Дисертації з теми "Neuromorphic applications"
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Chen, Xing. "Modeling and simulations of skyrmionic neuromorphic applications." Thesis, université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST083.
Повний текст джерелаSpintronics nanodevices, which exploit both the magnetic and electrical properties of electrons, have emerged to bring various exciting characteristics promising for neuromorphic computing. Magnetic textures, such as domain walls and skyrmions, are particularly intriguing as neuromorphic components because they can support different functionalities due to their rich physical mechanisms. How the skyrmion dynamics can be utilized to build energy efficient neuromorphic hardware, and how deep learning can help achieve fast and accurate tests and validations of the proposals form the central topics of this thesis. The major contributions and innovations of this thesis can be summarized as follows: 1. Numerical and theoretical studies on skyrmion dynamics in confined nanostructures. We explore the skyrmion dynamics in terms of size, velocity, energy, and stability in a width-varying nanotrack. We found nanoscale skyrmion with small sizes could be obtained by employing this asymmetric structure. We also obtain a tradeoff between the nanotrack width (storage density) and the skyrmion motion velocity (data access speed). We study the skyrmion dynamics under voltage excitation through the voltage-controlled magnetic anisotropy effect in a circular thin film. We find that the breathing skyrmion can be analogized as a modulator. These findings could help us design efficient neuromorphic devices. 2. Skyrmion based device applications for neuromorphic computing. We present a compact Leaky-Integrate-Fire spiking neuron device by exploiting the current-driven skyrmion dynamics in a wedge-shaped nanotrack. We propose a True random number generators based on continuous skyrmion thermal Brownian motion in a confined geometry at room temperature. Our design are promising in emerging low power neuromorphic computing system, such as spiking neural network and stochastic/ probabilistic computing neuron network.3. A data-driven approach for modeling dynamical physical systems based on the Neural Ordinary Differential Equations (ODEs). We show that the adapted formalisms of Neural ODEs, designed for spintronics, can accurately predict the behavior of a non-ideal nanodevice, including noise, after training on a minimal set of micromagnetic simulations or experimental data, with new inputs and material parameters not belonging to the training data. With this modeling strategy, we can perform more complicated computational tasks, such as Mackey-Glass time-series predictions and spoken digit recognition, using the trained models of spintronic systems, with high accuracy and fast speed compared to conventional micromagnetic simulations
Shi, Yuanyuan. "Two dimensional materials based electronic synapses for neuromorphic applications." Doctoral thesis, Universitat de Barcelona, 2018. http://hdl.handle.net/10803/663415.
Повний текст джерелаEl cerebro humano puede realizar de forma sencilla infinidad de operaciones que los ordenadores no pueden hacer, pueden aprender naturalmente adaptando su estructura física, y consumen mucho menos energía. La razón es que el cerebro humano usa una sofisticada y muy densa red neuronal que procesa y almacena la información en paralelo. Este masivo paralelismo es la genuina característica que los ordenadores no pueden igualar, ya que éstos procesan y almacenan la información en unidades distintas, creando un embudo que limita sus prestaciones. Por lo tanto, emular el funcionamiento del cerebro utilizando componentes electrónicos es extremadamente importante, y se ha convertido en la obsesión de las mayores empresas. Las primeras redes neuronales artificiales para el desarrollo de inteligencia artificial están basadas en transistores, ya que éstos han sido la base de todos los dispositivos electrónicos modernos. Sin embargo, estudios recientes indican que los memristores podrían ser más idóneos para emular la interacción entre neuronas. En concreto, dos neuronas interactúan entre ellas a través de sinapsis, es decir, finas membranas que cambian su resistividad dependiendo de los impulsos eléctricos emitidos por las dos neuronas. La estructura y principio de funcionamiento de una sinapsis es muy similar al de un memristor, el cual presenta la ventaja de tener una estructura más simple y un coste de fabricación más bajo que un transistor. En esta tesis doctoral hemos desarrollado memristores avanzados utilizando materiales bidimensionales, como el grafeno y, especialmente, el nitruto de boto hexagonal con estructura multicapa. Nuestros experimentos y simulaciones indican que los dispositivos metal/h-BN/metal pueden ser utilizados como sinapsis electrónicas, ya que muestran comportamientos sinápticos en un único dispositivo. En nuestros dispositivos hemos observado short term plasticity, long term plasticity, spike timing dependent plasticity, y synapse relaxation. El régimen de funcionamiento puede ser controlado modificando la amplitud, duración e intervalo entre los pulsos aplicados. Además, las sinapsis electrónicas hechas mediante estructuras metal/h-BN/metal muestran un proceso de relajación muy repetitivo y con una baja variabilidad nunca observada anteriormente. Además, el consumo de potencia es muy bajo tanto en reposo (0.1 fW) como en modo volátil (600 pW).
