Teses / dissertações sobre o tema "Neuromorphic devices"
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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.
Texto completo da fonteHirtzlin, Tifenn. "Digital Implementation of Neuromorphic systems using Emerging Memory devices". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPAST071.
Texto completo da fonteWhile 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
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.
Texto completo da fonteAzam, Md Ali. "Energy Efficient Spintronic Device for Neuromorphic Computation". VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/6036.
Texto completo da fonteZaman, 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.
Texto completo da fonteMandal, 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.
Texto completo da fonteWenke, 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.
Texto completo da fontePedró, 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.
Texto completo da fonteThe 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.
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.
Texto completo da fonteHosseini, Peiman. "Phase-change and carbon based materials for advanced memory and computing devices". Thesis, University of Exeter, 2013. http://hdl.handle.net/10871/10122.
Texto completo da fonteCONTI, DANIELE. "Neuromorphic systems based on memristive devices - From the material science perspective to bio-inspired learning hardware". Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2711511.
Texto completo da fonteAlzate, Banguero Melissa. "Towards neuromorphic computing on quantum many-body architectures : VO2 transition dynamics". Electronic Thesis or Diss., Université Paris sciences et lettres, 2024. http://www.theses.fr/2024UPSLS021.
Texto completo da fonteAs AI demands grow, new computing paradigms are essential. Traditional von Neumann architectures struggle with intensive AI requirements. Neuromorphic computing, inspired by the brain, integrates processing and memory for faster, efficient computation, ideal for AI applications like deep learning and pattern recognition.Key materials for neuromorphic computing include synaptors and neuristors. Memristors, non-volatile memories made from oxides like HfO2 and TiO2, mimic synaptic behavior by switching states via nanoscale filaments or phase transitions. Neuristors emulate neuron spiking behavior using memristors and resistance-capacitance circuits to replicate the Leaky, Integrate, and Fire model. Mott insulators like VO2 mimic neuron-like behavior by forming volatile conductive pathways. However, synaptors and neuristors often require different materials. Optimizing VO2 for synaptic behavior could enable it to serve both functions at room temperature.Studying phase-separated systems like VO2 is complex due to inhomogeneities. Advances in infrared and optical microscopy now allow imaging these regions with nanometer-scale resolution. Near-field techniques, using atomic force microscopes coupled to IR lasers, can probe local conductivity at the nanoscale. However, these probes have limitations: (i) long scans for larger inhomogeneities and (ii) temperature-driven phase transitions causing temperature drifts and difficult imaging comparisons.To address these, we developed a far-field optical microscopy setup to study VO2 phase transitions. This setup leverages optical contrast between insulating and metallic phases, observable from nanometers to microns. We applied different temperature protocols while continuously imaging, counteracting temperature drift and aligning sharp images. This enables single-pixel time traces to indicate specific phase transition temperatures.We first mapped critical temperature (Tc), transition width (ΔTc), and transition sharpness (δTc) in VO2. These maps could enable tailoring VO2 properties for specific applications like memory devices and fast switching components.We also presented the first optical imaging of ramp reversal memory (RRM) in VO2, showing cluster evolution during thermal subloop training. Memory accumulation occurs at cluster boundaries and within patches, suggesting preferential diffusion of point defects. This could enhance memory effects through defect engineering, improving memory devices' robustness and stability.Additionally, we pursued a machine learning (ML) analysis of fractal patterns in VO2, using ML to classify the Hamiltonian driving pattern formation. Our convolutional neural network (CNN) achieved high accuracy with synthetic and experimental data, confirming pattern formation driven by proximity to a critical point of the two-dimensional random field Ising model. This framework, combined with symmetry reduction and confidence quantification, offers a new powerful tool for analyzing complex phase transitions in correlated materials.Our research provides a new optical characterization method for understanding VO2 transition dynamics and introduces innovative approaches for optimizing VO2 for non-memory applications. These insights lay a foundation for future studies that explore RRM's potential, and extend ML frameworks to other correlated materials
Calayir, Vehbi. "Neurocomputing and Associative Memories Based on Emerging Technologies: Co-optimization of Technology and Architecture". Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/422.
