological synapse with low power consumption, fast switching speed and simulation of synaptic plasticity, while natural organic materials are water soluble, renewable and environmental friendly. In this study, the potential of a natural organic material—honey-based memristor for SNNs was demonstrated. The device exhibited forming-free bipolar resistive switching, a high switching speed of 100 ns set time and 500 ns reset time, STDP and SRDP learning behaviours, and dissolving in water. The intuitive conduction models for STDP and SRDP were proposed. These results testified that honey-based memristive synaptic devices are promising for SNN implementation in green electronics and biodegradable neuromorphic systems.
11
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 (March 30, 2022): 053001. http://dx.doi.org/10.1088/1361-6668/ac4cd2.
Анотація:
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 done on biologically inspired superconductive devices, circuits, and architectures and discuss the scaling potential of these demonstrations.
12
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 (October 20, 2021): 2564. http://dx.doi.org/10.3390/electronics10212564.
Анотація:
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 substrate was conducted to observe its characterization. In addition, a switching mechanism of a memristive device was theoretically studied and associated with ion migrations into a polymeric insulating layer. Biological short-term and long-term memory properties were imitated with the memristive device using low power consumption.
13
Jha, Rashmi. "Emerging Memory Devices Beyond Conventional Data Storage: Paving the Path for Energy-Efficient Brain-Inspired Computing." Electrochemical Society Interface 32, no. 1 (March 1, 2023): 49–51. http://dx.doi.org/10.1149/2.f10231if.
Анотація:
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 closer to the computing units. There are also research efforts where in-memory computing architectures have been implemented using DRAMs or flash memory technologies. However, DRAMs suffer from scaling limitations, while flash memory devices suffer from endurance issues. Additionally, in spite of this significant progress, the massive energy consumption needed in neuromorphic processors while meeting the required training and inferencing performance for AI/ML algorithms for future applications needs to be addressed. On the AI/ML algorithm side, there are several pending issues such as life-long learning, explainability, context-based decision making, multimodal association of data, adaptation to address personalized responses, and resiliency. These unresolved challenges in AI/ML have led researchers to explore brain-inspired computing architectures and paradigms.
14
Khajooei, Arash, Mohammad (Behdad) Jamshidi, and Shahriar B. Shokouhi. "A Super-Efficient TinyML Processor for the Edge Metaverse." Information 14, no. 4 (April 10, 2023): 235. http://dx.doi.org/10.3390/info14040235.
Анотація:
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 (TinyML) technologies, can be an effective tool to enhance edge devices in such emerging ecosystems. Thus, a super-efficient TinyML processor to use in the edge-enabled Metaverse platforms has been designed and evaluated in this research. This processor includes a Winner-Take-All (WTA) circuit that was implemented via a simplified Leaky Integrate and Fire (LIF) neuron on an FPGA. The WTA architecture is a computational principle in a neuromorphic system inspired by the mini-column structure in the human brain. The resource consumption of the WTA architecture is reduced by employing our simplified LIF neuron, making it suitable for the proposed edge devices. The results have indicated that the proposed neuron improves the response speed to almost 39% and reduces resource consumption by 50% compared to recent works. Using our simplified neuron, up to 4200 neurons can be deployed on VIRTEX 6 devices. The maximum operating frequency of the proposed neuron and our spiking WTA is 576.319 MHz and 514.095 MHz, respectively.
15
Gao, Zhan, Yan Wang, Ziyu Lv, Pengfei Xie, Zong-Xiang Xu, Mingtao Luo, Yuqi Zhang, et al. "Ferroelectric coupling for dual-mode non-filamentary memristors." Applied Physics Reviews 9, no. 2 (June 2022): 021417. http://dx.doi.org/10.1063/5.0087624.
Анотація:
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–trifluoroethylene), as the dielectric layer. This approach can finely modulate ion transport and contact barrier at the switching interface in non-filamentary perovskite memristors, thus, creating two distinct operation modes (volatile and nonvolatile). Additionally, perovskite memristors show desirable resistive switching performance, including forming-free operation, high yield of 88.9%, cycle-to-cycle variation of 7.8%, and low operating current of sub-100 nA. The dual-mode memristor is capable of emulating biological nociception in both active (perceiving pain) and blocked states (suppressing pain signaling).
16
Chiappalone, Michela, Vinicius R. Cota, Marta Carè, Mattia Di Florio, Romain Beaubois, Stefano Buccelli, Federico Barban, et al. "Neuromorphic-Based Neuroprostheses for Brain Rewiring: State-of-the-Art and Perspectives in Neuroengineering." Brain Sciences 12, no. 11 (November 19, 2022): 1578. http://dx.doi.org/10.3390/brainsci12111578.
Анотація:
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 perform real-time data processing, and of mimicking neurobiological computation for an improved synergy between the technological and biological counterparts. In this manuscript, after providing definitions of key concepts, we reviewed the first exploitation of a real-time hardware neuromorphic prosthesis to restore the bidirectional communication between two neuronal populations in vitro. Starting from that ‘case-study’, we provide perspectives on the technological improvements for real-time interfacing and processing of neural signals and their potential usage for novel in vitro and in vivo experimental designs. The development of innovative neuroprosthetics for translational purposes is also presented and discussed. In our understanding, the pursuit of neuromorphic-based closed-loop neuroprostheses may spur the development of novel powerful technologies, such as ‘brain-prostheses’, capable of rewiring and/or substituting the injured nervous system.
17
Banerjee, Writam. "Challenges and Applications of Emerging Nonvolatile Memory Devices." Electronics 9, no. 6 (June 22, 2020): 1029. http://dx.doi.org/10.3390/electronics9061029.
Анотація:
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 alternative devices, RRAM technology is showing promise with its highly scalable, cost-effective, simple two-terminal structure, low-voltage and ultra-low-power operation capabilities, high-speed switching with high-endurance, long retention, and the possibility of three-dimensional integration for high-density applications. More precisely, this review explains the journey and device engineering of RRAM with various architectures. The challenges in different prototype and eNVM devices is disused with the conventional and novel application areas. Compare to other technologies, RRAM is the most promising approach which can be applicable as high-density memory, storage class memory, neuromorphic computing, and also in hardware security. In the post-CMOS era, a more efficient, intelligent, and secure computing system is possible to design with the help of eNVM devices.
18
Li, Bixin, Shiyang Zhang, Lan Xu, Qiong Su, and Bin Du. "Emerging Robust Polymer Materials for High-Performance Two-Terminal Resistive Switching Memory." Polymers 15, no. 22 (November 10, 2023): 4374. http://dx.doi.org/10.3390/polym15224374.
