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Literatura académica sobre el tema "Neuromorphic devices"

Autor: Grafiati

Publicado: 14 de diciembre de 2024

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Artículos de revistas sobre el tema "Neuromorphic devices"

Ielmini, Daniele y Stefano Ambrogio. "Emerging neuromorphic devices". Nanotechnology 31, n.º 9 (9 de diciembre de 2019): 092001. http://dx.doi.org/10.1088/1361-6528/ab554b.

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Guo, Zhonghao. "Synaptic device-based neuromorphic computing in artificial intelligence". Applied and Computational Engineering 65, n.º 1 (23 de mayo de 2024): 253–59. http://dx.doi.org/10.54254/2755-2721/65/20240511.

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The application of synaptic device-based neuromorphic computing in artificial intelligence is an emerging research field aimed at simulating the structure and function of the human brain and realizing high-efficiency, low-power, and adaptive intelligent computing. This paper reviews the principles, growth and challenges of neuromorphic devices based on synapses computing and its applications and perspectives in artificial intelligence fields like an image processing as well as natural language processing. The paper first introduces the basic concepts, properties and classification of synaptic devices, as well as the basic framework and algorithms of neuromorphic computing. Then, the paper analyzes the advantages and difficulties of neuromorphic computing based on synaptic devices, including the preparation, testing, modelling and integration of the devices, as well as the systems architecture, programming and optimization. Then, this paper gives examples of the applications and effects of synaptic device-based neuromorphic computing in artificial intelligence fields such as image processing and natural language processing, including image denoising, image segmentation, image recognition, text classification, text summarization, and text generation. Finally, this paper summarizes the current research status and future synaptic device-based neuromorphic computing trends. It puts forward some research directions and suggestions to promote the development and innovation in this field.

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Park, Jisoo, Jihyun Shin y Hocheon Yoo. "Heterostructure-Based Optoelectronic Neuromorphic Devices". Electronics 13, n.º 6 (14 de marzo de 2024): 1076. http://dx.doi.org/10.3390/electronics13061076.

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The concept of neuromorphic devices, aiming to process large amounts of information in parallel, at low power, high speed, and high efficiency, is to mimic the functions of human brain by emulating biological neural behavior. Optoelectronic neuromorphic devices are particularly suitable for neuromorphic applications with their ability to generate various pulses based on wavelength and to control synaptic stimulation. Each wavelength (ultraviolet, visible, and infrared) has specific advantages and optimal applications. Here, the heterostructure-based optoelectronic neuromorphic devices are explored across the full wavelength range (ultraviolet to infrared) by categorizing them on the basis of irradiated wavelength and structure (two-terminal and three-terminal) with respect to emerging optoelectrical materials. The relationship between neuromorphic applications, light wavelength, and mechanism is revisited. Finally, the potential and challenging aspects of next-generation optoelectronic neuromorphic devices are presented, which can assist in the design of suitable materials and structures for neuromorphic-based applications.

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Huang, Wen, Huixing Zhang, Zhengjian Lin, Pengjie Hang y Xing’ao Li. "Transistor-Based Synaptic Devices for Neuromorphic Computing". Crystals 14, n.º 1 (9 de enero de 2024): 69. http://dx.doi.org/10.3390/cryst14010069.

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Currently, neuromorphic computing is regarded as the most efficient way to solve the von Neumann bottleneck. Transistor-based devices have been considered suitable for emulating synaptic functions in neuromorphic computing due to their synergistic control capabilities on synaptic weight changes. Various low-dimensional inorganic materials such as silicon nanomembranes, carbon nanotubes, nanoscale metal oxides, and two-dimensional materials are employed to fabricate transistor-based synaptic devices. Although these transistor-based synaptic devices have progressed in terms of mimicking synaptic functions, their application in neuromorphic computing is still in its early stage. In this review, transistor-based synaptic devices are analyzed by categorizing them into different working mechanisms, and the device fabrication processes and synaptic properties are discussed. Future efforts that could be beneficial to the development of transistor-based synaptic devices in neuromorphic computing are proposed.

