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Advanced Memristor Modeling: Memristor Circuits and Networks

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ISBN: 9783038971047 9783038971030 Year: Pages: 172 DOI: doi.org/10.3390/books978-3-03897-103-0 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General)
Added to DOAB on : 2019-05-22 16:48:48
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The investigation of new memory schemes, neural networks, computer systems, and many other improved electronic devices is very important for the future generation’s electronic circuits and for their widespread application in all the areas of industry. In this respect, the analysis of new efficient and advanced electronic elements and circuits is an essential field of highly developed electrical and electronic engineering. The resistance-switching phenomenon, observed in many amorphous oxides, has been investigated since 1970 and is a promising technology for constructing new electronic memories. It has been established that such oxide materials have the ability for changing their conductance in accordance with the applied voltage, and for memorizing their state for long-time interval. Similar behaviour has been predicted for the memristor element by Leon Chua in 1971. The memristor is proposed in accordance with symmetry considerations and the relationships between the four basic electric quantities—electric current i, voltage v, charge q, and magnetic flux Ψ. The memristor is an essential passive one-port element together with the resistor, inductor, and capacitor. The Williams HP research group has made a link between resistive switching devices and the memristor proposed by Chua. A number of scientific papers related to memristors and memristor devices have been issued, and several memristor models have been proposed. The memristor is a highly nonlinear component. It relates the electric charge q and the flux linkage, expressed as a time integral of the voltage. The memristor element has the important capability for remembering the electric charge passed through its cross-section and its respective resistance, when the electrical signals are switched off. Due to its nano-scale dimensions, non-volatility, and memorizing properties, the memristor is a sound potential candidate for application in computer high-density memories, artificial neural networks, and many other electronic devices.

Enabling Technologies for Very Large-Scale Synaptic Electronics

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889455089 Year: Pages: 105 DOI: 10.3389/978-2-88945-508-9 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Neurology
Added to DOAB on : 2019-01-23 14:53:42
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An important part of the colossal effort associated with the understanding of the brain involves using electronics hardware technology in order to reproduce biological behavior in ‘silico’. The idea revolves around leveraging decades of experience in the electronics industry as well as new biological findings that are employed towards reproducing key behaviors of fundamental elements of the brain (notably neurons and synapses) at far greater speed-scale products than any software-only implementation can achieve for the given level of modelling detail. So far, the field of neuromorphic engineering has proven itself as a major source of innovation towards the ‘silicon brain’ goal, with the methods employed by its community largely focused on circuit design (analogue, digital and mixed signal) and standard, commercial, Complementary Metal-Oxide Silicon (CMOS) technology as the preferred `tools of choice’ when trying to simulate or emulate biological behavior. However, alongside the circuit-oriented sector of the community there exists another community developing new electronic technologies with the express aim of creating advanced devices, beyond the capabilities of CMOS, that can intrinsically simulate neuron- or synapse-like behavior. A notable example concerns nanoelectronic devices responding to well-defined input signals by suitably changing their internal state (‘weight’), thereby exhibiting `synapse-like’ plasticity. This is in stark contrast to circuit-oriented approaches where the `synaptic weight’ variable has to be first stored, typically as charge on a capacitor or digitally, and then appropriately changed via complicated circuitry. The shift of very much complexity from circuitry to devices could potentially be a major enabling factor for very-large scale `synaptic electronics’, particularly if the new devices can be operated at much lower power budgets than their corresponding 'traditional' circuit replacements. To bring this promise to fruition, synergy between the well-established practices of the circuit-oriented approach and the vastness of possibilities opened by the advent of novel nanoelectronic devices with rich internal dynamics is absolutely essential and will create the opportunity for radical innovation in both fields. The result of such synergy can be of potentially staggering impact to the progress of our efforts to both simulate the brain and ultimately understand it. In this Research Topic, we wish to provide an overview of what constitutes state-of-the-art in terms of enabling technologies for very large scale synaptic electronics, with particular stress on innovative nanoelectronic devices and circuit/system design techniques that can facilitate the development of very large scale brain-inspired electronic systems