Анотація:
Facing the era of information explosion and the advent of artificial intelligence, there is a growing demand for information technologies with huge storage capacity and efficient computer processing. However, traditional silicon-based storage and computing technology will reach their limits and cannot meet the post-Moore information storage requirements of ultrasmall size, ultrahigh density, flexibility, biocompatibility, and recyclability. As a response to these concerns, polymer-based resistive memory materials have emerged as promising candidates for next-generation information storage and neuromorphic computing applications, with the advantages of easy molecular design, volatile and non-volatile storage, flexibility, and facile fabrication. Herein, we first summarize the memory device structures, memory effects, and memory mechanisms of polymers. Then, the recent advances in polymer resistive switching materials, including single-component polymers, polymer mixtures, 2D covalent polymers, and biomacromolecules for resistive memory devices, are highlighted. Finally, the challenges and future prospects of polymer memory materials and devices are discussed. Advances in polymer-based memristors will open new avenues in the design and integration of high-performance switching devices and facilitate their application in future information technology.
19
Mikhaylov, A. N. "Neuroelectronics as neuromorphic and neurohybryd systems enabled by memristive technology." Genes & Cells 18, no. 4 (December 15, 2023): 825–26. http://dx.doi.org/10.17816/gc623426.
Анотація:
Due to its distinctive capability to replicate vital functions of synapses and neurons, memristive devices and arrays enable both the hardware implementation of neural networks and a significant advancement towards integrating artificial electronic and biological systems to address pressing issues in artificial intelligence (AI), robotics, and medicine. This research area is still nascent and can be viewed as part of the broader field of neuroelectronics. The aforementioned is a fusion of analog and digital solutions used for diverse computational tasks inspired by biology. Notably, analog neuromorphic systems that employ memristive components are distinctive features of this arena and they can significantly enhance throughput and energy efficiency in contrast to existing AI accelerators. Designing neuromorphic systems grounded on this fresh component base mandates coordinated and interdisciplinary research and development. The foundation of this scientific and technological field lies in the cross-cutting technology of memristive devices and circuits. This technology enables the development of a novel brain-like information and computing system infrastructure that can be applied in a diverse range of fields. Current perspectives include the seamless integration of memristive devices and arrays with CMOS circuits, and the co-optimization of materials, devices, and architectures to create prototypes for computing and information systems. These systems replicate computational features present in biological neural networks capable of solving cognitive tasks that are typically either intractable by traditional AI or highly time-consuming. Neuroelectronic solutions can integrate with the brain or living neuronal cultures to form neurohybrid systems. In this presentation, we discuss two distinct strategies for connecting memristive systems with biological neural networks both in vitro and in vivo. These include a perceptron using an array of programmable memristive weights represented by metal-oxide resistive-switching devices and a methodology leveraging memristive stochastic plasticity and neural synchrony, which is part of the brain’s spiking architecture. Finally, the concept of a memristive neurohybrid chip is presented to create a compact, multifunctional, bidirectional interface between biological neural networks and memristive electronics, combined with microelectrode and microfluidic systems on a single chip. The technological advancements in component base and the development of memristor-based neuroelectronic systems will diversify hardware for the continuous evolution and mass application of artificial intelligence technologies. This will enable the creation of hybrid intelligence based on the symbiosis of artificial and biological neural networks and allow for the establishment of novel tasks at an unprecedented level.
20
Abd, Hamam, and Andreas König. "On-Chip Adaptive Implementation of Neuromorphic Spiking Sensory Systems with Self-X Capabilities." Chips 2, no. 2 (June 6, 2023): 142–58. http://dx.doi.org/10.3390/chips2020009.
Анотація:
In contemporary devices, the number and diversity of sensors is increasing, thus, requiring both efficient and robust interfacing to the sensors. Implementing the interfacing systems in advanced integration technologies faces numerous issues due to manufacturing deviations, signal swings, noise, etc. The interface sensor designers escape to the time domain and digital design techniques to handle these challenges. Biology gives examples of efficient machines that have vastly outperformed conventional technology. This work pursues a neuromorphic spiking sensory system design with the same efficient style as biology. Our chip, that comprises the essential elements of the adaptive neuromorphic spiking sensory system, such as the neuron, synapse, adaptive coincidence detection (ACD), and self-adaptive spike-to-rank coding (SA-SRC), was manufactured in XFAB CMOS 0.35 μm technology via EUROPRACTICE. The main emphasis of this paper is to present the measurement outcomes of the SA-SRC on-chip, evaluating the efficacy of its adaptation scheme, and assessing its capability to produce spike orders that correspond to the temporal difference between the two spikes received at its inputs. The SA-SRC plays a crucial role in performing the primary function of the adaptive neuromorphic spiking sensory system. The measurement results of the chip confirm the simulation results of our previous work.
21
Akai-Kasaya, Megumi, Yuki Takeshima, Shaohua Kan, Kohei Nakajima, Takahide Oya, and Tetsuya Asai. "Performance of reservoir computing in a random network of single-walled carbon nanotubes complexed with polyoxometalate." Neuromorphic Computing and Engineering 2, no. 1 (January 24, 2022): 014003. http://dx.doi.org/10.1088/2634-4386/ac4339.
Анотація:
Abstract Molecular neuromorphic devices are composed of a random and extremely dense network of single-walled carbon nanotubes (SWNTs) complexed with polyoxometalate (POM). Such devices are expected to have the rudimentary ability of reservoir computing (RC), which utilizes signal response dynamics and a certain degree of network complexity. In this study, we performed RC using multiple signals collected from a SWNT/POM random network. The signals showed a nonlinear response with wide diversity originating from the network complexity. The performance of RC was evaluated for various tasks such as waveform reconstruction, a nonlinear autoregressive model, and memory capacity. The obtained results indicated its high capability as a nonlinear dynamical system, capable of information processing incorporated into edge computing in future technologies.
22
Shen, Zongjie, Chun Zhao, Yanfei Qi, Ivona Z. Mitrovic, Li Yang, Jiacheng Wen, Yanbo Huang, Puzhuo Li, and Cezhou Zhao. "Memristive Non-Volatile Memory Based on Graphene Materials." Micromachines 11, no. 4 (March 25, 2020): 341. http://dx.doi.org/10.3390/mi11040341.