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Lim, Jung Wook, Su Jae Heo, Min A. Park y Jieun Kim. "Synaptic Transistors Exhibiting Gate-Pulse-Driven, Metal-Semiconductor Transition of Conduction". Materials 14, n.º 24 (7 de diciembre de 2021): 7508. http://dx.doi.org/10.3390/ma14247508.

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Neuromorphic devices have been investigated extensively for technological breakthroughs that could eventually replace conventional semiconductor devices. In contrast to other neuromorphic devices, the device proposed in this paper utilizes deep trap interfaces between the channel layer and the charge-inducing dielectrics (CID). The device was fabricated using in-situ atomic layer deposition (ALD) for the sequential deposition of the CID and oxide semiconductors. Upon the application of a gate bias pulse, an abrupt change in conducting states was observed in the device from the semiconductor to the metal. Additionally, numerous intermediate states could be implemented based on the number of cycles. Furthermore, each state persisted for 10,000 s after the gate pulses were removed, demonstrating excellent synaptic properties of the long-term memory. Moreover, the variation of drain current with cycle number demonstrates the device’s excellent linearity and symmetry for excitatory and inhibitory behaviors when prepared on a glass substrate intended for transparent devices. The results, therefore, suggest that such unique synaptic devices with extremely stable and superior properties could replace conventional semiconducting devices in the future.

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Diao, Yu, Yaoxuan Zhang, Yanran Li y Jie Jiang. "Metal-Oxide Heterojunction: From Material Process to Neuromorphic Applications". Sensors 23, n.º 24 (12 de diciembre de 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 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.

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Feng, Chenyin, Wenwei Wu, Huidi Liu, Junke Wang, Houzhao Wan, Guokun Ma y Hao Wang. "Emerging Opportunities for 2D Materials in Neuromorphic Computing". Nanomaterials 13, n.º 19 (7 de octubre de 2023): 2720. http://dx.doi.org/10.3390/nano13192720.

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Recently, two-dimensional (2D) materials and their heterostructures have been recognized as the foundation for future brain-like neuromorphic computing devices. Two-dimensional materials possess unique characteristics such as near-atomic thickness, dangling-bond-free surfaces, and excellent mechanical properties. These features, which traditional electronic materials cannot achieve, hold great promise for high-performance neuromorphic computing devices with the advantages of high energy efficiency and integration density. This article provides a comprehensive overview of various 2D materials, including graphene, transition metal dichalcogenides (TMDs), hexagonal boron nitride (h-BN), and black phosphorus (BP), for neuromorphic computing applications. The potential of these materials in neuromorphic computing is discussed from the perspectives of material properties, growth methods, and device operation principles.

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Kim, Dongshin, Ik-Jyae Kim y Jang-Sik Lee. "Memory Devices for Flexible and Neuromorphic Device Applications". Advanced Intelligent Systems 3, n.º 5 (25 de enero de 2021): 2000206. http://dx.doi.org/10.1002/aisy.202000206.

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Huang, Yi, Fatemeh Kiani, Fan Ye y Qiangfei Xia. "From memristive devices to neuromorphic systems". Applied Physics Letters 122, n.º 11 (13 de marzo de 2023): 110501. http://dx.doi.org/10.1063/5.0133044.

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Progress in hardware and algorithms for artificial intelligence (AI) has ushered in large machine learning models and various applications impacting our everyday lives. However, today's AI, mainly artificial neural networks, still cannot compete with human brains because of two major issues: the high energy consumption of the hardware running AI models and the lack of ability to generalize knowledge and self-adapt to changes. Neuromorphic systems built upon emerging devices, for instance, memristors, provide a promising path to address these issues. Although innovative memristor devices and circuit designs have been proposed for neuromorphic computing and applied to different proof-of-concept applications, there is still a long way to go to build large-scale low-power memristor-based neuromorphic systems that can bridge the gap between AI and biological brains. This Perspective summarizes the progress and challenges from memristor devices to neuromorphic systems and proposes possible directions for neuromorphic system implementation based on memristive devices.

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Machado, Pau, Salvador Manich, Álvaro Gómez-Pau, Rosa Rodríguez-Montañés, Mireia Bargalló González, Francesca Campabadal y Daniel Arumí. "Programming Techniques of Resistive Random-Access Memory Devices for Neuromorphic Computing". Electronics 12, n.º 23 (27 de noviembre de 2023): 4803. http://dx.doi.org/10.3390/electronics12234803.