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.
Повний текст джерелаLai, 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.
Повний текст джерелаMandal, 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.
Повний текст джерелаPedró, 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.
Повний текст джерелаThe 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.
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.
Повний текст джерелаCommittee Chair: Paul Hasler; Committee Member: Christopher Rozell; Committee Member: David Anderson. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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.
Повний текст джерелаMARRONE, FRANCESCO. "Memristor-based hardware accelerators: from device modeling to AI applications." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2972305.
Повний текст джерелаSECCO, JACOPO. "Memristor Platforms for Pattern Recognition Memristor Theory, Systems and Applications." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2680573.
Повний текст джерелаBai, Kang Jun. "Moving Toward Intelligence: A Hybrid Neural Computing Architecture for Machine Intelligence Applications." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103711.
Повний текст джерелаDoctor of Philosophy
Deep learning strategies are the cutting-edge of artificial intelligence, in which the artificial neural networks are trained to extract key features or finding similarities from raw sensory information. This is made possible through multiple processing layers with a colossal amount of neurons, in a similar way to humans. Deep learning strategies run on von Neumann computers are deployed worldwide. However, in today's data-driven society, the use of general-purpose computing systems and cloud infrastructures can no longer offer a timely response while themselves exposing other significant security issues. Arose with the introduction of neuromorphic architecture, application-specific integrated circuit chips have paved the way for machine intelligence applications in recently years. The major contributions in this dissertation include designing and fabricating a new class of hybrid neural computing architecture and implementing various deep learning strategies to diverse machine intelligence applications. The resulting hybrid neural computing architecture offers an alternative solution to accelerate the neural computations required for sophisticated machine intelligence applications with a simple system-level design, and therefore, opening the door to low-power system-on-chip design for future intelligence computing, what is more, providing prominent design solutions and performance improvements for internet of things applications.
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.
Повний текст джерелаMarcireau, Alexandre. "Vision par ordinateur évènementielle couleur : cadriciel, prototype et applications." Thesis, Sorbonne université, 2019. http://www.theses.fr/2019SORUS248.
Повний текст джерелаNeuromorphic engineering is a bio-inspired approach to sensors and computers design. It aims to mimic biological systems down to the transistor level, to match their unparalleled robustness and power efficiency. In this context, event-based vision sensors have been developed. Unlike conventional cameras, they feature independent pixels which asynchronously generate an output upon detecting changes in their field of view, with high temporal precision. These properties are not leveraged by conventional computer vision algorithms, thus a new paradigm has been devised. It advocates short calculations performed on each event to mimic the brain, and shows promise both for computer vision and as a model of biological vision. This thesis explores event-based computer vision to improve our understanding of visual perception and identify potential applications. We approach the issue through color, a mostly unexplored aspect of event-based sensors. We introduce a framework supporting color events, as well as two experimental devices leveraging it: a three-chip event-based camera performing absolute color measurements, and a visual psychophysics setup to study the role of precise-timing in the brain. We explore the possibility to apply the color sensor to the genetic engineering Brainbow method, and present a new mathematical model for the latter
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.