Анотація:
Resistive random access memory (RRAM), which is considered as one of the most promising next-generation non-volatile memory (NVM) devices and a representative of memristor technologies, demonstrated great potential in acting as an artificial synapse in the industry of neuromorphic systems and artificial intelligence (AI), due its advantages such as fast operation speed, low power consumption, and high device density. Graphene and related materials (GRMs), especially graphene oxide (GO), acting as active materials for RRAM devices, are considered as a promising alternative to other materials including metal oxides and perovskite materials. Herein, an overview of GRM-based RRAM devices is provided, with discussion about the properties of GRMs, main operation mechanisms for resistive switching (RS) behavior, figure of merit (FoM) summary, and prospect extension of GRM-based RRAM devices. With excellent physical and chemical advantages like intrinsic Young’s modulus (1.0 TPa), good tensile strength (130 GPa), excellent carrier mobility (2.0 × 105 cm2∙V−1∙s−1), and high thermal (5000 Wm−1∙K−1) and superior electrical conductivity (1.0 × 106 S∙m−1), GRMs can act as electrodes and resistive switching media in RRAM devices. In addition, the GRM-based interface between electrode and dielectric can have an effect on atomic diffusion limitation in dielectric and surface effect suppression. Immense amounts of concrete research indicate that GRMs might play a significant role in promoting the large-scale commercialization possibility of RRAM devices.
23
Zatsarinny, A. A., and K. K. Abgaryan. "Factors determining the relevance of creation research infrastructure for the synthesis of new materials in the framework of the implementation of the priorities of scientific and technological development of Russia." Izvestiya Vysshikh Uchebnykh Zavedenii. Materialy Elektronnoi Tekhniki = Materials of Electronics Engineering 22, no. 4 (February 4, 2020): 298–301. http://dx.doi.org/10.17073/1609-3577-2019-4-298-301.
Анотація:
In the modern world, knowledge and high technologies determine the effectiveness of the economy, can radically improve the quality of life of people, modernize infrastructure and public administration, and ensure law and order and security. The creation of a research infrastructure based on a high-performance hybrid cluster enabled detailed calculations of complex phenomena and processes without full-scale experiments. It has become possible to most efficiently apply modern methods of multiscale computer modeling when developing prototypes of new materials with desired properties for their further synthesis. Such approaches can significantly reduce the cost and speed up the development of modern technologies for producing new semiconductor materials for nanoelectronics, composite materials for the aerospace industry and others. Thus, the use of multiscale modeling methods in combination with the use of high-performance software tools made it possible to create a computer model of a nanoscale heterostructure, develop tools for predictive computer modeling of the physical structure of nanoelectronic devices, the neuromorphic architecture of multilevel memory devices, defect formation in composite materials, and others.
24
Kamath, Rachana, Parantap Sarkar, Sindhoora Kaniyala Melanthota, Rajib Biswas, Nirmal Mazumder, and Shounak De. "Resistive Memory-Switching Behavior in Solution-Processed Trans, trans-1,4-bis-(2-(2-naphthyl)-2-(butoxycarbonyl)-vinyl) Benzene–PVA-Composite-Based Aryl Acrylate on ITO-Coated PET." Polymers 16, no. 2 (January 12, 2024): 218. http://dx.doi.org/10.3390/polym16020218.
Анотація:
Resistive switching memories are among the emerging next-generation technologies that are possible candidates for in-memory and neuromorphic computing. In this report, resistive memory-switching behavior in solution-processed trans, trans-1,4-bis-(2-(2-naphthyl)-2-(butoxycarbonyl)-vinyl) benzene–PVA-composite-based aryl acrylate on an ITO-coated PET device was studied. A sandwich configuration was selected, with silver (Ag) serving as a top contact and trans, trans-1,4-bis-(2-(2-naphthyl)-2-(butoxycarbonyl)-vinyl) benzene–PVA-composite-based aryl acrylate and ITO-PET serving as a bottom contact. The current–voltage (I–V) characteristics showed hysteresis behavior and non-zero crossing owing to voltages sweeping from positive to negative and vice versa. The results showed non-zero crossing in the devices’ current–voltage (I–V) characteristics due to the nanobattery effect or resistance, capacitive, and inductive effects. The device also displayed a negative differential resistance (NDR) effect. Non-volatile storage was feasible with non-zero crossing due to the exhibition of resistive switching behavior. The sweeping range was −10 V to +10 V. These devices had two distinct states: ‘ON’ and ‘OFF’. The ON/OFF ratios of the devices were 14 and 100 under stable operating conditions. The open-circuit voltages (Voc) and short-circuit currents (Isc) corresponding to memristor operation were explained. The DC endurance was stable. Ohmic conduction and direct tunneling mechanisms with traps explained the charge transport model governing the resistive switching behavior. This work gives insight into data storage in terms of a new conception of electronic devices based on facile and low-temperature processed material composites for emerging computational devices.
25
Ou, Qiao-Feng, Bang-Shu Xiong, Lei Yu, Jing Wen, Lei Wang, and Yi Tong. "In-Memory Logic Operations and Neuromorphic Computing in Non-Volatile Random Access Memory." Materials 13, no. 16 (August 10, 2020): 3532. http://dx.doi.org/10.3390/ma13163532.
Анотація:
Recent progress in the development of artificial intelligence technologies, aided by deep learning algorithms, has led to an unprecedented revolution in neuromorphic circuits, bringing us ever closer to brain-like computers. However, the vast majority of advanced algorithms still have to run on conventional computers. Thus, their capacities are limited by what is known as the von-Neumann bottleneck, where the central processing unit for data computation and the main memory for data storage are separated. Emerging forms of non-volatile random access memory, such as ferroelectric random access memory, phase-change random access memory, magnetic random access memory, and resistive random access memory, are widely considered to offer the best prospect of circumventing the von-Neumann bottleneck. This is due to their ability to merge storage and computational operations, such as Boolean logic. This paper reviews the most common kinds of non-volatile random access memory and their physical principles, together with their relative pros and cons when compared with conventional CMOS-based circuits (Complementary Metal Oxide Semiconductor). Their potential application to Boolean logic computation is then considered in terms of their working mechanism, circuit design and performance metrics. The paper concludes by envisaging the prospects offered by non-volatile devices for future brain-inspired and neuromorphic computation.
26
Rahmani, Amir Masoud, Rizwan Ali Naqvi, Saqib Ali, Seyedeh Yasaman Hosseini Mirmahaleh, Mohammed Alswaitti, Mehdi Hosseinzadeh, and Kamran Siddique. "An Astrocyte-Flow Mapping on a Mesh-Based Communication Infrastructure to Defective Neurons Phagocytosis." Mathematics 9, no. 23 (November 24, 2021): 3012. http://dx.doi.org/10.3390/math9233012.