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Neuromorphic computing offers a promising solution to overcome the von Neumann bottleneck, where the separation between the memory and the processor poses increasing limitations of latency and power consumption. For this purpose, a device with analog switching for weight update is necessary to implement neuromorphic applications. In the diversity of emerging devices postulated as synaptic elements in neural networks, RRAM emerges as a standout candidate for its ability to tune its resistance. The learning accuracy of a neural network is directly related to the linearity and symmetry of the weight update behavior of the synaptic element. However, it is challenging to obtain such a linear and symmetrical behavior with RRAM devices. Thus, extensive research is currently devoted at different levels, from material to device engineering, to improve the linearity and symmetry of the conductance update of RRAM devices. In this work, the experimental results based on different programming pulse conditions of RRAM devices are presented, considering both voltage and current pulses. Their suitability for application as analog RRAM-based synaptic devices for neuromorphic computing is analyzed by computing an asymmetric nonlinearity factor.

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Tesis sobre el tema "Neuromorphic devices"

Islam, Rabiul. "Fabrication and Electrical Characterization of Organic Neuromorphic Memory Devices". Master's thesis, Department of Materials Science, TU Darmstadt, 2019. https://tuprints.ulb.tu-darmstadt.de/9208/1/Final%20Thesis%20Report_Rabiul%20Islam_2997810.pdf.

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The organic polymer has gained considerable interest in the field of bioelectronics during the last few decades. Organic materials based devices have several unique characteristics; low-cost and low thermal budget fabrication processes, tunable properties through chemical synthesis, flexibility and biocompatibility. Those entire features make organic materials suitable for new functionalities in comparison to their inorganic counterparts. Moreover, the attributes mentioned earlier give an additional degree of freedom to use organic materials in neuromorphic devices whose functions have the potential to induce biological realism in brain-inspired information processing. Nowadays, neuromorphic devices have attracted the interest in research and industry. The use of organic materials might lead to a new class of neuromorphic devices that has several applications in areas that range from brain-computer interfaces to circuits for local data processing in energy restricted environments. However, flexibility and biocompatibility helps to optimize the mechanical mismatch between electronics and biological substances that might be a new way of signal processing at the interface with biology. In this thesis project, three-terminal organic polymer-based Organic Electrochemical Transistors (OECTs) have fabricated in cleanroom-based fabrication process. PEDOT:PSS and p(g2T-TT) thin-film polymers were used as active channel materials in OECTs. Ions inject from the liquid electrolytes by using a specific gate bias. The migrated ions modulate the entire bulk-volume conductivity of the organic polymer channel due to the strong coupling between ionic and electronic charges within the channel. Several electrical characterizations of OECTs were investigated in the presence of liquid electrolytes. The memory phenomena of PEDOT:PSS and p(g2T-TT) polymer-based OECTs were systematically studied in this work. It was observed that PEDOT:PSS organic polymer shows no memory properties/negligible memory, and p(g2T-TT) polymer exhibits memory phenomena due to its unique polymer structure. It also seen that the memory process in p(g2T-TT) polymer is a reversible process that can be return to its initial state by applying opposite gate bias. Beside it, the polymer's behavior also was investigated in contact with and without aqueous solutions. Additionally, it observed that p(g2T-TT) polymer is less hydrophilic compared to PEDOT:PSS due to its intrinsic properties. Multiple memory devices were fabricated at different times and reproducible memory phenomenon was observed in OECTs.

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Hirtzlin, Tifenn. "Digital Implementation of Neuromorphic systems using Emerging Memory devices". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPAST071.

