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Dissertations / Theses on the topic 'Neuromorphic applications'

Author: Grafiati

Published: 6 September 2023

Last updated: 26 July 2025

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1

Chen, Xing. "Modeling and simulations of skyrmionic neuromorphic applications." Thesis, université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST083.

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Les nanodispositifs spintroniques, qui exploitent à la fois les propriétés magnétiques et électriques des électrons, apportent diverses caractéristiques intéressantes et prometteuses pour le calcul neuromorphique. Les textures magnétiques, telles que les parois de domaine et les skyrmions, sont particulièrement intrigantes en tant que composants neuromorphiques, car elles peuvent prendre en charge différentes fonctionnalités grâce à la richesse de leurs mécanismes physiques. La façon dont la dynamique des skyrmions peut être utilisée pour construire du matériel neuromorphique économe en énergi

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2

Shi, Yuanyuan. "Two dimensional materials based electronic synapses for neuromorphic applications." Doctoral thesis, Universitat de Barcelona, 2018. http://hdl.handle.net/10803/663415.

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Electronic machines and computers have experienced a huge development during the last four decades, mainly thanks to the continuous scaling down of the hardware responsible of information processing and storage (i.e. transistors). However, as the size of these devices approaches inter-atomic distances, the fabrication costs increase exponentially. In order to solve this problem, the industry has started to consider new system architectures and hardware for processing and storing information. Inspired by nature, scientists and engineers have focused their attention on the human brain, which is

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3

Uppala, Roshni. "Simulating Large Scale Memristor Based Crossbar for Neuromorphic Applications." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429296073.

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4

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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5

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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6

Jouni, Zalfa. "Analog spike-based neuromorphic computing for low-power smart IoT applications." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST114.

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Avec l'expansion de l'Internet des objets (IoT) et l'augmentation des appareils connectés et des communications complexes, la demande de technologies de localisation précises et économes en énergie s'est intensifiée. Les techniques traditionnelles de machine learning et d'intelligence artificielle (IA) offrent une haute précision dans la localisation par radiofréquence (RF), mais au prix d'une complexité accrue et d'une consommation d'énergie élevée. Pour relever ces défis, cette thèse explore le potentiel de l'informatique neuromorphique, inspirée par les mécanismes du cerveau, pour permettre

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7

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 cr

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8

Petre, Csaba. "Sim2spice a tool for compiling simulink designs on FPAA and applications to neuromorphic circuits /." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31820.

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Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.<br>Committee Chair: Paul Hasler; Committee Member: Christopher Rozell; Committee Member: David Anderson. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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9

Herrmann, Eric. "A Novel Gate Controlled Metal Oxide Resistive Memory Cell and its Applications." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1540565326482153.

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10

MARRONE, FRANCESCO. "Memristor-based hardware accelerators: from device modeling to AI applications." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2972305.

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11

SECCO, JACOPO. "Memristor Platforms for Pattern Recognition Memristor Theory, Systems and Applications." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2680573.

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In the last decade a large scientific community has focused on the study of the memristor. The memristor is thought to be by many the best alternative to CMOS technology, which is gradually showing its flaws. Transistor technology has developed fast both under a research and an industrial point of view, reducing the size of its elements to the nano-scale. It has been possible to generate more and more complex machinery and to communicate with that same machinery thanks to the development of programming languages based on combinations of boolean operands. Alas as shown by Moore’s law, th

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12

Bai, Kang Jun. "Moving Toward Intelligence: A Hybrid Neural Computing Architecture for Machine Intelligence Applications." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103711.

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Rapid advances in machine learning have made information analysis more efficient than ever before. However, to extract valuable information from trillion bytes of data for learning and decision-making, general-purpose computing systems or cloud infrastructures are often deployed to train a large-scale neural network, resulting in a colossal amount of resources in use while themselves exposing other significant security issues. Among potential approaches, the neuromorphic architecture, which is not only amenable to low-cost implementation, but can also deployed with in-memory computing strategy

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13

Dai, Yang [Verfasser], Roger [Gutachter] Wördenweber, Markus [Gutachter] Grüninger, and Jutta [Gutachter] Schwarzkopf. "Tailoring the Electronic Properties of Epitaxial Oxide Films via Strain for SAW and Neuromorphic Applications / Yang Dai ; Gutachter: Roger Wördenweber, Markus Grüninger, Jutta Schwarzkopf." Köln : Universitäts- und Stadtbibliothek Köln, 2017. http://d-nb.info/1149794100/34.