Анотація:
In deploying the Internet of Things (IoT) and Internet of Medical Things (IoMT)-based applications and infrastructures, the researchers faced many sensors and their output’s values, which have transferred between service requesters and servers. Some case studies addressed the different methods and technologies, including machine learning algorithms, deep learning accelerators, Processing-In-Memory (PIM), and neuromorphic computing (NC) approaches to support the data processing complexity and communication between IoMT nodes. With inspiring human brain structure, some researchers tackled the challenges of rising IoT- and IoMT-based applications and neural structures’ simulation. A defective device has destructive effects on the performance and cost of the applications, and their detection is challenging for a communication infrastructure with many devices. We inspired astrocyte cells to map the flow (AFM) of the Internet of Medical Things onto mesh network processing elements (PEs), and detect the defective devices based on a phagocytosis model. This study focuses on an astrocyte’s cholesterol distribution into neurons and presents an algorithm that utilizes its pattern to distribute IoMT’s dataflow and detect the defective devices. We researched Alzheimer’s symptoms to understand astrocyte and phagocytosis functions against the disease and employ the vaccination COVID-19 dataset to define a set of task graphs. The study improves total runtime and energy by approximately 60.85% and 52.38% after implementing AFM, compared with before astrocyte-flow mapping, which helps IoMT’s infrastructure developers to provide healthcare services to the requesters with minimal cost and high accuracy.
27
Pedretti, Giacomo, and Daniele Ielmini. "In-Memory Computing with Resistive Memory Circuits: Status and Outlook." Electronics 10, no. 9 (April 30, 2021): 1063. http://dx.doi.org/10.3390/electronics10091063.
Анотація:
In-memory computing (IMC) refers to non-von Neumann architectures where data are processed in situ within the memory by taking advantage of physical laws. Among the memory devices that have been considered for IMC, the resistive switching memory (RRAM), also known as memristor, is one of the most promising technologies due to its relatively easy integration and scaling. RRAM devices have been explored for both memory and IMC applications, such as neural network accelerators and neuromorphic processors. This work presents the status and outlook on the RRAM for analog computing, where the precision of the encoded coefficients, such as the synaptic weights of a neural network, is one of the key requirements. We show the experimental study of the cycle-to-cycle variation of set and reset processes for HfO2-based RRAM, which indicate that gate-controlled pulses present the least variation in conductance. Assuming a constant variation of conductance σG, we then evaluate and compare various mapping schemes, including multilevel, binary, unary, redundant and slicing techniques. We present analytical formulas for the standard deviation of the conductance and the maximum number of bits that still satisfies a given maximum error. Finally, we discuss RRAM performance for various analog computing tasks compared to other computational memory devices. RRAM appears as one of the most promising devices in terms of scaling, accuracy and low-current operation.
28
Ostrovskii, V. Yu, O. S. Druzhina, O. Kamal, T. I. Karimov, and D. N. Butusov. "Design of a memristor-based neuron for spiking neural networks." Genes & Cells 18, no. 4 (December 15, 2023): 827–30. http://dx.doi.org/10.17816/gc623428.
Анотація:
The primary objective of neuromorphic system design is to surpass limitations in energy efficiency and scaling of classical von Neumann computing systems, through the emulation of animals’ nervous systems. This is achieved by conducting calculations in memory and encoding information in impulse signals, ultimately leading to enhanced adaptability. Adhering to these principles allows for improved energy efficiency and computational speed when solving machine learning problems, encompassing biomedical applications, embedded systems, and cyber-physical systems. Functional blocks modeling the main elements of the central nervous system, namely neurons and synapses, offer an advantage in implementing learning on a chip. The use of memristive electronic components, capable of altering their resistance based on the charge flowing through them, opens new doors for hardware implementation of neuromorphic systems. These devices offer advantages over conventional transistor electronics with respect to power consumption, component density, and performance. To achieve optimal results, the architecture of neuromorphic systems should be optimized at the device level. Memristive components are utilized to create neurons and synapses. This thesis is specifically focused on producing memristive neuron-like spike signal generators. Previously, memristive neurons were crafted using a locally active element comprised of vanadium dioxide VO2, which incorporated a negative differential resistance section of the IV-curve. One of the recent advancements in this field is a spiking neuron with frequency adaptation [1]. Its drawbacks, however, involve separating the memristive and locally active elements physically, resulting in higher energy consumption and decreased integration quality. In [2], models of memristive neurons with minimal complexity are introduced, which incorporate the Leaky Integrate-and-Fire principle. However, the circuits presented require the application of negative voltage pulses to a DC battery to reset the memristor to its initial high-resistance state. This limitation restricts its sphere of application in neuromorphic systems. This paper proposes a model of a neuron that overcomes these limitations by using the negative differential resistance of the memristor to generate spikes, along with integrating supplementary circuit components to sustain the resistive switching cycles of the memristor. The neuron model under consideration is implemented using the NI Multisim 14.2 SPICE environment and has been verified in the NI LabVIEW 2022 tool environment. The equations of the modified model of the generalized mean metastable switch of the memristor with self-directed channel [3] represent the current in the memristor branch of the neuron equivalent circuit. The simplicity of the equivalent circuitry of the neuron is attained by merging all the nonlinear features necessary for spike generation into one memristor model. The experimental phase of the study employed obtainable memristors from Knowm Corporation and the laboratory prototyping platform NI ELVIS III. The investigation of the proposed neuron model was accomplished through the application of sinusoidal and rectangular input signals. The refractory time of the neuron model was calculated. The chosen stack of computer simulation and semi-natural modeling technologies is applied within the research-driven design concept of electronic devices. This approach considers the importance of refining the properties and identification of the design object or its components during the development cycle.
29
Yanushkevich, Svetlana, Hong Tran, Golam Tangim, Vladimir Shmerko, Elena Zaitseva, and Vitaly Levashenko. "The EXOR gate under uncertainty: A case study." Facta universitatis - series: Electronics and Energetics 24, no. 3 (2011): 451–82. http://dx.doi.org/10.2298/fuee1103451y.
Анотація:
Probabilistic AND/EXOR networks have been defined, in the past, as a class of Reed-Muller circuits, which operate on random signals. In contemporary logic network design, it is classified as behavioral notation of probabilistic logic gates and networks. In this paper, we introduce additional notations of probabilistic AND/EXOR networks: belief propagation, stochastic, decision diagram, neuromorphic models, and Markov random field model. Probabilistic logic networks, and, in particular, probabilistic AND/EXOR networks, known as turbo-decoders (used in cell phones and iPhone) are in demand in the coding theory. Another example is intelligent decision support in banking and security applications. We argue that there are two types of probabilistic networks: traditional logic networks assuming random signals, and belief propagation networks. We propose the taxonomy for this design, and provide the results of experimental study. In addition, we show that in forthcoming technologies, in particular, molecular electronics, probabilistic computing is the platform for developing the devices and systems for low-power low-precise data processing.