Повний текст джерелаThe exponential growth of data in recent years has led to a significant increase in energy consumption, creating a pressing need for innovative memory technologies to overcome the limitations of conventional solutions. This data deluge has resulted in a forecasted consumption surge in data centers, with an expected fourfold increase in data by 2025 compared to the present volume. To address this challenge, emerging memory technologies such as RRAM (Resistive RAM), PCM (Phase-Change Memory), and MRAM (Magnetoresistive RAM) are being developed to offer high density, fast access times, and non-volatility, thereby revolutionizing storage and memory solutions (Molas & Nowak, 2021).One promising technique to address the need for innovative memory technologies is the use of frequency modulation to modulate resistance in PCM which is a crucial aspect of its use in neuromorphic computing. PCM is a non-volatile memory technology based on the reversible phase transition between amorphous and crystalline phases of certain materials. The ability to alter conductance levels makes PCM well-suited for synaptic realizations in neuromorphic computing. The progressive crystallization of the phase-change material and the subsequent increase in device conductance enable PCM to be used in neuromorphic applications. Additionally, PCM-based memristor neural networks have been developed, and the resistance drift effect in PCM has been quantified, opening up new paths for the development of PCM-based memristor neuromorphic accelerators. Furthermore, frequency modulation has been identified as a promising technique to modulate resistance in PCM. This approach can be applied to PCM as well as RRAM, and it is expected to yield improved learning effects in more complex networks using multi-level cells (Wang et al., 2011). The primary aim of this thesis is to explore innovative methods for controlling resistance levels in PCM devices with a focus on their application in neuromorphic systems. The research involves a comprehensive understanding of the mechanisms underlying PCM devices and an identification of parameters that may influence the reliability of these devices. Additionally, the thesis aims to propose a novel approach to effectively modulate resistance levels in PCM devices, contributing to advancements in this field
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.
Повний текст джерелаWenke, 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.
Повний текст джерелаMarquez, 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.
Повний текст джерелаArtificial neural networks are systems prominently used in computation and investigations of biological neural systems. They provide state-of-the-art performance in challenging problems like the prediction of chaotic signals. Yet, the understanding of how neural networks actually solve problems like prediction remains vague; the black-box analogy is often employed. Merging nonlinear dynamical systems theory with machine learning, we develop a new concept which describes neural networks and prediction within the same framework. Taking profit of the obtained insight, we a-priori design a hybrid computer, which extends a neural network by an external memory. Furthermore, we identify mechanisms based on spatio-temporal synchronization with which random recurrent neural networks operated beyond their fixed point could reduce the negative impact of regular spontaneous dynamics on their computational performance. Finally, we build a recurrent delay network in an electro-optical setup inspired by the Ikeda system, which at first is investigated in a nonlinear dynamics framework. We then implement a neuromorphic processor dedicated to a prediction task
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.
Повний текст джерелаSuri, 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.
Повний текст джерелаMohta, Neha. "Two-dimensional materials based artificial synapses for neuromorphic applications." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/6054.
Повний текст джерелаCHEN, 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.
Повний текст джерела國立交通大學
電子研究所
107
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 resistance change, the better the learning accuracy we can achieve in the machine learning task. In this thesis, we mainly focus on ZrOx-based RRAM, trying to improve the nonlinearity by device design engineering. Firstly, by changing the bottom electrode from Pt to TiN, we successfully demonstrate ZrOx-based RRAM with gradual resistivity change. In addition, we propose a mechanism to explain the difference. Secondly, by introducing additional post-deposition annealing, the nonlinearity of the weight update is further improved from >9 to 4.45 for potentiation; >9 to 5.29 for depression. This can be explained by interface oxygen vacancies due to the formation of the TiON layer after annealing. In the third part, based on the previous report, a methodology to improve the nonlinearity, we used the AlOx as a barrier layer, because AlOx has low ion mobility due to the ALD process. By stacking AlOx under ZrOx, we obtained the bilayer structure RRAM. Compared to the single layer (ZrOx) device, the nonlinearity was further improved to 3.94 and 2.42 for potentiation and depression, respectively, and the methodology was confirmed. Additionally, with process parameter optimized, we have fabricated a synaptic RRAM with high linearity weight update, which nonlinearity is 1.3 for potentiation, and 1.82 for depression. In the future, this can be further applied to the neuromorphic computing system to serve as the electronic synapse.