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Depuis les années soixante-dix l'évolution des performances des circuits électroniques repose exclusivement sur l'amélioration des performances des transistors. Ce composant a des propriétés extraordinaires puisque lorsque ses dimensions sont réduites, toutes ses caractéristiques sont améliorées. Mais, dû à certaines limites physiques fondamentales, la diminution des dimensions des transistors n’est plus possible. Néanmoins, de nouveaux nano-composants mémoire innovants qui peuvent être intégré conjointement avec les transistors voient le jour tant au niveau académique qu'industriel, ce qui constitue une opportunité pour repenser complètement l'architecture des circuits électroniques actuels. L'une des voies de recherche possible est l’inspiration du fonctionnement du cerveau biologique. Ce dernier peut accomplir des tâches complexes et variées en consommant très peu d’énergie. Ces travaux de thèse explorent trois paradigmes neuro-inspirés pour l'utilisation de ces composants mémoire. Chacune de ces approches explore différentes problématiques du calcul en mémoire
While electronics has prospered inexorably for several decades, its leading source of progress will stop in the next coming years, due to the fundamental technological limits of transistors. Nevertheless, microelectronics is currently offering a major breakthrough: in recent years, memory technologies have undergone incredible progress, opening the way for multiple research venues in embedded systems. Additionally, a major feature for future years will be the ability to integrate different technologies on the same chip. new emerging memory devices that can be embedded in the core of the CMOS, such as Resistive Random Access Memory (RRAM) or Spin Torque Magnetic Tunnel Junction (STMRAM) based on naturally intelligent inmemory-computing architecture. Three braininspired algorithms are carefully examined: Bayesian reasoning binarized neural networks, and an approach that further exploits the intrinsic behavior of components, population coding of neurons. Each of these approaches explores different aspects of in-memory computing

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Lai, Qianxi. "Electrically configurable materials and devices for intelligent neuromorphic applications". Diss., Restricted to subscribing institutions, 2009. http://proquest.umi.com/pqdweb?did=1872061101&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Azam, Md Ali. "Energy Efficient Spintronic Device for Neuromorphic Computation". VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/6036.

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Future computing will require significant development in new computing device paradigms. This is motivated by CMOS devices reaching their technological limits, the need for non-Von Neumann architectures as well as the energy constraints of wearable technologies and embedded processors. The first device proposal, an energy-efficient voltage-controlled domain wall device for implementing an artificial neuron and synapse is analyzed using micromagnetic modeling. By controlling the domain wall motion utilizing spin transfer or spin orbit torques in association with voltage generated strain control of perpendicular magnetic anisotropy in the presence of Dzyaloshinskii-Moriya interaction (DMI), different positions of the domain wall are realized in the free layer of a magnetic tunnel junction to program different synaptic weights. Additionally, an artificial neuron can be realized by combining this DW device with a CMOS buffer. The second neuromorphic device proposal is inspired by the brain. Membrane potential of many neurons oscillate in a subthreshold damped fashion and fire when excited by an input frequency that nearly equals their Eigen frequency. We investigate theoretical implementation of such “resonate-and-fire” neurons by utilizing the magnetization dynamics of a fixed magnetic skyrmion based free layer of a magnetic tunnel junction (MTJ). Voltage control of magnetic anisotropy or voltage generated strain results in expansion and shrinking of a skyrmion core that mimics the subthreshold oscillation. Finally, we show that such resonate and fire neurons have potential application in coupled nanomagnetic oscillator based associative memory arrays.

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Zaman, Ayesha. "Modeling and Experimental Characterization of Memristor Devices for Neuromorphic Computing". University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton159636782366637.

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Mandal, Saptarshi. "Study of Mn doped HfO2 based Synaptic Devices for Neuromorphic Applications". University of Toledo / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1384535471.

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Wenke, Sam. "Application and Simulation of Neuromorphic Devices for use in Neural Networks". University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1523635913955071.

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Pedró, Puig Marta. "Implementation of unsupervised learning mechanisms on OxRAM devices for neuromorphic computing applications". Doctoral thesis, Universitat Autònoma de Barcelona, 2019. http://hdl.handle.net/10803/667894.