30
Fiorelli, Rafaella, Eduardo Peralías, Roberto Méndez-Romero, Mona Rajabali, Akash Kumar, Mohammad Zahedinejad, Johan Åkerman, Farshad Moradi, Teresa Serrano-Gotarredona, and Bernabé Linares-Barranco. "CMOS Front End for Interfacing Spin-Hall Nano-Oscillators for Neuromorphic Computing in the GHz Range." Electronics 12, no. 1 (January 3, 2023): 230. http://dx.doi.org/10.3390/electronics12010230.
Анотація:
Spin-Hall-effect nano-oscillators are promising beyond the CMOS devices currently available, and can potentially be used to emulate the functioning of neurons in computational neuromorphic systems. As they oscillate in the 4–20 GHz range, they could potentially be used for building highly accelerated neural hardware platforms. However, due to their extremely low signal level and high impedance at their output, as well as their microwave-range operating frequency, discerning whether the SHNO is oscillating or not carries a great challenge when its state read-out circuit is implemented using CMOS technologies. This paper presents the first CMOS front-end read-out circuitry, implemented in 180 nm, working at a SHNO oscillation frequency up to 4.7 GHz, managing to discern SHNO amplitudes of 100 µV even for an impedance as large as 300 Ω and a noise figure of 5.3 dB300 Ω. A design flow of this front end is presented, as well as the architecture of each of its blocks. The study of the low-noise amplifier is deepened for its intrinsic difficulties in the design, satisfying the characteristics of SHNOs.
31
Chen, An. "(Invited, Digital Presentation) Emerging Materials and Devices for Energy-Efficient Computing." ECS Meeting Abstracts MA2022-01, no. 19 (July 7, 2022): 1073. http://dx.doi.org/10.1149/ma2022-01191073mtgabs.
Анотація:
As the CMOS scaling driven by the Moore’s Law approaching the fundamental limits, high energy consumption and heat dissipation have been recognized as the most critical device challenges. Novel switching devices with significantly lower power based on unconventional mechanisms have been explored to replace CMOS in various research programs, e.g., Nanoelectronics Research Initiative (NRI). The major categories of these devices include steep-slope transistors, spintronic devices, ferroelectric devices, and van der Waals devices [1]. These devices are often implemented on emerging materials with unique properties. As the foundation of nanoelectronic devices and systems, novel materials (including dielectrics) present both great challenges and promising opportunities. For example, dielectric layers for gating and electrical insulation are critical for low-dimension devices; magnetic insulators are promising for low-power high-efficiency spintronic devices; ferroelectric materials have been utilized to realize “negative-capacitance” transistors with steep subthreshold slope. Despite abundant scientific breakthroughs achieved on these emerging devices, comprehensive benchmarking has revealed that most of them do not outperform CMOS for Boolean logic and von Neumann architectures [2]. Therefore, the focus of emerging materials and devices has increasingly shifted toward novel computing paradigms. Novel computing paradigms beyond Boolean logic and von Neumann architectures may provide solutions for energy-efficient computing. For example, in-memory computing reduces data movement between computing and memory units, and exploits the intrinsic parallelism in memory arrays. Neural-inspired computing implements cognitive and intelligent functions through a wide range of approaches, e.g., deep neural network, spiking neural network, hyperdimensional computing, probabilistic network, dynamic systems, etc. Although many of these approaches can be implemented in CMOS technologies, more efficient solutions may originate from the engineering and optimization of materials and devices that could enable native implementations of novel computing paradigms. For example, ferroelectric materials, binary and complex oxides, and chalcogenides have been utilized in a wide range of nonvolatile memories and analog devices, which may enable highly efficient in-memory computing and analog computing solutions. At the same time, stringent requirements exist for emerging devices to significantly outperform CMOS in novel computing paradigms, e.g., high density, fast speed, low power, high endurance, long retention, wide analog tunability, asymmetry, etc. [3] Specific requirements vary from application to application. Therefore, device-architecture co-design and co-optimization are important to address these requirements. A holistic approach from basic material exploration to device engineering and further up to architecture co-design has been adopted in more recent research programs, e.g., Energy-Efficient Computing from Devices to Architectures (E2CDA) [4]. This presentation will review the opportunities and challenges of emerging materials and devices for energy-efficient nanoelectronics, and highlight the approaches and perspectives of the E2CDA program. References: K. Bernstein, R.K. Cavin, W. Porod, A. Seabaugh, and J. Welser, “Device and architecture outlook for beyond CMOS switches,” IEEE Proc. 98(12), 2169-2184 (2010). C. Pan and A. Naeemi, “Non-Boolean computing benchmarking for beyond-CMOS devices based on cellular neural network,” IEEE J. Explor. Solid-State Comp. Dev. & Circ 2, 36-43 (2016). G.W. Burr, et al, “Neuromorphic computing using non-volatile memory,” Advances in Physics: X, 2(1), 89-124 (2017). A. Chen, “New directions of nanoelectronics research for computing,” 14th IEEE ICSICT (2018).
32
Ji, Xiaoyue, Donglian Qi, Zhekang Dong, Chun Sing Lai, Guangdong Zhou, and Xiaofang Hu. "TSSM: Three-State Switchable Memristor Model Based on Ag/TiOx Nanobelt/Ti Configuration." International Journal of Bifurcation and Chaos 31, no. 07 (June 15, 2021): 2130020. http://dx.doi.org/10.1142/s0218127421300202.
Анотація:
Memristive technologies are attractive due to their nonvolatility, high density, low power, nanoscale geometry, nonlinearity, binary/multiple memory capacity, and negative differential resistance. For memristive devices, a model corresponding with practical behavioral characteristics is highly favorable for the realization of its neuromorphic system and applications. In this paper, we propose a novel memristor model based on the Ag/TiOx nanobelt/Ti configuration, which can reflect three different states (i.e. original stage, transition stage, and resistive switching state) of the physical memristor with a satisfactory fitting precision (greater than 99.88%). Meanwhile, this work gives (1) an insight onto the electrical characteristics of the memristor model under different humidity conditions; (2) the influence of the water molecular concentration on the memristor behavior, which is of importance for the memristor fabrication and subsequent applications. For verification purposes, the proposed three-state switchable memristor is applied into the memristor-based logic implementation. The experimental results demonstrate that the constructed circuit is able to realize basic Boolean logic operations with fast response speed and high efficiency.
33
Przyczyna, Dawid, Krzysztof Mech, Ewelina Kowalewska, Mateusz Marzec, Tomasz Mazur, Piotr Zawal, and Konrad Szaciłowski. "The Memristive Properties and Spike Timing-Dependent Plasticity in Electrodeposited Copper Tungstates and Molybdates." Materials 16, no. 20 (October 13, 2023): 6675. http://dx.doi.org/10.3390/ma16206675.