Kavehei, Omid. "Memristive devices and circuits for computing, memory, and neuromorphic applications." Thesis, 2012. http://hdl.handle.net/2440/73316.
Повний текст джерелаThesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2012.
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.
Повний текст джерелаChen, 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.
Повний текст джерела國立交通大學
電子研究所
108
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 reveals an inverse power-law dependence on cumulative read number in the second stage. We discover that the measured power factor is dependent on read voltages. On the other hand, the measured transition read number between the two stages is affected by both read voltages and initial conductance levels. To describe the conductance evolution, we present an analytical model to simulate the transition read number at different read voltages and initial conductance levels. The proposed model is in great consistency with measurement results. Our model is not only capable of describing the conductance evolution in a wide range of read cycle numbers but also provides physical insights to read-induced conductance changes in RRAM synapses.
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.
Повний текст джерела國立中山大學
材料與光電科學學系研究所
102
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 analysis, the lithium silicate RRAM shows abnormal resistive switching behaviors, especially the high resistance states distribute in a wide range. Based on the corroboration of conduction current fitting analysis, a model was proposed to explain the electrical resistive switching behaviors. By controlling the stop-voltage, the device can achieve multi-bit function and perform complementary resistive switches (CRS). Generally, CRS consists of two anti-serial RRAMs to solve the sneak path problem. However, the lithium silicate RRAM can archive CRS in a single device due to the dual-ion effect (Li+ and O2-). The lithium silicate RRAM device is demonstrated advanced synaptic function such as synaptic plasticity, a spike-timing-dependent plasticity (STDP), a short-term memory (STM) and long-term memory function (LTM), which is relying on the synaptic plasticity with a continuous transition between intermediate resistance states. Further, after a constant voltage applying, the irreversible switching from LRS to HRS is recovered, and the device reveals good endurance again.
(8811866), Mei-Chin Chen. "SPINTRONIC DEVICES AND ITS APPLICATIONS." Thesis, 2020.
Знайти повний текст джерелаProcess 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.
We propose a new Spin-Orbit Torque based Domino-style Spin Logic (SOT-DSL) that operates in a sequence of Preset and Evaluation modes of operations. During the preset mode, the output magnet is clocked to its hard-axis using spin Hall effect. In the evaluation mode, the clocked output magnet is switched by a spin current from the preceding stage. The nano-magnets in SOT-DSL are always driven by orthogonal spins rather than collinear spins, which in turn eliminates the incubation delay and allows fast magnetization switching. Based on our simulation results, SOT-DSL shows up to 50% improvement in energy consumption compared to All-Spin Logic. Moreover, SOT-DSL relaxes the requirement for buffer insertion between long spin channels, and significantly lowers the design complexity. This dissertation also covers two applications using MS as information carriers. MS has been shown to possess several advantages in terms of unprecedented stability, ultra-low depinning current density, and compact size.
We propose a multi-bit MS cell with appropriate peripheral circuits. A systematic device-circuit-architecture co-design is performed to evaluate the feasibility of using MS-based memory as last-level caches for general purpose processors. To further establish the viability of skyrmions for other applications, a deep spiking neural network (SNN) architecture where computation units are realized by MS-based devices is also proposed. We develop device architectures and models suitable for neurons and synapses, provide device-to-system level analysis for the design of an All-Spin Spiking Neural Network based on skyrmionic devices, and demonstrate its efficiency over a corresponding CMOS implementation.
