Low-Power Forming Free TiO2–&lt;italic&gt;x&lt;/italic&gt;/HfO2–&lt;italic&gt;y&lt;/italic&gt;/TiO2–&lt;italic&gt;x&lt;/italic&gt;-Trilayer RRAM Devices Exhibiting Synaptic Property Characteristics

Bousoulas, Panagiotis; Michelakaki, Irini; Skotadis, Evangelos; Tsigkourakos, Menelaos; Tsoukalas, D.

doi:10.1109/ted.2017.2709338

Cited by 48 publications

(25 citation statements)

References 39 publications

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“…The situation is quite different in the case of SiO x :Ag/ TiO x -based memristor device, where the SiO x thin layer has a high-bandgap (~9 eV) and a lower dielectric constant (~3), but the TiO x layer has a low-bandgap (~3 eV) and a highdielectric constant (~40), which makes the electric field across SiO x layer higher than that of the TiO x layer, dissolving more Ag atoms in the switching layer [28]. It is the low ion mobility and low redox reaction rate of titanium oxide that controls the migration and accumulation of Ag atoms and Ag ion across the interfacial layer [36]. These two facts, as mentioned above, can cause the formation of nano-coneshaped filament from TE to BE [37].…”

Section: Resultsmentioning

confidence: 99%

Analog Switching and Artificial Synaptic Behavior of Ag/SiOx:Ag/TiOx/p++-Si Memristor Device

Ilyas

et al. 2020

Nanoscale Res Lett

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In this study, by inserting a buffer layer of TiO x between the SiO x :Ag layer and the bottom electrode, we have developed a memristor device with a simple structure of Ag/SiO x :Ag/TiO x /p ++ -Si by a physical vapor deposition process, in which the filament growth and rupture can be efficiently controlled during analog switching. The synaptic characteristics of the memristor device with a wide range of resistance change for weight modulation by implementing positive or negative pulse trains have been investigated extensively. Several learning and memory functions have been achieved simultaneously, including potentiation/depression, paired-pulse-facilitation (PPF), short-term plasticity (STP), and STP-to-LTP (long-term plasticity) transition controlled by repeating pulses more than a rehearsal operation, and spike-time-dependent-plasticity (STDP) as well. Based on the analysis of logarithmic I-V characteristics, it has been found that the controlled evolution/dissolution of conductive Ag-filaments across the dielectric layers can improve the performance of the testing memristor device.

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Section: Resultsmentioning

confidence: 99%

Analog Switching and Artificial Synaptic Behavior of Ag/SiOx:Ag/TiOx/p++-Si Memristor Device

Ilyas

et al. 2020

Nanoscale Res Lett

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“…Furthermore, a trilayer structure of TiO x /HfO x /TiO x has been developed to reduce the power of memristive synapses . Figure a presents the I – V curves of the TiO x single‐layer, HfO x /TiO x bilayer, and TiO x /HfO x /TiO x trilayer structures.…”

Section: Working Mechanisms Of Memristive Synapsesmentioning

confidence: 99%

Section: Working Mechanisms Of Memristive Synapsesmentioning

confidence: 99%

Memristive Synapses for Brain‐Inspired Computing

Wang

Zhuge

2019

Adv Materials Technologies

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be precisely regulated by the ionic flow, which is called synaptic plasticity. Generally, there are two types of synaptic plasticity: potentiation and depression. In the case of potentiation or depression, the synaptic weight increases or decreases upon repeated stimulation. Inspired by the human brain, novel non von Neumann computing architectures mainly composed of artificial synapses and artificial neurons have been proposed, which we can call "artificial neural networks," "brain-like computers," or "neuromorphic computers." [1,[3][4][5] However, the development of artificial brains that possess the efficiency of their biological counterparts in information processing remains a major challenge in computing. [6] One of the major challenges is the lack of suitable hardware to implement synapses, which are the main data processing elements in artificial brains. Generally, there are four essential requirements for an artificial synapse. [7,8] First, the analog weight should be nonvolatile in the absence of learning and weight updates should be bidirectional when the synapse is learning. Second, the synapse output is the product of the input signal and the synapse weight. Third, the weight updates vary with both the input signal and the stored weight value. Fourth, the synapse should be rather compact, and operate off a unidirectional information transfer with low power consumption.Mead and co-workers fabricated a four-terminal synaptic device based on a single floating gate silicon transistor in 1995. [8] It can simultaneously perform long term weight storage, compute the product of the input and floating gate value, and update the weight value according to a Hebbian or a backpropagation learning rule. In 1996, they developed a new floating-gate silicon transistor synapse with a threeterminal structure. [7] Hot-electron injection and electron tunneling make possible bidirectional weight updates. Given that these updates depend on both the stored weight value and the transistor terminal voltages, such synapse can implement a learning function. However, the integration density of transistor synapses is severely restricted to their three-or fourterminal structures. [6] The emergence of memristors endows artificial synapses with a new development opportunity. The memristor (short for memory resistor) is a two-terminal circuit element characterized by a relationship between the charge and the flux-linkage, which shows a distinctive "fingerprint" characterized by a pinched hysteresis loop confined to the first and the third quadrants of the Although the structure and function of the human brain are still far from being fully understood, brain-inspired computing architectures mainly consisting of artificial neurons and artificial synapses have been attracting more and more attentions due to their powerful computing capability and energy efficient operation. Synaptic plasticity is believed to be the origin of learning and memory. However, it is still a big challenge to realize artificial synapses with high reliability, go...

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“…The operation of the RRAM appeared in the figure ions using as resistors rather than electronic changes. The ions are communicated at the nanoscale [13]. The cells have ions at the two disintegrated cathodes [14].…”

Section: B States In Rrammentioning

confidence: 99%

Comprehensive Examination on Resistive Random Access Memory

Dharani*,

Bhavani,

Hridhya

2019

IJRTE

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With the latest advances in materials science, resistive random access memory (RRAM) devices are attracting non-volatile, low power consumption, non-destructive read, and high density memory. Related performance parameters for RRAM devices include operating voltage, operating speed, resistivity, durability, retention time, device yield, and multi-level storage. Numerous resistive mechanisms, such as conductive filaments, space charge limited conduction, trap charging and discharging, Schottky emission, and pool-Frenkel emission, have been proposed to explain the resistance switches of RRAM devices. Therefore, in this work, different oxide-based random access memories (RRAMs) were provided for comprehensive investigation of neuromorphiccalculations. With the development of RRAM, the physical mechanism of conduction, the basic history of neuromorphic calculations begins. Finally, suggestions for future research, as well as waiting for the challenges of RRAM equipment, are given.

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Low-Power Forming Free TiO_{2–<italic>x</italic>}/HfO_{2–<italic>y</italic>}/TiO_{2–<italic>x</italic>}-Trilayer RRAM Devices Exhibiting Synaptic Property Characteristics

Cited by 48 publications

References 39 publications

Analog Switching and Artificial Synaptic Behavior of Ag/SiOx:Ag/TiOx/p++-Si Memristor Device

Analog Switching and Artificial Synaptic Behavior of Ag/SiOx:Ag/TiOx/p++-Si Memristor Device

Memristive Synapses for Brain‐Inspired Computing

Comprehensive Examination on Resistive Random Access Memory

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