Анотація:
Memristors possess non-volatile memory, adjusting their electrical resistance to the current that flows through them and allowing switching between high and low conducting states. This technology could find applications in fields such as IT, consumer electronics, computing, sensors, and medicine. In this paper, we report successful electrodeposition of thin-film materials consisting of copper tungstate and copper molybdate (CuWO4 and Cu3Mo2O9), which showed notable memristive properties. Material characterisation was performed with techniques such as XRD, XPS, and SEM. The electrodeposited materials exhibited the ability to switch between low and high resistive states during varied cyclic scans and short-term impulses. The retention time of these switched states was also explored. Using these materials, the effects seen in biological systems, specifically spike timing-dependent plasticity, were simulated, being based on analogue operation of the memristors to achieve multiple conductivity states. Bio-inspired simulations performed directly on the material could possibly offer energy and time savings for classical computations. Memristors could be crucial for the advancement of high-efficiency, low-energy neuromorphic electronic devices and technologies in the future.
34
Passian, Ali, and Neena Imam. "Nanosystems, Edge Computing, and the Next Generation Computing Systems." Sensors 19, no. 18 (September 19, 2019): 4048. http://dx.doi.org/10.3390/s19184048.
Анотація:
It is widely recognized that nanoscience and nanotechnology and their subfields, such as nanophotonics, nanoelectronics, and nanomechanics, have had a tremendous impact on recent advances in sensing, imaging, and communication, with notable developments, including novel transistors and processor architectures. For example, in addition to being supremely fast, optical and photonic components and devices are capable of operating across multiple orders of magnitude length, power, and spectral scales, encompassing the range from macroscopic device sizes and kW energies to atomic domains and single-photon energies. The extreme versatility of the associated electromagnetic phenomena and applications, both classical and quantum, are therefore highly appealing to the rapidly evolving computing and communication realms, where innovations in both hardware and software are necessary to meet the growing speed and memory requirements. Development of all-optical components, photonic chips, interconnects, and processors will bring the speed of light, photon coherence properties, field confinement and enhancement, information-carrying capacity, and the broad spectrum of light into the high-performance computing, the internet of things, and industries related to cloud, fog, and recently edge computing. Conversely, owing to their extraordinary properties, 0D, 1D, and 2D materials are being explored as a physical basis for the next generation of logic components and processors. Carbon nanotubes, for example, have been recently used to create a new processor beyond proof of principle. These developments, in conjunction with neuromorphic and quantum computing, are envisioned to maintain the growth of computing power beyond the projected plateau for silicon technology. We survey the qualitative figures of merit of technologies of current interest for the next generation computing with an emphasis on edge computing.
35
Song, Young-Woong, Min-Kyu Song, Yoon Jeong Hyun, Daehwan Choi, and J. Y. Kwon. "Fluoropolymer Passivation Enhanced Switching Endurance of MoS2 Memristors." ECS Meeting Abstracts MA2022-01, no. 18 (July 7, 2022): 1029. http://dx.doi.org/10.1149/ma2022-01181029mtgabs.
Анотація:
The advances in computing and mobile devices have realized massive generation, collection, and processing of data. The concept "Internet of things" and "Big data" have necessiated large-scale parallel processing, as computing of collected data is highly dependent on matrix multiplication process, which in inherently parallel. In this context, conventional machines are exhibiting their limits as they are unsuited for parallel processing, resulting in low performance and high power consumption [1]. The need for advances in parallel processing hardware is emerging. In this regard, memristors have gathered attention for their high potentials as parallel processing units. There have been reports of memristors with high on/off ratio, fine retention, high switching enderance, and fast switching spped, etc [2-4]. Memristor is a non-volatile memory which stores data as internal resistance, modulated by applied voltage. Even though memristors contain high capabilities for parallel processing, scaling of devices still remain as a challenge. Conventional materials have their limit for both lateral and vertical scaling. To achieve a major breakthrough, we have fabricated memristors based on 2D materials with atomic-scale thickness of ~ 7 nm [5]. As surface-to-volume ratio is high in 2D materials, they are chemically active, leading to susceptible properties to external environment and instability in fabricated devices with 2D materials. To compensate the drawback, we applied fluoropolymer passivation layer and obtained stable switching endurance for 100 potentiation & depression cycles with 25 states. For device fabrication, we used direct current sputtering & wet etching to pattern bottom Au/Cr (50/5 nm) electrodes. Molybdenum disulfide flakes were mechanically exfoliated from bulk mineral and dry-transferred onto bottom electrodes by PDMS (Polydimethylsiloxane) stamps. Top electrodes were patterned by photolithography & evaporation of Ni/Au (5/100 nm). With the introduction of 2D materials to next-generation electronics, memristors can be even more revolutionized towards extreme scaling of devices. By compensating susceptibility in the material itself and studying degradation mechanism, parallel computing would be realized away from power-plugged environment, and accelerate artificial intelligence in our everyday lives. Figure Caption Fig 1. Electrical characterization of the resistive switching MoS2 memristor with non-volatile memory behavior. (a) Semi-log plots of IV curve, resistive switching triggered by voltage sweeping. Inset: False-color SEM image of a fabricated device; scale bar: 4μm. (b) Scheme for pulse voltages (top), potentiation and depression of conductance states (bottom). (c) Encapsulation-enhanced switching stability of MoS2 memristors. Detailed illustration of 20 PD cycles in CYTOP-encapsulated (top) and bare devices (bottom). References [1] J. Backus, Can programming be liberated from the von neumann style? A functional style and its algebra of programs, Commun. ACM 21 (1978) 613–641. [2] J.J. Yang, D.B. Strukov, D.R. Stewart, Memristive devices for computing, Nat. Nanotechnol., 8 (2013) 13–24. [3] C.H. Kim, S. Lim, S.Y. Woo, W.M. Kang, Y.T. Seo, S.T. Lee, S. Lee, D. Kwon, S. Oh, Y. Noh, H. Kim, J. Kim, J.H. Bae, J.H. Lee, Emerging memory technologies for neuromorphic computing, Nanotechnology 30 (2019) 032001. [4] M.-K. Song, S.D. Namgung, D. Choi, H. Kim, H. Seo, M. Ju, Y.H. Lee, T. Sung, Y.- S. Lee, K.T. Nam, J.-Y. Kwon, Proton-enabled activation of peptide materials for biological bimodal memory, Nat. Commun. 11 (2020) 5896. [5] Y.-W. Song, M.-K. Song, D. Choi, J.-Y. Kwon, Encapsulation-enhanced switching stability of MoS2 memristors, J. Alloys Compd. 885 (2021) 161016. Figure 1
36
Qin, Fei, and Sunghwan Lee. "(Digital Presentation) Investigation of Top Electrodes Impact on Performance of Transparent Amorphous Indium Gallium Zinc Oxide (a-InGaZnO) Based Resistive Random Access Memory." ECS Meeting Abstracts MA2022-01, no. 19 (July 7, 2022): 1075. http://dx.doi.org/10.1149/ma2022-01191075mtgabs.