Memristors for Neuromorphic Circuits and Artificial Intelligence Applications

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ISBN: 9783039285761 / 9783039285778 Year: Pages: 244 DOI: 10.3390/books978-3-03928-577-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2020-06-09 16:38:57
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Artificial Intelligence (AI) has found many applications in the past decade due to the ever increasing computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses. Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. The so-called Spiking Neural Networks behave similarly to how the brain functions and are very energy efficient. Up to this moment, both spiking and conventional neural networks have been implemented in software programs running on conventional computing units. However, this approach requires high computing power, a large physical space and is energy inefficient. Thus, there is an increasing interest in developing AI tools directly implemented in hardware. The first hardware demonstrations have been based on CMOS circuits for neurons and specific communication protocols for synapses. However, to further increase training speed and energy efficiency while decreasing system size, the combination of CMOS neurons with memristor synapses is being explored. The memristor is a resistor with memory which behaves similarly to biological synapses. This book explores the state-of-the-art of neuromorphic circuits implementing neural networks with memristors for AI applications.

Keywords

memristor --- artificial synapse --- neuromorphic computing --- memristor-CMOS hybrid circuit --- temporal pooling --- sensory and hippocampal responses --- cortical neurons --- hierarchical temporal memory --- neocortex --- memristor-CMOS hybrid circuit --- defect-tolerant spatial pooling --- boost-factor adjustment --- memristor crossbar --- neuromorphic hardware --- memristor --- compact model --- emulator --- neuromorphic --- synapse --- STDP --- pavlov --- neuromorphic systems --- spiking neural networks --- memristors --- spike-timing-dependent plasticity --- RRAM --- vertical RRAM --- neuromorphics --- neural network hardware --- reinforcement learning --- AI --- neuromorphic computing --- multiscale modeling --- memristor --- optimization --- RRAM --- simulation --- memristors --- neuromorphic engineering --- OxRAM --- self-organization maps --- synaptic device --- memristor --- neuromorphic computing --- artificial intelligence --- hardware-based deep learning ICs --- circuit design --- memristor --- RRAM --- variability --- time series modeling --- autocovariance --- graphene oxide --- laser --- memristor --- crossbar array --- neuromorphic computing --- wire resistance --- synaptic weight --- character recognition --- neuromorphic computing --- Flash memories --- memristive devices --- resistive switching --- synaptic plasticity --- artificial neural network --- spiking neural network --- pattern recognition --- strongly correlated oxides --- resistive switching --- neuromorphic computing --- transistor-like devices --- artificial intelligence --- neural networks --- resistive switching --- memristive devices --- deep learning networks --- spiking neural networks --- electronic synapses --- crossbar array --- pattern recognition

Emerging Memory and Computing Devices in the Era of Intelligent Machines

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ISBN: 9783039285020 / 9783039285037 Year: Pages: 276 DOI: 10.3390/books978-3-03928-503-7 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2020-06-09 16:38:57
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Computing systems are undergoing a transformation from logic-centric towards memory-centric architectures, where overall performance and energy efficiency at the system level are determined by the density, performance, functionality and efficiency of the memory, rather than the logic sub-system.

Keywords

3D-stacked --- DRAM --- in-DRAM cache --- low-latency --- low-power --- resistive memory --- crossbar --- in-memory computing --- analogue computing --- matrix-vector multiplication --- ECG --- voltage-controlled magnetic anisotropy --- magnetoresistive random access memory --- magnetic tunnel junction --- bioelectronic devices --- bionanohybrid material --- biomemory --- biologic gate --- bioprocessor --- protein --- nucleic acid --- nanoparticles --- SONOS --- flash memory --- charge spreading --- plasma treatment --- Oxygen-related trap --- data retention --- BCH --- decoder --- iBM --- GPU --- hybrid --- flash memory --- Galois field --- CUDA --- in-memory computing --- logic-in-memory --- non-von Neumann architecture --- configurable logic-in-memory architecture --- memory wall --- convolutional neural networks --- emerging technologies --- perpendicular Nano Magnetic Logic (pNML) --- silicon oxide-based memristors --- resistance switching mechanism --- variability --- conductive filament --- Weibull distribution --- quantum point contact --- real-time system --- dynamic voltage scaling --- task placement --- low-power technique --- nonvolatile memory --- neuromorphic system --- Hebbian training --- guide training --- memristor --- image classification --- STT-MRAM --- flip-flop --- power gating --- low-power --- bipolar resistive switching characteristics --- annealing temperatures --- solution-based dielectric --- resistive random access memory (RRAM) --- multi-level cell --- phase change memory --- programmable ramp-down current pulses --- Fast Fourier Transform --- in-memory computing --- associative processor --- non-von neumann architecture --- in-memory computing --- memristor --- RISC-V --- Internet of things --- blockchain --- U-shape recessed channel --- floating gate --- neuromorphic computing --- MCU (microprogrammed control unit) --- chalcogenide --- electrochemical metallization cell --- electrochemical metallization (ECM) --- ion conduction --- memristor --- self-directed channel (SDC) --- memristor --- crossbar array --- wire resistance --- synaptic weight --- character recognition --- n/a