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La present tesi recull els resultats de la recerca orientada a aportar una metodologia de caracterització elèctrica, modelat i simulació per a dispositius de commutació resistiva, quan es consideren aplicacions de computació neuromòrfica basades en aprenentatge no-supervisat, àmpliament demandades en l’actualitat com a solució de baix consum a les següents problemàtiques: per una banda, la limitació de la velocitat en la transferència de dades entre les unitats de memoria i processament que té lloc en les arquitectures de computador convencional (von Neumann). Per altra banda, la necessitat creixent de sistemes computacionals que realitzin tasques de classificació, anàlisi i inferència de quantitats massives de dades (per exemple, per a aplicacions de Big Data), junt amb tasques de detecció de patrons, predicció de comportaments i presa de decisions (aplicacions enfocades a Internet-of-Things, entre d’altres). En concret, s’investiguen els dispositius Oxide-based Resistive Random Access Memory (OxRAM) com a candidats per a la implementació electrònica de sinapsis en xarxes neuronals artificials físiques, altrament anomenades arquitectures neuromòrfiques. En primer lloc, es presenta una introducció teòrica a les diferents tecnologies electròniques amb propietats de commutació resistiva i memòria no volàtil, junt amb les figures de mèrit de cadascuna d’aquestes, tan demostrades com projectades segons l’International Roadmap for Devices and Systems de 2018. Amb aquest primer capítol, es pretén proveïr al lector de les bases necessàries per a poder comprendre els resultats exposats en els següents capítols. A continuació i mitjançant un enfocament bottom-up dividit en tres capítols, es tracten els procediments i resultats de la caracterització elèctrica i modelat dels dispositius estudiats per a la implementació de sinapsis electròniques analògiques. Com a punt de partida, es verifica experimentalment que els dispositius compleixen els requisits necessaris per a l’aplicació indicada. Al següent capítol, es demostren de forma experimental dues regles d’aprenentatge fonamentals per a poder executar algorismes d’aprenentatge autònoms (no supervisats) sobre una arquitectura neuromòrfica basada en els dispositius analitzats. Les regles d’aprenentatge demostrades permeten que els dispositius emulin procesos i mecanismes d’aprenentatge reportats en el camp de les neurociències, tals com la dependència temporal de la plasticitat, o el fenòmen de condicionament clàssic, per al qual es replica l’experiment dels gossos de Pavlov, permetent establir els fonaments de l’aprenentatge associatiu en dos o més dispositius. Per a concloure aquesta part relativa a les sinapsis electròniques analògiques, es proposa l’adaptació hardware d’un algorisme d’aprenentatge no supervisat. L’algorisme dissenyat permet que el sistema organitzi les seves connexions de forma autònoma i no supervisada, de tal manera que, un cop entrenada, la xarxa neuronal física mostri una organització topogràfica a la seva capa de sortida, que és característica de les regions del cervell biològic dedicades al processament de la informació sensorial. A més, el disseny del sistema permet concatenar diverses xarxes neuronals per a poder executar tasques cognitives de naturalesa més complexa, tals com l’associació de diferents atributs a un mateix concepte, permetent la computació jeràrquica. L’últim capítol està dedicat a l’estudi de dispositius OxRAM quan es considera un mode d’operació de baix consum, per a la implementació de sinapsis binàries. De nou, amb una perspectiva bottom-up, es parteix de la caracterització elèctrica i modelat dels dispositius, que en aquest cas constitueixen un xip neuromòrfic. Es verifica una regla d’aprenentatge probabilística, que després s’empra en un algorisme d’aprenentatge no supervisat dissenyat per a la inferència i predicció de seqüències periòdiques. Per acabar, es discuteixen les diferències i similituds entre els dos algorismes descrits a la tesi, i es proposa com es poden fer servir cadascun d’aquests de forma conjunta i complementària.
The present thesis compiles the results of the research oriented to provide a methodology for the electrical characterization, modeling and simulation of resistive switching devices, taking into consideration neuromorphic applications based on unsupervised learning This is widely demanded today as a low-consumption solution to the following issues: on the one hand, the speed limitations that take place in data transfer between the memory and processing units that takes place in conventional computer architectures. On the other hand, the growing need for low-power computational systems that perform tasks of classification, analysis and inference of massive amounts of data (for example, for Big Data applications), together with pattern recognition, prediction of behaviors and decision-making tasks (for applications focused on Internet-of-Things, among others). Specifically, Oxide-based Resistive Random Access Memory (OxRAM) devices are investigated as candidates for the electronic implementation of synapses in physical artificial neural networks, also referred to as neuromorphic architectures. First of all, a theoretical introduction to the different electronic technologies with resistive switching and non-volatile memory properties is provided. The figures of merit demonstrated and projected of each one of them are indicated according to the International Roadmap for Devices and Systems of 2018. With this first chapter, the intention is to provide the reader with the necessary background required to understand the results outlined in the following chapters. Next, and by using a bottom-up approach divided into the three following chapters, the procedures and results of the electrical characterization and modeling of the OxRAM devices studied for the implementation of analog electronic synapses are discussed. As a starting point, it is experimentally verified that the devices meet the requirements for the indicated application. In the following chapter, two fundamental learning rules are demonstrated experimentally in order to permit the execution of an autonomous (unsupervised) learning algorithm on a neuromorphic architecture based on the tested devices. The proven learning rules allow the devices to emulate certain processes and learning mechanisms reported in the neuroscience field, such as spike-timing dependent plasticity, or the classical conditioning phenomenon, for which Pavlov’s dog experiment is replicated as to establish the foundations of associative learning, to be implemented between two or more synaptic devices. To conclude this part related to analog electronic synapses, the hardware adaptation of an unsupervised learning algorithm is proposed. The designed algorithm provides the system with the property of self-organization, in such a way that, once trained, the physical neuronal network shows a topographical organization in its output layer, which is characteristic of the sensory processing areas of the biological brain. Furthermore, the proposed design and algorithm allow the concatenation of several neuronal networks, in order to execute cognitive tasks of a more complex nature, such as the association of different attributes to the same concept, related to hierarchical computation. The last chapter is dedicated to the study of OxRAM devices when a low-power mode is considered, for the implementation of binary synapses. Again using a bottom-up perspective, the chapter begins with the electrical characterization and modeling of the devices, which in this case constitute a neuromorphic chip. A probabilistic learning rule is demonstrated, which is then used in an unsupervised on-line learning algorithm designed for the inference and prediction of periodic temporal sequences. Finally, the differences and similarities between the two algorithms described in the thesis are discussed, and a proposal is made as to how each of these can be used in a joint and complementary way.