Анотація:
The traditional von Neumann architecture limits the increase in computing efficiency and results in massive power consumption in modern computers due to the separation of storage and processing units. The novel neuromorphic computation system, an in-memory computing architecture with low power consumption, is aimed to break the bottleneck and meet the needs of the next generation of artificial intelligence (AI) systems. Thus, it is urgent to find a memory technology to implement the neuromorphic computing nanosystem. Nowadays, the silicon-based flash memory dominates non-volatile memory market, however, it is facing challenging issues to achieve the requirements of future data storage device development due to the drawbacks, such as scaling issue, relatively slow operation speed, and high voltage for program/erase operations. The emerging resistive random-access memory (RRAM) has prompted extensive research as its simple two-terminal structure, including top electrode (TE) layer, bottom electrode (BE) layer, and an intermediate resistive switching (RS) layer. It can utilize a temporary and reversible dielectric breakdown to cause the RS phenomenon between the high resistance state (HRS) and the low resistance state (LRS). RRAM is expected to outperform conventional memory device with the advantages, notably its low-voltage operation, short programming time, great cyclic stability, and good scalability. Among the materials for RS layer, indium gallium zinc oxide (IGZO) has shown attractive prospects in abundance and high atomic diffusion property of oxygen atoms, transparency. Additionally, its electrical properties can be easily modulated by controlling the stoichiometric ratio of indium and gallium as well as oxygen potential in the sputter gas. Moreover, since the IGZO can be applied to both the thin-film transistor (TFT) channel and RS layer, it has a great potential for fully integrated transparent electronics application. In this work, we proposed amorphous transparent IGZO-based RRAMs and investigated switching behaviors of the memory cells prepared with different top electrodes. First, ITO was choosing to serve as both TE and BE to achieve high transmittance. A multi-target magnetron sputtering system was employed to deposit all three layers (TE, RS, BE layers) on glass substrate. I-V characteristics were evaluated by a semiconductor parameter analyzer, and the bipolar RS feature of our RRAM devices was demonstrated by typical butterfly curves. The optical transmission analysis was carried out via a UV-Vis spectrometer and the average transmittance was around 80% out of entire devices in the visible-light wavelength range, implying high transparency. We adjusted the oxygen partial pressure during the sputtering of IGZO to optimize the property because the oxygen vacancy concentration governs the RS performance. Electrode selection is crucial and can impact the performance of the whole device. Thus, Cu TE was chosen for our second type of device because the diffusion of Cu ions can be beneficial for the formation of the conductive filament (CF). A ~5 nm SiO2 barrier layer was employed between TE and RS layers to confine the diffusion of Cu into the RS layer. At the same time, this SiO2 inserting layer can provide an additional interfacial series resistance in the device to lower the off current, consequently, improve the on/off ratio and whole performance. Finally, an oxygen affinity metal Ti was selected as the TE for our third type of device because the concentration of the oxygen atoms can be shifted towards the Ti electrode, which provides an oxygengettering activity near the Ti metal. This process may in turn lead to the formation of a sub-stoichiometric region in the neighboring oxide that is believed to be the origin of better performance. In conclusion, the transparent amorphous IGZO-based RRAMs were established. To tune the property of RS layer, the sputtering conditions of RS were varied. To investigate the influence of TE selections on switching performance of RRAMs, we integrated a set of TE materials, and a barrier layer on IGZO-based RRAM and compared the switch characteristics. Our encouraging results clearly demonstrate that IGZO is a promising material in RRAM applications and breaking the bottleneck of current memory technologies.
37
Woo, Jiyong, Jeong Hun Kim, Jong‐Pil Im, and Seung Eon Moon. "Recent Advancements in Emerging Neuromorphic Device Technologies." Advanced Intelligent Systems 2, no. 10 (August 23, 2020): 2000111. http://dx.doi.org/10.1002/aisy.202000111.
38
Zhou, Kui, Ziqi Jia, Xin-Qi Ma, Wenbiao Niu, Yao Zhou, Ning Huang, Guanglong Ding, 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.
Анотація:
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 tremendous interest for electronic and optoelectronic applications. Recent research results highlight the significance of implementing graphene into artificial synaptic devices. Herein, to better demonstrate the potential of graphene-based synaptic devices, the fabrication technologies of graphene are first presented. Then, the roles of graphene in various synaptic devices are explained. Furthermore, their typical optoelectronic applications in neuromorphic systems are reviewed. Finally, outlooks for development of synaptic devices based on graphene are also proposed. This review will provide a comprehensive understanding of graphene fabrication technologies and graphene-based synaptic device for optoelectronic applications, also present an outlook for development of graphene-based synaptic device in future neuromorphic systems.
39
Wan, Changjin, Mengjiao Pei, Kailu Shi, Hangyuan Cui, Haotian Long, Lesheng Qiao, Qianye Xing, and Qing Wan. "Toward a Brain‐Neuromorphics Interface." Advanced Materials, February 10, 2024. http://dx.doi.org/10.1002/adma.202311288.
Анотація:
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 computation power with extremely high energy‐efficiency by implementing in‐materia computing such as in situ vector‐matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.This article is protected by copyright. All rights reserved
40
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.
Анотація:
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 traditional optical communications, due to its innate superiority in speed, bandwidth, energy efficiency, etc. Thereinto, photo-assisted and photoelectrical synapses are developed for neuromorphic computing. Additionally, both the storage and readout processes can be implemented in optical domain in some emerging photonic devices to leverage unique properties of photonics. In this review, we introduce typical photonic neuromorphic devices rooted from emerging memory technologies together with corresponding operational mechanisms. In the end, the advantages and limitations of these devices originated from different modulation means are listed and discussed.
41
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 (October 23, 2019). http://dx.doi.org/10.1038/s41598-019-51814-5.
Анотація:
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 more reliable digital-type switching device has been recently demonstrated, i.e., a binarized neural network (BNN). The synaptic device is a more mature digital-type switching device, and the training/recognition algorithm developed for the BNN enables the task of facial image classification with a supervised training scheme. Here, we quantitatively investigate the effects of device parameter variations on the classification accuracy; the parameters include the number of weight states (Nstate), the weight update margin (ΔG), and the weight update variation (Gvar). This analysis demonstrates the feasibility of the BNN and introduces a practical neuromorphic system based on mature, conventional digital device technologies.
42
Donati, Elisa, and Giacomo Valle. "Neuromorphic hardware for somatosensory neuroprostheses." Nature Communications 15, no. 1 (January 16, 2024). http://dx.doi.org/10.1038/s41467-024-44723-3.