Nanoelectronic Materials, Devices and Modeling

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ISBN: 9783039212255 9783039212262 Year: Pages: 242 DOI: 10.3390/books978-3-03921-226-2 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-08-28 11:21:27
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As CMOS scaling is approaching the fundamental physical limits, a wide range of new nanoelectronic materials and devices have been proposed and explored to extend and/or replace the current electronic devices and circuits so as to maintain progress with respect to speed and integration density. The major limitations, including low carrier mobility, degraded subthreshold slope, and heat dissipation, have become more challenging to address as the size of silicon-based metal oxide semiconductor field effect transistors (MOSFETs) has decreased to nanometers, while device integration density has increased. This book aims to present technical approaches that address the need for new nanoelectronic materials and devices. The focus is on new concepts and knowledge in nanoscience and nanotechnology for applications in logic, memory, sensors, photonics, and renewable energy. This research on nanoelectronic materials and devices will be instructive in finding solutions to address the challenges of current electronics in switching speed, power consumption, and heat dissipation and will be of great interest to academic society and the industry.

Keywords

UAV --- vision localization --- hierarchical --- landing --- information integration --- memristor --- synaptic device --- spike-timing-dependent plasticity --- neuromorphic computation --- memristive device --- ZnO films --- conditioned reflex --- quantum dot --- sample grating --- cross-gain modulation --- bistability --- distributed Bragg --- semiconductor optical amplifier --- topological insulator --- field-effect transistor --- nanostructure synthesis --- optoelectronic devices --- topological magnetoelectric effect --- drain-induced barrier lowering (DIBL) --- gate-induced drain leakage (GIDL) --- silicon on insulator (SOI) --- graphene --- supercapacitor --- energy storage --- ionic liquid --- UV irradiation --- luminescent centres --- bismuth ions --- two-photon process --- oscillatory neural networks --- pattern recognition --- higher order synchronization --- thermal coupling --- vanadium dioxide --- band-to-band tunneling --- L-shaped tunnel field-effect-transistor --- double-gate tunnel field-effect-transistor --- corner-effect --- AlGaN/GaN --- high-electron mobility transistor (HEMTs) --- p-GaN --- enhancement-mode --- 2DEG density --- InAlN/GaN heterostructure --- polarization effect --- quantum mechanical --- gallium nitride --- MISHEMT --- dielectric layer --- interface traps --- current collapse --- PECVD --- gate-induced drain leakage (GIDL) --- drain-induced barrier lowering (DIBL) --- recessed channel array transistor (RCAT) --- on-current (Ion) --- off-current (Ioff) --- subthreshold slope (SS) --- threshold voltage (VTH) --- saddle FinFET (S-FinFET) --- potential drop width (PDW) --- shallow trench isolation (STI) --- source/drain (S/D) --- conductivity --- 2D material --- Green’s function --- reflection transmision method --- variational form --- dual-switching transistor --- third harmonic tuning --- low voltage --- high efficiency --- CMOS power amplifier IC --- insulator–metal transition (IMT) --- charge injection --- Mott transition --- conductive atomic force microscopy (cAFM) --- gate field effect --- atomic layer deposition (ALD) --- zinc oxide --- silicon --- ZnO/Si --- electron affinity --- bandgap tuning --- conduction band offset --- heterojunction --- solar cells --- PC1D --- vertical field-effect transistor (VFET) --- back current blocking layer (BCBL) --- gallium nitride (GaN) --- normally off power devices --- n/a

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