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Ignatov, Marina [Verfasser]. "Emulation of Neural Dynamics in Neuromorphic Circuits Based on Memristive Devices / Marina Ignatov". Kiel : Universitätsbibliothek Kiel, 2018. http://d-nb.info/1156601932/34.

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Hosseini, Peiman. "Phase-change and carbon based materials for advanced memory and computing devices". Thesis, University of Exeter, 2013. http://hdl.handle.net/10871/10122.

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The aggressive scaling of CMOS technology, to reduce device size while also increasing device performance, has reached a point where continuing improvement is becoming increasingly problematic and alternative routes for the development of future memory and processing devices may be necessary; in this thesis the use of phase-change and carbon based materials as one such alternative route is investigated. As pointed out by Ovshinsky [1, 2] some phase-change material should be capable of non-binary arithmetic processing, multi-value logic and biological (neuromorphic) type processing. In this thesis, generic, nanometre-sized, phase-change pseudodevices were fabricated and utilised to perform various types of computational operations for the first time, including addition, subtraction, division, parallel factorization and logic using a novel resistive switching accumulator-type regime in the electrical domain. The same accumulator response is also shown to provide an electronic mimic of an integrate-and-fire type neuron. The accumulator-type regime uses fast electrical pulses to gradually crystallize a phase-change device in a finite number of steps and does not require a multilevel detection scheme. The phase-change materials used in this study were protected by a capping layer of sputtered amorphous carbon. It was found that this amorphous carbon layer also underwent a form of resistive switching when subjected to electrical pulses. In particular, sputtered amorphous carbon layers were found to switch from an initially high resistivity state to a low resistivity state when a voltage pulse was locally applied using a Conductive Atomic Force Microscope (CAFM) tip. Further experiments on amorphous carbon vertical pseudo-devices and lithographically defined planar devices showed that it has potential as a new material for Resistive Random Access Memory (ReRam) applications. The switching mechanism was identified as clustering of the sp2 hybridized carbon sites induced by Joule heating. It was not possible to reset the devices back to their initial high resistivity state presumably due to the highly conductive nature of sputtered amorphous carbon.