Анотація:
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 design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies.
43
Covi, Erika, Elisa Donati, Xiangpeng Liang, David Kappel, Hadi Heidari, Melika Payvand, and Wei Wang. "Adaptive Extreme Edge Computing for Wearable Devices." Frontiers in Neuroscience 15 (May 11, 2021). http://dx.doi.org/10.3389/fnins.2021.611300.
Анотація:
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 this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.
44
Deng, Sunbin, Haoming Yu, Tae Joon Park, A. N. M. Nafiul Islam, Sukriti Manna, Alexandre Pofelski, Qi Wang, et al. "Selective area doping for Mott neuromorphic electronics." Science Advances 9, no. 11 (March 15, 2023). http://dx.doi.org/10.1126/sciadv.ade4838.
Анотація:
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 devices are successfully integrated on a single chip. Feedforward excitation and inhibition neural motifs are demonstrated at hardware level, followed by simulation of network-level handwritten digit and fashion product recognition tasks with experimental characteristics. Spatially selective electron doping opens up previously unidentified avenues for integration of emerging correlated semiconductors in electronic device technologies.
45
Liu, Xuerong, Cui Sun, Xiaoyu Ye, Xiaojian Zhu, Cong Hu, Hongwei Tan, Shang He, Mengjie Shao, and Run‐Wei Li. "Neuromorphic Nanoionics for human‐machine Interaction: from Materials to Applications." Advanced Materials, February 29, 2024. http://dx.doi.org/10.1002/adma.202311472.
Анотація:
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 comprehensive review of recent developments in nanoionic device‐based neuromorphic computing technologies and their pivotal role in shaping the next‐generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion‐gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.This article is protected by copyright. All rights reserved
46
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.
Анотація:
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 classifying neuromorphic AI systems by the brain features they use: connectionism, parallelism, asynchrony, impulse nature of information transfer, on-device-learning, local learning, sparsity, analog, and in-memory computing. In addition to reviewing new architectural approaches used by neuromorphic devices based on existing silicon microelectronics technologies, we also discuss the prospects for using a new memristor element base. Examples of recent advances in the use of memristors in neuromorphic applications are also given.
47
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 (June 1, 2024). http://dx.doi.org/10.1063/5.0204104.
Анотація:
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-processing capability, which is a cornerstone in artificial intelligence evolution and human–machine interface enhancement. Herein, we highlight recent advancements in neuromorphic systems designed to mimic the functionalities of the human tactile sensory system, a critical component of somatosensory functions. After discussing the tactile sensors which biomimic the mechanoreceptors, insights are provided to integrate artificial synapses and neural networks for advanced information recognition emphasizing the efficiency and sophistication of integrated system. It showcases the evolution of tactile recognition biomimicry, extending beyond replicating the physical properties of human skin to biomimicking tactile sensations and efferent/afferent nerve functions. These developments demonstrate significant potential for creating sensitive, adaptive, plastic, and memory-capable devices for human-centric applications. Moreover, this review addresses the impact of skin-related diseases on tactile perception and the research toward developing artificial skin to mimic sensory and motor functions, aiming to restore tactile reception for perceptual challenged individuals. It concludes with an overview of state-of-the-art biomimetic artificial tactile systems based on the manufacturing–structure–property–performance relationships, from devices mimicking mechanoreceptor functions to integrated systems, underscoring the promising future of artificial tactile sensing and neuromorphic device innovation.
48
Li, Shen-Yi, Ji-Tuo Li, Kui Zhou, Yan Yan, Guanglong Ding, Su-Ting Han, and Ye Zhou. "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.
Анотація:
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 extremely important to design neuromorphic devices with sensing ability and the properties of low power consumption and high switching speed for exploring in-sensor computing devices and systems. In this review, we provide an elementary introduction to the structures and properties of two common optoelectronic materials, perovskites and transition metal dichalcogenides (TMDs). Subsequently, we discuss the fundamental concepts of neuromorphic devices, including device structures and working mechanisms. Furthermore, we summarize and extensively discuss the applications of perovskites and TMDs in in-sensor computing. Finally, we propose potential strategies to address challenges and offer a brief outlook on the application of optoelectronic materials in term of in-sensor computing.
49
Merces, Leandro, Letícia Mariê Minatogau Ferro, Ali Nawaz, and Prashant Sonar. "Advanced Neuromorphic Applications Enabled by Synaptic Ion‐Gating Vertical Transistors." Advanced Science, May 17, 2024. http://dx.doi.org/10.1002/advs.202305611.
Анотація:
AbstractBioinspired synaptic devices have shown great potential in artificial intelligence and neuromorphic electronics. Low energy consumption, multi‐modal sensing and recording, and multifunctional integration are critical aspects limiting their applications. Recently, a new synaptic device architecture, the ion‐gating vertical transistor (IGVT), has been successfully realized and timely applied to perform brain‐like perception, such as artificial vision, touch, taste, and hearing. In this short time, IGVTs have already achieved faster data processing speeds and more promising memory capabilities than many conventional neuromorphic devices, even while operating at lower voltages and consuming less power. This work focuses on the cutting‐edge progress of IGVT technology, from outstanding fabrication strategies to the design and realization of low‐voltage multi‐sensing IGVTs for artificial‐synapse applications. The fundamental concepts of artificial synaptic IGVTs, such as signal processing, transduction, plasticity, and multi‐stimulus perception are discussed comprehensively. The contribution draws special attention to the development and optimization of multi‐modal flexible sensor technologies and presents a roadmap for future high‐end theoretical and experimental advancements in neuromorphic research that are mostly achievable by the synaptic IGVTs.
50
Beilliard, Yann, and Fabien Alibart. "Multi-Terminal Memristive Devices Enabling Tunable Synaptic Plasticity in Neuromorphic Hardware: A Mini-Review." Frontiers in Nanotechnology 3 (November 19, 2021). http://dx.doi.org/10.3389/fnano.2021.779070.
Анотація:
Neuromorphic computing based on spiking neural networks has the potential to significantly improve on-line learning capabilities and energy efficiency of artificial intelligence, specially for edge computing. Recent progress in computational neuroscience have demonstrated the importance of heterosynaptic plasticity for network activity regulation and memorization. Implementing heterosynaptic plasticity in hardware is thus highly desirable, but important materials and engineering challenges remain, calling for breakthroughs in neuromorphic devices. In this mini-review, we propose an overview of the latest advances in multi-terminal memristive devices on silicon with tunable synaptic plasticity, enabling heterosynaptic plasticity in hardware. The scalability and compatibility of the devices with industrial complementary metal oxide semiconductor (CMOS) technologies are discussed.