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14

Marcireau, Alexandre. "Vision par ordinateur évènementielle couleur : cadriciel, prototype et applications." Thesis, Sorbonne université, 2019. http://www.theses.fr/2019SORUS248.

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L'ingénierie neuromorphique aborde de manière bio-inspirée le design des capteurs et ordinateurs. Elle prône l'imitation du vivant à l'échelle du transistor, afin de rivaliser avec la robustesse et la faible consommation des systèmes biologiques. Les caméras évènementielles ont vu le jour dans ce cadre. Elles possèdent des pixels indépendants qui détectent de manière asynchrone les changements dans leur champ visuel, avec une grande précision temporelle. Ces propriétés étant mal exploitées par les algorithmes usuels de vision par ordinateur, un nouveau paradigme encourageant de petits calculs

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15

Trabelsi, Ahmed. "Modulation des niveaux de résistance dans une mémoire PCM pour des applications neuromorphiques." Electronic Thesis or Diss., Université Grenoble Alpes, 2024. http://www.theses.fr/2024GRALT027.

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La croissance exponentielle des données au cours des dernières années a entraîné une augmentation significative de la consommation d'énergie, créant ainsi un besoin urgent de technologies de mémoire innovantes pour surmonter les limitations des solutions conventionnelles. Cette inondation de données a entraîné une augmentation prévue de la consommation dans les centres de données, avec une multiplication par quatre des données d'ici 2025 par rapport au volume actuel. Pour relever ce défi, des technologies de mémoire émergentes telles que la RRAM (RAM résistive), la PCM (mémoire à changement de

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16

Bichler, Olivier. "Contribution à la conception d'architecture de calcul auto-adaptative intégrant des nanocomposants neuromorphiques et applications potentielles." Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00781811.

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Dans cette thèse, nous étudions les applications potentielles des nano-dispositifs mémoires émergents dans les architectures de calcul. Nous montrons que des architectures neuro-inspirées pourraient apporter l'efficacité et l'adaptabilité nécessaires à des applications de traitement et de classification complexes pour la perception visuelle et sonore. Cela, à un cout moindre en termes de consommation énergétique et de surface silicium que les architectures de type Von Neumann, grâce à une utilisation synaptique de ces nano-dispositifs. Ces travaux se focalisent sur les dispositifs dit "memrist

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17

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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18

Marquez, Alfonzo Bicky. "Reservoir computing photonique et méthodes non-linéaires de représentation de signaux complexes : Application à la prédiction de séries temporelles." Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCD042/document.

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Les réseaux de neurones artificiels constituent des systèmes alternatifs pour effectuer des calculs complexes, ainsi que pour contribuer à l'étude des systèmes neuronaux biologiques. Ils sont capables de résoudre des problèmes complexes, tel que la prédiction de signaux chaotiques, avec des performances à l'état de l'art. Cependant, la compréhension du fonctionnement des réseaux de neurones dans la résolution de problèmes comme la prédiction reste vague ; l'analogie avec une boîte-noire est souvent employée. En combinant la théorie des systèmes dynamiques non linéaires avec celle de l'apprenti

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19

Baek, Eunhye [Verfasser], Gianaurelio [Gutachter] Cuniberti, and Ronald [Gutachter] Tetzlaff. "Multi-functional Hybrid Gating Silicon Nanowire Field-effect Transistors : From Optoelectronics to Neuromorphic Application / Eunhye Baek ; Gutachter: Gianaurelio Cuniberti, Ronald Tetzlaff." Dresden : Technische Universität Dresden, 2020. http://d-nb.info/1227202164/34.

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20

Suri, Manan. "Technologies émergentes de mémoire résistive pour les systèmes et application neuromorphique." Phd thesis, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00935190.

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La recherche dans le domaine de l'informatique neuro-inspirée suscite beaucoup d'intérêt depuis quelques années. Avec des applications potentielles dans des domaines tels que le traitement de données à grande échelle, la robotique ou encore les systèmes autonomes intelligents pour ne citer qu'eux, des paradigmes de calcul bio-inspirés sont étudies pour la prochaine génération solutions informatiques (post-Moore, non-Von Neumann) ultra-basse consommation. Dans ce travail, nous discutons les rôles que les différentes technologies de mémoire résistive non-volatiles émergentes (RRAM), notamment (i

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21

Mohta, Neha. "Two-dimensional materials based artificial synapses for neuromorphic applications." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/6054.