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Libros sobre el tema "Neuromorphic devices"

Yilmaz, Yalcin, Pinaki Mazumder y Idongesit Ebong. Neuromorphic Circuits for Nanoscale Devices. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338918.

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Suri, Manan, ed. Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices. New Delhi: Springer India, 2017. http://dx.doi.org/10.1007/978-81-322-3703-7.

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Neuromorphic Circuits for Nanoscale Devices. River Publishers, 2019.

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Mazumder, Pinaki, Yalcin Yilmaz y Idongesit Ebong. Neuromorphic Circuits for Nanoscale Devices. River Publishers, 2022.

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Mazumder, Pinaki, Yalcin Yilmaz y Idongesit Ebong. Neuromorphic Circuits for Nanoscale Devices. River Publishers, 2022.

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Mazumder, Pinaki, Yalcin Yilmaz y Idongesit Ebong. Neuromorphic Circuits for Nanoscale Devices. River Publishers, 2022.

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Dong, Yibo, Min Gu, Elena Goi, Yangyundou Wang y Zhengfen Wan. Neuromorphic Photonic Devices and Applications. Elsevier, 2023.

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Mazumder, Pinaki, Yalcin Yilmaz, Idongesit Ebong y Woo Hyung Lee. Neuromorphic Circuits for Nanoscale Devices. River Publishers, 2020.

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Wang, Jing, Min Gu, Elena Goi, Yangyundou Wang y Zhengfen Wan. Neuromorphic Photonic Devices and Applications. Elsevier, 2023.

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Wan, Qing y Yi Shi, eds. Neuromorphic Devices for Brain‐Inspired Computing. Wiley, 2022. http://dx.doi.org/10.1002/9783527835317.

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Capítulos de libros sobre el tema "Neuromorphic devices"

Das, Sonali. "Perovskite Based Neuromorphic Devices". En Engineering Materials, 417–46. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-57663-8_12.

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Erokhin, Victor. "Memristive Devices and Circuits". En Fundamentals of Organic Neuromorphic Systems, 1–17. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79492-7_1.

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Zahari, Finn, Martin Ziegler, Pouya Doerwald, Christian Wenger y Hermann Kohlstedt. "Neuromorphic Circuits with Redox-Based Memristive Devices". En Springer Series on Bio- and Neurosystems, 43–85. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36705-2_2.

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Resumen

AbstractThis chapter addresses opportunities for memristive devices in the framework of neuromorphic computing. Memristive devices are two-terminal circuit elements, comprising resistance and memory functionality. This simple and likewise ingeniously concept allows beneficial applications in numerous neuromorphic circuits. However, the electrical characteristics as well as the materials and technological framework of memristive devices need an optimization for each specific application. The chapter starts with a short overview of basic principles of biological data processing followed by a taxonomy of different bio-inspired computing architectures, divided into time-dependent and time-independent concepts. Furthermore, the requirements on particular memristive device properties, such as $$I\text {-}V$$ I - V linearity, switching time, retention, number of states, time-dependency, and device variability, are discussed. The results of tangible examples of digital and analog memristive switching devices used in a deep neural network based on CMOS-integrated resistive random access memory devices (RRAMs) for chronic obstructive pulmonary disease (COPD) detection, in stochastic learning, in bio-inspired analog learning, and, finally, in oscillatory computing are presented and discussed.

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Ebong, Idongesit y Pinaki Mazumder. "Neuromorphic Building Blocks with Memristors". En Neuromorphic Circuits for Nanoscale Devices, 145–68. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338918-5.

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Danial, Loai, Parul Damahe, Purvi Agrawal, Ruchi Dhamnani y Shahar Kvatinsky. "Neuromorphic Data Converters Using Memristors". En Emerging Computing: From Devices to Systems, 245–90. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7487-7_8.

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Yilmaz, Yalcin y Pinaki Mazumder. "Multi-Level Memory Architecture". En Neuromorphic Circuits for Nanoscale Devices, 117–44. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338918-4.