Texto completo da fonteMIRIGLIANO, MATTEO. "CHARACTERIZATION OF NANOSTRUCTURED METALLIC FILMS WITH NON-LINEAR ELECTRICAL PROPERTIES FOR THE FABRICATION OF NEUROMORPHIC DEVICES AND UNCONVENTIONAL DATA PROCESSING". Doctoral thesis, Università degli Studi di Milano, 2021. http://hdl.handle.net/2434/820647.
Texto completo da fonteABOU, KHALIL ALI. "Event Driven Tactile Sensors for Artificial Devices". Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1001986.
Texto completo da fonteSarim, Mohammad. "Memristive Device based Brain-Inspired Navigation and Localization for Robots". University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1522419391485511.
Texto completo da fonteArth, Kevin. "Neuromorphic sensory substitution with an asynchronous tactile belt for unsighted people : from design to clinical trials". Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS218.
Texto completo da fonteThis document presents the conception of the first neuromorphic tactile sensory substitution device, merging the domains of neuroprosthetics and sensory substitution.After a presentation of the state of art of the domains at the core of this work, we will introduce the device and present its chronological evolution and technical choices. We will then in a second stage introduce the validation studies that have been carried out to test the tactile neuromorphic device on blind and healthy control patients. The first study relies on psychophysical tests carried out to evaluate the link between spatial and temporal resolution of the developed device. The test relied on the ability of subjects to detect the direction of motion of a point sent on the tactile belt contacting the back of the subject. In the second study, the neuromorphic tactile system is coupled with an artificial silicon retina. A clinical trial is performed to study the performances of the developed device in a more complex environments using an incremental learning method. This study also evaluates the subjects’ feedback on the ergonomics of such an equipment. Ten visually impaired and five well-sighted subjects were selected. Subjects were able to detect objects in motion, discriminate the spacing between shapes, find a target in a scene with variable brightness, follow a signaled path on the ground and even avoid potential obstacles
Koke, Christoph [Verfasser], e Karlheinz [Akademischer Betreuer] Meier. "Device Variability in Synapses of Neuromorphic Circuits / Christoph Koke ; Betreuer: Karlheinz Meier". Heidelberg : Universitätsbibliothek Heidelberg, 2017. http://d-nb.info/1180985427/34.
Texto completo da fonteDelacour, Corentin. "Architecture Design for Analog Oscillatory Neural Networks". Electronic Thesis or Diss., Université de Montpellier (2022-....), 2023. http://www.theses.fr/2023UMONS069.
Texto completo da fonteDigitalization of society creates important quantities of data that have been increasing at an exponential rate during the past few years. Despite the tremendous technological progress, digital computers have trouble meeting the demand, especially for challenging tasks involving artificial intelligence or optimization problems. The fundamental reason comes from the architecture of digital computers which separates the processor and memory and slows down computations due to undesired data transfers, the so-called von Neumann bottleneck. To avoid unnecessary data movement, various computing paradigms have been proposed that merge processor and memory such as neuromorphic architectures that take inspiration from the brain and physically implement artificial neural networks. Furthermore, rethinking digital operations and using analog physical laws to compute has the potential to accelerate some tasks at a low energy cost.This dissertation aims to explore an energy-efficient physical computing approach based on analog oscillatory neural networks (ONN). In particular, this dissertation unveils (1) the performances of ONN based on vanadium dioxide oscillating neurons with resistive synapses, (2) a novel mixed-signal and scalable ONN architecture that computes in the analog domain and propagates the information digitally, and (3) how ONNs can tackle combinatorial optimization problems whose complexity scale exponentially with the problem size. The dissertation concludes with discussions of some promising future research directions
Bûrger, Jens. "Architectures and Algorithms for Intrinsic Computation with Memristive Devices". PDXScholar, 2016. https://pdxscholar.library.pdx.edu/open_access_etds/3104.
Texto completo da fonteYakopcic, Chris. "Memristor Device Modeling and Circuit Design for Read Out Integrated Circuits, Memory Architectures, and Neuromorphic Systems". University of Dayton / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1398725462.
