[go: up one dir, main page]

Jeong et al., 2016 - Google Patents

Memristors for energy‐efficient new computing paradigms

Jeong et al., 2016

View PDF
Document ID
9944160829644815436
Author
Jeong D
Kim K
Kim S
Choi B
Hwang C
Publication year
Publication venue
Advanced Electronic Materials

External Links

Snippet

In this Review, memristors are examined from the frameworks of both von Neumann and neuromorphic computing architectures. For the former, a new logic computational process based on the material implication is discussed. It consists of several memristors which play …
Continue reading at advanced.onlinelibrary.wiley.com (PDF) (other versions)

Classifications

    • H—ELECTRICITY
    • H01—BASIC ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES; ELECTRIC SOLID STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H01L45/00—Solid state devices adapted for rectifying, amplifying, oscillating or switching without a potential-jump barrier or surface barrier, e.g. dielectric triodes; Ovshinsky-effect devices; Processes or apparatus peculiar to the manufacture or treatment thereof or of parts thereof
    • H01L45/04—Bistable or multistable switching devices, e.g. for resistance switching non-volatile memory
    • H01L45/12—Details
    • H01L45/122—Device geometry
    • H—ELECTRICITY
    • H01—BASIC ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES; ELECTRIC SOLID STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H01L45/00—Solid state devices adapted for rectifying, amplifying, oscillating or switching without a potential-jump barrier or surface barrier, e.g. dielectric triodes; Ovshinsky-effect devices; Processes or apparatus peculiar to the manufacture or treatment thereof or of parts thereof
    • H01L45/04—Bistable or multistable switching devices, e.g. for resistance switching non-volatile memory
    • H01L45/14—Selection of switching materials
    • H01L45/145—Oxides or nitrides
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • H—ELECTRICITY
    • H01—BASIC ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES; ELECTRIC SOLID STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H01L45/00—Solid state devices adapted for rectifying, amplifying, oscillating or switching without a potential-jump barrier or surface barrier, e.g. dielectric triodes; Ovshinsky-effect devices; Processes or apparatus peculiar to the manufacture or treatment thereof or of parts thereof
    • H01L45/04—Bistable or multistable switching devices, e.g. for resistance switching non-volatile memory
    • H01L45/16—Manufacturing
    • G—PHYSICS
    • G11—INFORMATION STORAGE
    • G11C—STATIC STORES
    • G11C13/00—Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00 - G11C25/00
    • G11C13/0002—Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00 - G11C25/00 using resistance random access memory [RRAM] elements
    • G11C13/0009—RRAM elements whose operation depends upon chemical change
    • G—PHYSICS
    • G11—INFORMATION STORAGE
    • G11C—STATIC STORES
    • G11C13/00—Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00 - G11C25/00
    • G11C13/0002—Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00 - G11C25/00 using resistance random access memory [RRAM] elements
    • G11C13/0021—Auxiliary circuits
    • H—ELECTRICITY
    • H01—BASIC ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES; ELECTRIC SOLID STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H01L27/00—Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate
    • H01L27/02—Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including semiconductor components specially adapted for rectifying, oscillating, amplifying or switching and having at least one potential-jump barrier or surface barrier; including integrated passive circuit elements with at least one potential-jump barrier or surface barrier
    • G—PHYSICS
    • G11—INFORMATION STORAGE
    • G11C—STATIC STORES
    • G11C2213/00—Indexing scheme relating to G11C13/00 for features not covered by this group

Similar Documents

Publication Publication Date Title
Jeong et al. Memristors for energy‐efficient new computing paradigms
Chen et al. Essential characteristics of memristors for neuromorphic computing
Li et al. Hardware implementation of neuromorphic computing using large‐scale memristor crossbar arrays
Kim et al. Competing memristors for brain-inspired computing
Choi et al. Emerging memristive artificial synapses and neurons for energy‐efficient neuromorphic computing
Ielmini et al. Brain-inspired computing via memory device physics
Wang et al. Resistive switching materials for information processing
Lanza et al. Memristive technologies for data storage, computation, encryption, and radio-frequency communication
Kuncic et al. Neuromorphic nanowire networks: principles, progress and future prospects for neuro-inspired information processing
Jeong et al. Nonvolatile memory materials for neuromorphic intelligent machines
Ielmini et al. Emerging neuromorphic devices
Song et al. Recent advances and future prospects for memristive materials, devices, and systems
Li et al. Review of memristor devices in neuromorphic computing: materials sciences and device challenges
Grollier et al. Neuromorphic spintronics
Islam et al. Device and materials requirements for neuromorphic computing
Upadhyay et al. Emerging memory devices for neuromorphic computing
Yu et al. An electronic synapse device based on metal oxide resistive switching memory for neuromorphic computation
Wu et al. Resistive random access memory for future information processing system
Rajendran et al. Neuromorphic computing based on emerging memory technologies
Indiveri et al. Integration of nanoscale memristor synapses in neuromorphic computing architectures
Liu et al. Optimization of non-linear conductance modulation based on metal oxide memristors
Woo et al. Optimized programming scheme enabling linear potentiation in filamentary HfO 2 RRAM synapse for neuromorphic systems
Hwang Prospective of semiconductor memory devices: from memory system to materials
Ma et al. Circuit‐level memory technologies and applications based on 2D materials
Moon et al. Improved conductance linearity and conductance ratio of 1T2R synapse device for neuromorphic systems