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Yu, Yongbin, Lefei Men, Qingqing Hu, Shouming Zhong, Nyima Tashi, Pinaki Mazumder, Idongesit Ebong, Qishui Zhong y Xingwen Liu. "Dynamic Analysis of Memristor-based Neural Network and its Application". En Neuromorphic Circuits for Nanoscale Devices, 303–49. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338918-12.

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Mazumder, Pinaki, Sing-Rong Li y Idongesit Ebong. "Tunneling-Based Cellular Nonlinear Network Architectures for Image Processing". En Neuromorphic Circuits for Nanoscale Devices, 183–203. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338918-7.

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Hu, Xiaofang, Shukai Duan, Wenbo Song, Jiagui Wu y Pinaki Mazumder. "Memristor-based Cellular Nonlinear/Neural Network: Design, Analysis and Applications". En Neuromorphic Circuits for Nanoscale Devices, 275–301. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338918-11.

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Yilmaz, Yalcin y Pinaki Mazumder. "Image Processing by a Programmable Artificial Retina Comprising Quantum Dots and Variable Resistance Devices". En Neuromorphic Circuits for Nanoscale Devices, 255–74. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338918-10.

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Actas de conferencias sobre el tema "Neuromorphic devices"

"Neuromorphic and Quantum Devices". En 2018 76th Device Research Conference (DRC). IEEE, 2018. http://dx.doi.org/10.1109/drc.2018.8443299.

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Santoro, Francesca. "Organic neuromorphic biointerfaces". En Bioelectronic Interfaces: Materials, Devices and Applications. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2024. http://dx.doi.org/10.29363/nanoge.cybioel.2024.047.

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Noheda, Beatriz. "Ferroelectrics for brain-inspired devices". En Neuromorphic Materials, Devices, Circuits and Systems. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2023. http://dx.doi.org/10.29363/nanoge.neumatdecas.2023.072.

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Neftci, Emre, Zhenming Yu y Nathan Lereoux. "Training-to-Learn with Memristive Devices". En Neuromorphic Materials, Devices, Circuits and Systems. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2023. http://dx.doi.org/10.29363/nanoge.neumatdecas.2023.013.

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Ham, Donhee. "Neuroelectronic interface and neuromorphic engineering". En Neuromorphic Materials, Devices, Circuits and Systems. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2023. http://dx.doi.org/10.29363/nanoge.neumatdecas.2023.058.

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Wu, Lingxi, Rahul Sreekumar, Rasool Sharifi, Kevin Skadron, Mircea R. Stant y Ashish Venkat. "Hardware Trojans in eNVM Neuromorphic Devices". En 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2023. http://dx.doi.org/10.23919/date56975.2023.10136984.

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Reita, C. "Memory devices in Neuromorphic Computing Systems". En 2017 International Conference on Solid State Devices and Materials. The Japan Society of Applied Physics, 2017. http://dx.doi.org/10.7567/ssdm.2017.m-2-01.

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Zhu, Ruomin, Sam Lilak, Alon Loeffler, Joseph Lizier, Adam Stieg, James Gimzewski y Zdenka Kuncic. "Reservoir Computing with Neuromorphic Nanowire Networks". En Neuromorphic Materials, Devices, Circuits and Systems. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2023. http://dx.doi.org/10.29363/nanoge.neumatdecas.2023.055.

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Farronato, Matteo, Piergiulio Mannocci, Saverio Ricci, Alessandro Bricalli, Margherita Melegari, Christian Monzio Compagnoni y Daniele Ielmini. "Memtransistor devices based on MoS2 for neuromorphic computing". En Neuromorphic Materials, Devices, Circuits and Systems. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2023. http://dx.doi.org/10.29363/nanoge.neumatdecas.2023.042.

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Venkatesan, T. "Robust Resistive and Mem-devices for Neuromorphic Circuits". En Neuromorphic Materials, Devices, Circuits and Systems. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2023. http://dx.doi.org/10.29363/nanoge.neumatdecas.2023.007.

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Informes sobre el tema "Neuromorphic devices"

Gall, W. E. Brain-Based Devices for Neuromorphic Computer Systems. Fort Belvoir, VA: Defense Technical Information Center, julio de 2013. http://dx.doi.org/10.21236/ada587348.

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