Apart from the aforementioned applications such as memory storage elements or logic operation, this research also focuses on the implementation of spin-based device to solve combinatorial optimization problems. Finding an efficient computing method to solve these problems has been researched extensively. The computational cost for such optimization problems exponentially increases with the number of variables using traditional von-Neumann architecture. Ising model, on the other hand, has been proposed as a more suitable computation paradigm for its simple architecture and inherent ability to efficiently solve combinatorial optimization problems. In this work, SHE-MTJs are used as a stochastic switching bit to solve these problems based on the Ising model. We also design an unique approach to map bi-prime factorization problem to our proposed device-circuit configuration. By solving coupled Landau- Lifshitz-Gilbert equations, we demonstrate that our coupling network can factorize up to 16-bit binary numbers.
Paul, Tathagata. "Physics and application of charge transfer in van der Waals heterostructures." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/4503.
Повний текст джерелаLuís, 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.
Повний текст джерела(7887626), Wonil Chung. "Integration of Ferroelectricity into Advanced 3D Germanium MOSFETs for Memory and Logic Applications." Thesis, 2019.
Знайти повний текст джерелаChang, Lung-Yu, and 張容瑜. "TiOx-based synaptic memory device for neuromorphic application." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/tq7dfd.
Повний текст джерела國立交通大學
電子研究所
107
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. In this thesis, the bipolar resistive switching behavior and synaptic characteristics are investigated in TiOx-based synaptic memory device. There are three parts in this thesis. First, different thickness TiOx film are deposited in TiN/Ti/TiOx/TiN structure. The relationship between thickness and electrical characteristics is discussed. The thickness of the TiOx switching layer determine the working operation current of the devices. The thicker layer device can work at lower compliance current and make smaller conductive filament. In addition, the influence of different pulse amplitudes applied on potentiation and depression is investigated. When lower pulse amplitude was applied on the device, conductance can gradually change and the nonlinearity is better. However, dynamic range become small and noise increase. The second part is that different Ti thickness effect on TiOx-based synaptic device. We compare their electrical characteristics and synaptic characteristics. We observed that the analog behavior can be improved after inserting a thin Ti layer. Different thickness of Ti layer make different thickness of interfacial layer, which leads the TiOx- based memory device has different capability to form and rupture the filament. As a result, they perform different electrical characteristics and weight update behavior. The other part is that comparing ZrOx/TiOx synaptic device and TiOx synaptic device. The ZrOx/TiOx synaptic device shows more stable analog switching and the nonlinearity of potentiation and depression can be improved to 2.08 and 1.84. Furthermore, it exhibits good endurance and data retention properties.It demonstrates good performance not only for data storage application but also for mimicking biological synapse.
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.
Повний текст джерела國立交通大學
電子研究所
107
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 between TiOx and HfOx for improving the device’s performance. Based on TEM and EDX analyses, we find that Al doped into HfOx layer to form HfAlOx compound film. Based on such the oxygen vacancy mobility of HfAlOx layer formation, would lead to narrow the second filament. Through experiments, 1nm thick AlOx layer employed in the TiN/TiO/HfAlOx/TiN device exhibits the best property. Such device obtains excellent properties such as faster speed device (both set and reset pulse width is 1us) with good nonlinearity (3.39 for potentiation and 2.87 for depression behavior) and best nonlinearity (2.15 for potentiation and 1.52 for depression behavior with 10us pulse width) with 500 conductance states and retention with more than 104 s.
Baek, Eunhye. "Multi-functional Hybrid Gating Silicon Nanowire Field-effect Transistors: From Optoelectronics to Neuromorphic Application." 2018. https://tud.qucosa.de/id/qucosa%3A72327.
Повний текст джерелаRahimi, 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/.
Повний текст джерелаDutta, 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.
Повний текст джерела