Texto completo da fonteMARRONE, FRANCESCO. "Memristor-based hardware accelerators: from device modeling to AI applications". Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2972305.
Texto completo da fonteJanzakova, Kamila. "Développement de dendrites polymères organiques en 3D comme dispositif neuromorphique". Electronic Thesis or Diss., Université de Lille (2022-....), 2023. http://www.theses.fr/2023ULILN017.
Texto completo da fonteNeuromorphic technologies is a promising direction for development of more advanced and energy-efficient computing. They aim to replicate attractive brain features such as high computational efficiency at low power consumption on a software and hardware level. At the moment, brain-inspired software implementations (such as ANN and SNN) have already shown their successful application for different types of tasks (image and speech recognition). However, to benefit more from the brain-like algorithms, one may combine them with appropriate hardware that would also rely on brain-like architecture and processes and thus complement them. Neuromorphic engineering has already shown the utilization of solid-state electronics (CMOS circuits, memristor) for the development of brain-inspired devices. Nevertheless, these implementations are fabricated through top-down methods. In contrast, brain computing relies on bottom-up processes such as interconnectivity between cells and the formation of neural communication pathways.In the light of mentioned above, this work reports on the development of programmable 3D organic neuromorphic devices, which, unlike most current neuromorphic technologies, can be created in a bottom-up manner. This allows bringing neuromorphic technologies closer to the level of brain programming, where necessary neural paths are established only on the need.First, we found out that PEDOT:PSS based 3D interconnections can be formed by means of AC-bipolar electropolymerization and that they are capable of mimicking the growth of neural cells. By tuning individually the parameters of the waveform (peak amplitude voltage -VP, frequency - f, duty cycle - dc and offset voltage - Voff), a wide range of dendrite-like structures was observed with various branching degrees, volumes, surface areas, asymmetry of formation, and even growth dynamics.Next, it was discovered that dendritic morphologies obtained at various frequencies are conductive. Moreover, each structure exhibits an individual conductance value that can be interpreted as synaptic weight. More importantly, the ability of dendrites to function as OECT was revealed. Different dendrites exhibited different performances as OECT. Further, the ability of PEDOT:PSS dendrites to change their conductivity in response to gate voltage was used to mimic brain memory functions (short-term plasticity -STP and long-term plasticity -LTP). STP responses varied depending on the dendritic structure. Moreover, emulation of LTP was demonstrated not only by means of an Ag/AgCl gate wire but as well by means of a self-developed polymer dendritic gate.Finally, structural plasticity was demonstrated through dendritic growth, where the weight of the final connection is governed according to Hebbian learning rules (spike-timing-dependent plasticity - STDP and spike-rate-dependent plasticity - SRDP). Using both approaches, a variety of dendritic topologies with programmable conductance states (i.e., synaptic weight) and various dynamics of growth have been observed. Eventually, using the same dendritic structural plasticity, more complex brain features such as associative learning and classification tasks were emulated.Additionally, future perspectives of such technologies based on self-propagating polymer dendritic objects were discussed
Roclin, David. "Utilisation des nano-composants électroniques dans les architectures de traitement associées aux imageurs". Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112408/document.
Texto completo da fonteBy using learning mechanisms extracted from recent discoveries in neuroscience, spiking neural networks have demonstrated their ability to efficiently analyze the large amount of data from our environment. The implementation of such circuits on conventional processors does not allow the efficient exploitation of their parallelism. The use of digital memory to implement the synaptic weight does not allow the parallel reading or the parallel programming of the synapses and it is limited by the bandwidth of the connection between the memory and the processing unit. Emergent memristive memory technologies could allow implementing this parallelism directly in the heart of the memory.In this thesis, we consider the development of an embedded spiking neural network based on emerging memory devices. First, we analyze a spiking network to optimize its different components: the neuron, the synapse and the STDP learning mechanism for digital implementation. Then, we consider implementing the synaptic memory with emergent memristive devices. Finally, we present the development of a neuromorphic chip co-integrating CMOS neurons with CBRAM synapses
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.