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The need and demand for continuous high-speed, energy-efficient hardware advancement is undisputed. Traditional computing system with von Neumann architecture leads to high energy consumption and latency due to a huge amount of data transfer between the separated memory unit and the logic unit. In response to this discrepancy, extensive research has been conducted to develop brain-inspired electronic devices that can provide alternate computing platforms needed for implementing hardware neural networks. Artificial synapse, which emulates the dynamics of biological synapses, such as “update” an

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22

CHEN, KUAN-CHIEH, and 陳冠傑. "Zirconium oxide-based resistive switching memory for neuromorphic computing applications." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/q847ky.

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碩士<br>國立交通大學<br>電子研究所<br>107<br>Resistive random access memory (RRAM) is the most promising nonvolatile memory in the future, due to its serval advantages, low power consumption, high operation speed, and 3D compatible architecture……etc. Another potential application of RRAM is to implement it to neuromorphic computing. To use RRAM as an electronic synapse, it should perform the capability of gradual resistance change. Furthermore, some electrical properties and metrics need to be considered, e.g., linearity, symmetry, dynamic range, etc. Many papers conclude that the higher linearity of resis

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23

Kavehei, Omid. "Memristive devices and circuits for computing, memory, and neuromorphic applications." Thesis, 2012. http://hdl.handle.net/2440/73316.

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A memristor, memory resistor, is a two-terminal nanodevice that can be made as thin as a single-atom-thick that has become of tremendous interest for its potential to revolutionise electronics, computing, computer architectures, and neuromorphic engineering. This thesis encompasses two major parts containing original contributions, (Part I) modelling and fabrication, and (Part II) circuit application and computing. Each part contains three chapters. The fundamentals necessary for understanding the main idea of each chapter are provided therein. A background chapter revolving around memristors

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24

Tsung-HanLin and 林宗翰. "Compact Modeling of Variability in RRAM Devices and its Impact on Neuromorphic Circuit Applications." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/5f8q44.

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25

Chen, Yu-Jia, and 陳昱嘉. "Modeling of Read Operation Induced Conductance Change in Resistive Switching Devices for Neuromorphic Applications." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/f2z2yg.

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碩士<br>國立交通大學<br>電子研究所<br>108<br>In this thesis, we characterize the negative voltage read induced conductance change in a hafnium oxide RRAM synapse. An analytical model is developed to describe the conductance evolution with the number of read cycles. The proposed model includes the impact of read voltage and initial conductance level on conductance state stability. We found that the current degradation induced by read pulses is determined by cumulative read time, rather than the duration of a single read pulse. A two-stage featured conductance evolution is observed. The conductance reduction

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26

Wang, Zhi-yang, and 王志揚. "Study on Applications of LiSiOx Thin-Film Resistance Random Access Memory as Synapse in Neuromorphic Systems." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/s562z4.

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碩士<br>國立中山大學<br>材料與光電科學學系研究所<br>102<br>The information stored in human brain is different from computer, it storages and transmits messages through analog signal instead of digital signal. In this study, the lithium silicate resistive random access memory (RRAM) mimics synaptic-like biological behavior with multi-bit function. It is helpful for the development of artificial neural network and analog storage by emulating learning rules in the brain. The lithium silicate thin film was prepared by RF sputtering, and it was fabricated the RRAM with Pt/LiSiOx/TiN structure. Though the electrical an

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27

(8811866), Mei-Chin Chen. "SPINTRONIC DEVICES AND ITS APPLICATIONS." Thesis, 2020.

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<div> <div> <div> <p>Process variations and increasing leakage current are major challenges toward memory realization in deeply-scaled CMOS devices. Spintronic devices recently emerged as one of the leading candidates for future information storage due to its potential for non-volatility, high speed, low power and good endurance. In this thesis, we start with the basic concepts and applications of three spintronic devices, namely spin or- bit torque (SOT) based spin-valves, SOT-based magnetic tunnel junctions and the magnetic skyrmion (MS) for both logic and machine learning hardware. </p> <p>

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28

Paul, Tathagata. "Physics and application of charge transfer in van der Waals heterostructures." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/4503.

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Since the discovery of graphene, the field of 2D materials has garnered massive interest from a materials science, basic physics and device application point of view. This results from the diverse range of electronic and transport properties observed in these systems. For example, graphene, which has a gapless Dirac fermionic band structure with extremely high carrier mobility, shows low photoresponse due to the absence of a band gap. However, layered transition metal dichalcogenides (TMDCs) such as MoS2, which possess a semiconducting band structure with disorder dominated hopping like t

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29

Luís, Joana Cristina Marques. "Nanoscale Memristor: Great potential for memory and synapse emulator for computing applications." Master's thesis, 2019. http://hdl.handle.net/10362/90978.

