Programmable current mode Hebbian learning neural network using programmable metallization cell

Swaroop, Balla Teja; West, William C.; Martı́nez, G.; Kozicki, Michael; Akers, L.A.

doi:10.1109/iscas.1998.703888

Cited by 16 publications

(16 citation statements)

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“…Using transistors more efficiently could in turn give us another decade, at least, of Moore's Law performance improvement, without requiring the costly and increasingly difficult doublings of transistor density on chips. In the end, memristors might even become the cornerstone of new analog circuits that compute using architecture much like that of the brain [4].…”

Section: Importance Of a Memristormentioning

confidence: 99%

“…507-519 (1971). In this paper, Prof. Chua proved a number of theorems to show that there was a 'missing' two-terminal circuit element from the family of "fundamental" passive devices : resistor, capacitor and inductor, for e.g., elements that do not add energy to a circuit [4].…”

Section: Origin Of Memristormentioning

confidence: 99%

“…The opposing flux induced in a resistor must also increase indefinitely so their sum remains finite. Any appropriate response to applied voltage may be called "magnetic flux" [4].…”

Section: Modelling Of Memristorsmentioning

confidence: 99%

“…They didn't know what those devices would be, but their goal was electronics that would keep improving even after the devices got so small that defective ones would become common. They then started to speculate about what this mystery nano-device would be [4].…”

Section: Why Titanium Dioxide and Not Any Other Materials For Memristormentioning

confidence: 99%

“…By the end of the month, the pieces had fallen into place. It was finally clear how the device worked and how they had a memristor [4].…”

Section: Why Titanium Dioxide and Not Any Other Materials For Memristormentioning

confidence: 99%

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Recent ATC’s in the design of Memristors

Haridas¹,

Patil²,

Manjunath³

2010

IJCEE

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Abstract-This paper presents a brief review about the recent advanced technology concepts in the field of the 4th element, called as the memristor, which has the properties of R, L & C. Traditionally, there have been three different basic components to circuits, namely capacitors, resistors, and inducers. However, a new basic element to a circuit has been developed. This element is known as the memristor. A memristor is essentially a resistor with memory. The resistance of a memristor is dependent upon the amount of voltage which has been applied to it and the length of time this voltage was applied. The concept of a memristor was first developed in 1971. However, it was not until recently that researchers at HP Labs were able to build the first working one. This is due mostly to the size dependence of the memristor. The changes in resistance of a memristor are not detectable until the memristor is in the nanometer range. When the thickness of the memristor is larger than this, it is indistinguishable from a standard resistor. The main advantage of the memristor in modern computing is that it retains its resistance even when no voltage is applied. This would allow a computer to retain its saved state while off and would require less power to run when it is on. Another advantage to memristors is their analog nature. Whereas the switches in computers have only two states, on and off, the resistance in a memristor does not have a set amount of values it can be. While the memristor can still be used for digital memory storage, there is research currently going on into the development of analog computers using memristors. The readers can use this paper as the ready reckoner for doing their research in the field of memristors, the data being obtained from various sources from the internet & from the papers of various researchers.

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Section: Importance Of a Memristormentioning

confidence: 99%

Section: Origin Of Memristormentioning

confidence: 99%

“…The opposing flux induced in a resistor must also increase indefinitely so their sum remains finite. Any appropriate response to applied voltage may be called "magnetic flux" [4].…”

Section: Modelling Of Memristorsmentioning

confidence: 99%

Section: Why Titanium Dioxide and Not Any Other Materials For Memristormentioning

confidence: 99%

“…By the end of the month, the pieces had fallen into place. It was finally clear how the device worked and how they had a memristor [4].…”

Section: Why Titanium Dioxide and Not Any Other Materials For Memristormentioning

confidence: 99%

See 3 more Smart Citations

Recent ATC’s in the design of Memristors

Haridas¹,

Patil²,

Manjunath³

2010

IJCEE

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Humidity‐Enabled Organic Artificial Synaptic Devices with Ultrahigh Moisture Resistivity

Qian

et al. 2022

Adv Elect Materials

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Organic memristors with homogeneous resistive switching behaviors can emulate the functionalities of biological synapses, making them promising candidates for use in brain‐inspired neuromorphic computing. Among various environmental parameters, humidity affecting device operational stability is a critical issue that organic memristors face in practical applications. A high humidity resistant organic memristor is demonstrated here using a small molecule material F16CuPc. Unencapsulated devices show excellent moisture stability, maintaining reliable memristive switching behavior after 1 year of exposure to air at 60% high relative humidity and remaining operational when immersed in water for 96 h. A proton conduction mechanism is discovered responsible for the resistive switching effect. The continuously adjustable conductance in F16CuPc‐based memristive devices enables the mimicking of biological synapse functions. These results open up a path to a humidity tolerant neuromorphic computing system based on high humidity resistant artificial synapse devices.

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Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

Zhu

2019

Physica Rapid Research Ltrs

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The von Neumann bottleneck constrains developments of conventional artificial intelligences (AIs) toward portable, energy efficient, and truly brain‐like systems. Biological synapses connecting neurons deliver neural action potentials by regulating neurotransmitters between presynaptic and postsynaptic membranes. In a similar way, ionotronic transistors and memristors transmit electronic signals through the migration of ionic species in dielectric and resistive switching electrolyte under external electrical field. Thus, utilizing ionotronic devices to emulate synapses and building bionic neural networks opens up a brand‐new path to realize hardware‐based AI. Moreover, it would allow the fabrication of an artificial perception learning system to mimic the human perception system with multi‐sensory learning abilities. This review presents ionotronic devices for neuromorphic engineering applications. The operation mechanisms and brain‐inspired synaptic responses and neural functions are discussed. At last, outlooks for ionotronic devices to construct bionic neural networks are provided.

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Programmable current mode Hebbian learning neural network using programmable metallization cell

Cited by 16 publications

References 1 publication

Recent ATC’s in the design of Memristors

Recent ATC’s in the design of Memristors

Humidity‐Enabled Organic Artificial Synaptic Devices with Ultrahigh Moisture Resistivity

Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

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