Texto completo da fonte"Neuromorphic Controller for Low Power Systems From Devices to Circuits". Doctoral diss., 2011. http://hdl.handle.net/2286/R.I.14383.
Texto completo da fonteDissertation/Thesis
Ph.D. Electrical Engineering 2011
Kavehei, Omid. "Memristive devices and circuits for computing, memory, and neuromorphic applications". Thesis, 2012. http://hdl.handle.net/2440/73316.
Texto completo da fonteThesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2012.
"Design of Resistive Synaptic Devices and Array Architectures for Neuromorphic Computing". Doctoral diss., 2018. http://hdl.handle.net/2286/R.I.49052.
Texto completo da fonteDissertation/Thesis
Doctoral Dissertation Electrical Engineering 2018
(11191896), Chamika M. Liyanagedera. "Intelligent Sensing and Energy Efficient Neuromorphic Computing using Magneto-Resistive Devices". Thesis, 2021.
Encontre o texto completo da fonteWith the Moore’s Law era coming to an end, much attention has been given to novel nanoelectronic devices as a key driving force behind technological innovation. Utilizing the inherent device physics of nanoelectronic components, for sensory and computational tasks have proven to be useful in reducing the area and energy requirements of the underlying hardware fabrics. In this work we demonstrate how the intrinsic noise present in nano magnetic devices can pave the pathway for energy efficient neuromorphic hardware. Furthermore, we illustrate how the unique magnetic properties of such devices can be leveraged for accurate estimation of environmental magnetic fields. We focus on spintronic technologies in particular, due to the low current and energy requirements in contrast to traditional CMOS technologies.
Image segmentation is a crucial pre-processing stage used in many object identification tasks that involves simplifying the representation of an image so it can be conveniently analyzed in the later stages of a problem. This is achieved through partitioning a complicated image into specific groups based on color, intensity or texture of the pixels of that image. Locally Excitatory Globally Inhibitory Oscillator Network or LEGION is one such segmentation algorithm, where synchronization and desynchronization between coupled oscillators are used for segmenting an image. In this work we present an energy efficient and scalable hardware implementation of LEGION using stochastic Magnetic Tunnel Junctions that leverage the fast parallel
nature
of the algorithm. We demonstrate that the proposed hardware is capable of segmenting
binary and gray-scale images with multiple objects more efficiently than
existing
hardware implementations.
It is understood that the underlying device physics
of spin devices can be used for emulating the functionality of a spiking
neuron. Stochastic spiking neural networks based on nanoelectronic spin devices
can be a possible pathway of achieving brain-like compact and energy-efficient
cognitive intelligence. Current computational models attempt to exploit the
intrinsic device stochasticity of nanoelectronic synaptic or neural components
to perform learning and inference. However, there has been limited analysis on
the scaling effect of stochastic spin devices and its impact on the operation
of such stochastic networks at the system level. Our work attempts to explore
the design space and analyze the performance of nanomagnet based stochastic neuromorphic
computing architectures, for magnets with different barrier heights. We illustrate
how the underlying network architecture must be modified to account for the
random telegraphic switching behavior displayed by magnets as they are scaled into
the superparamagnetic regime.
Next we investigate how the magnetic properties
of spin devices can be utilized for real world sensory applications. Magnetic
Tunnel Junctions can efficiently translate variations in external magnetic
fields into variations in electrical resistance. We couple this property of
Magnetic Tunnel Junctions with Amperes law to design a non-invasive sensor to
measure the current flowing through a wire. We demonstrate how undesirable
effects of thermal noise and process variations can be suppressed through novel
analog and digital signal conditioning techniques to obtain reliable and
accurate current measurements. Our results substantiate that the proposed
noninvasive current sensor surpass other state-of-the-art technologies in terms
of noise and accuracy.
Tsung-HanLin e 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.
Texto completo da fonteChen, Yu-Jia, e 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.
Texto completo da fonte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.
(8811866), Mei-Chin Chen. "SPINTRONIC DEVICES AND ITS APPLICATIONS". Thesis, 2020.
Encontre o texto completo da fonteProcess 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.