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This work reports the fabrication and electrical characterization of Metal-Insulator-Metal (MIM) devices for neuromorphic applications using zinc-tin-oxide (ZTO) and indium-gallium-zinc-oxide (IGZO) as the switching layers and molybdenum (Mo) for the devices ‘contacts. A lithographic mask was used along with physical vapor deposition (PVD) processes for the production of the different samples’ layers. Using ZTO as a switching layer in order to replace other elements that are becoming scarce such as indium or gallium is of relevant importance, therefore it was first attempted a ZTO based MIM de

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(7887626), Wonil Chung. "Integration of Ferroelectricity into Advanced 3D Germanium MOSFETs for Memory and Logic Applications." Thesis, 2019.

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<div>Germanium-based MOS device which is considered as one of the promising alternative channel materials has been studied with well-known FinFET, nanowire structures and HKMG (High-k metal gate). Recent introduction of Ferroelectric (FE) Zr-doped HfO<sub>2</sub> (Hf<sub>x</sub>Zr<sub>1-x</sub>O<sub>2</sub>, HZO) has opened various possibilities both in memory and logic</div><div>applications.</div><div><br></div><div>First, integration of FE HZO into the conventional Ge platform was studied to demonstrate Ge FeFET. The FE oxide was deposited with optimized atomic layer deposition (ALD) recipe

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31

Chang, Lung-Yu, and 張容瑜. "TiOx-based synaptic memory device for neuromorphic application." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/tq7dfd.

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碩士<br>國立交通大學<br>電子研究所<br>107<br>Neuromorphic computing is expected to emulate brain functions in the near future. There are several nonvolatile memory such as PCRAM, CBRAM, RRAM have been proposed as synaptic memory device. All of above, RRAM is the most promising candidate, due to its several advantages, low power consumption, simple structure, excellent endurance, high operation speed. However, the desirable characteristic of synaptic device is different from traditional RRAM. It requires analog switching behavior and multi-level conductance states, which are beneficial to learning accuracy.

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32

Chu, Chun-An, and 朱俊安. "HfOx-based Resistive Random Access Memory for neuromorphic computing application." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/ccvue3.

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碩士<br>國立交通大學<br>電子研究所<br>107<br>Due to that the device toward more powerful, Artificial Intelligence (AI) computing become more and more popular. Neuromorphic computing is one of the most popular model of AI computing. By simulating the weight update of synapses between the neuro cells when the human brain accepts outside signal, we can use a new way to update the hardware condition instead of software. It is expected that the AI computing become much faster and much lower power consumption. According to oxygen vacancy rich layer model and low oxygen vacancy mobility model, we add AlOx layer b

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33

Baek, Eunhye. "Multi-functional Hybrid Gating Silicon Nanowire Field-effect Transistors: From Optoelectronics to Neuromorphic Application." 2018. https://tud.qucosa.de/id/qucosa%3A72327.

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Enormous demands for fast and low-power computing and memory building blocks for consumer electronics, such as smartphones or tablets, have led to the emergence of silicon nanowire transistors a decade ago. Along with the Si-based nanotechnology, the silicon compatible optical and chemical sensing applications have boosted the research on hybrid devices that combine the organic and inorganic materials. Apart from the revolution in the device dimensions, the rapid growth of artificial intelligence in the software industry brunch requires the next generation’s computers with the revolutionized h

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34

Rahimi, Azghadi S. Mostafa. "Neuromorphic VLSI designs for spike timing and rate-based synaptic plasticity with application in pattern classification." Thesis, 2014. http://hdl.handle.net/2440/84732/.

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This thesis presents a versatile study on the design and Very Large Scale Integration(VLSI) implementation of various synaptic plasticity rules ranging from phenomenological rules, to biophysically realistic ones. In particular, the thesis aims at developing novel spike timing-based learning circuits that advance the current neuromorphic systems, in terms of power consumption, compactness and synaptic modification (learning) abilities. Furthermore, the thesis investigates the usefulness of the developed designs and algorithms in specific engineering tasks such as pattern classification. To fol

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35

Dutta, Mrinmoy, and Mrinmoy Dutta. "Improvement of resistive switching memory using Cu filament based interfacial engineering in high-k/MoS2 electrolyte and its neuromorphic/bio-sensing application." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107CGU05428021%22.&searchmode=basic.

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