(9751070), Vaibhav R. Ostwal. "SPINTRONIC DEVICES FROM CONVENTIONAL AND EMERGING 2D MATERIALS FOR PROBABILISTIC COMPUTING". Thesis, 2020.
Encontre o texto completo da fonteNovel computational paradigms based on non-von Neumann architectures are being extensively explored for modern data-intensive applications and big-data problems. One direction in this context is to harness the intrinsic physics of spintronics devices for the implementation of nanoscale and low-power building blocks of such emerging computational systems. For example, a Probabilistic Spin Logic (PSL) that consists of networks of p-bits has been proposed for neuromorphic computing, Bayesian networks, and for solving optimization problems. In my work, I will discuss two types of device-components required for PSL: (i) p-bits mimicking binary stochastic neurons (BSN) and (ii) compound synapses for implementing weighted interconnects between p-bits. Furthermore, I will also show how the integration of recently discovered van der Waals ferromagnets in spintronics devices can reduce the current densities required by orders of magnitude, paving the way for future low-power spintronics devices.
First, a spin-device with input-output isolation and stable magnets capable of generating tunable random numbers, similar to a BSN, was demonstrated. In this device, spin-orbit torque pulses are used to initialize a nano-magnet with perpendicular magnetic anisotropy (PMA) along its hard axis. After removal of each pulse, the nano-magnet can relax back to either of its two stable states, generating a stream of binary random numbers. By applying a small Oersted field using the input terminal of the device, the probability of obtaining 0 or 1 in binary random numbers (P) can be tuned electrically. Furthermore, our work shows that in the case when two stochastic devices are connected in series, “P” of the second device is a function of “P” of the first p-bit and the weight of the interconnection between them. Such control over correlated probabilities of stochastic devices using interconnecting weights is the working principle of PSL.
Next my work focused on compact and energy efficient implementations of p-bits and interconnecting weights using modified spin-devices. It was shown that unstable in-plane magnetic tunneling junctions (MTJs), i.e. MTJs with a low energy barrier, naturally fluctuate between two states (parallel and anti-parallel) without any external excitation, in this way generating binary random numbers. Furthermore, spin-orbit torque of tantalum is used to control the time spent by the in-plane MTJ in either of its two states i.e. “P” of the device. In this device, the READ and WRITE paths are separated since the MTJ state is read by passing a current through the MTJ (READ path) while “P” is controlled by passing a current through the tantalum bar (WRITE path). Hence, a BSN/p-bit is implemented without energy-consuming hard axis initialization of the magnet and Oersted fields. Next, probabilistic switching of stable magnets was utilized to implement a novel compound synapse, which can be used for weighted interconnects between p-bits. In this experiment, an ensemble of nano-magnets was subjected to spin-orbit torque pulses such that each nano-magnet has a finite probability of switching. Hence, when a series of pulses are applied, the total magnetization of the ensemble gradually increases with the number of pulses
applied similar to the potentiation and depression curves of synapses. Furthermore, it was shown that a modified pulse scheme can improve the linearity of the synaptic behavior, which is desired for neuromorphic computing. By implementing both neuronal and synaptic devices using simple nano-magnets, we have shown that PSL can be realized using a modified Magnetic Random Access Memory (MRAM) technology. Note that MRAM technology exists in many current foundries.
To further reduce the current densities required for spin-torque devices, we have fabricated heterostructures consisting of a 2-dimensional semiconducting ferromagnet (Cr2Ge2Te6) and a metal with spin-orbit coupling metal (tantalum). Because of properties such as clean interfaces, perfect crystalline nanomagnet structure and sustained magnetic moments down to the mono-layer limit and low current shunting, 2D ferromagnets require orders of magnitude lower current densities for spin-orbit torque switching than conventional metallic ferromagnets such as CoFeB.
Chang, Lung-Yu, e 張容瑜. "TiOx-based synaptic memory device for neuromorphic application". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/tq7dfd.
Texto completo da fonte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.
Ayazianmavi, Sahar. "Photovoltaic (PV) and fully-integrated implantable CMOS ICs". Thesis, 2012. http://hdl.handle.net/2152/ETD-UT-2012-05-5527.
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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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