Tuning Resistive, Capacitive, and Synaptic Properties of Forming Free TiO<sub>2‐x</sub>‐Based RRAM Devices by Embedded Pt and Ta Nanocrystals

Bousoulas, Panagiotis; Karageorgiou, I.; Aslanidis, Evangelos; Giannakopoulos, K.; Tsoukalas, D.

doi:10.1002/pssa.201700440

Cited by 12 publications

(9 citation statements)

References 32 publications

(38 reference statements)

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“…Resistive random accesses memories (RRAMs) are considered as one of the most promising NVM . A number of research groups have reported synaptic simulation using RRAM devices. − It is found that RRAM possesses several advantages for the mimic of biological synapses, such as good data retention (10 years), excellent memory (100 On/Off ratio), excellent scalability (10 nm), and multilevel analogue memory characteristics (∼5 bit or 32 levels). However, analog switching with multilevel states and reliability still have issues for RRAM.…”

Section: Introductionmentioning

confidence: 99%

Toward a Reliable Synaptic Simulation Using Al-Doped HfO₂ RRAM

Roy

Niu

Wang

et al. 2020

ACS Appl. Mater. Interfaces

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The potential in a synaptic simulation for neuromorphic computation has revived the research interest of resistive random access memory (RRAM). However, novel applications require reliable multilevel resistive switching (RS), which still represents a challenge. We demonstrate in this work the achievement of reliable HfO 2 -based RRAM devices for synaptic simulation by performing the Al doping and the postdeposition annealing (PDA). Transmission electron microscopy and operando hard X-ray photoelectron spectroscopy results reveal the positive impact of Al doping on the formation of oxygen vacancies. Detailed I−V characterizations demonstrate that the 16.5% Al doping concentration leads to better RS properties of the device. In comparison with the other reported results based on HfO 2 RRAM, our devices with 16.5% Aldoping and PDA at 450 °C show better reliable multilevel RS (∼20 levels) performance and an increased on/off ratio. The 16.5% Al:HfO 2 sample with PDA at 450 °C shows good potentiation/depression characteristics with low pulse width (10 μs) along with a good On/Off ratio (>1000), good data retention at room temperature, and high temperature and good program/erase endurance characteristics with a pulse width of 50 ns. The synapse features including potentiation, depression, and spike time-dependent plasticity were successfully achieved using optimized Al-HfO 2 RRAM devices. Our results demonstrate the beneficial effects of Al doping and PDA on the enhancement of the performances of RRAM devices for the synaptic simulation in neuromorphic computing applications.

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Section: Introductionmentioning

confidence: 99%

Toward a Reliable Synaptic Simulation Using Al-Doped HfO₂ RRAM

Roy

Niu

Wang

et al. 2020

ACS Appl. Mater. Interfaces

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“…This assumption is also in line with the experimental data of the retention performance ( Figure 4 b), thus highlighting the acceptable behavior of our prototype. Additionally, in our previous work, we have dealt with similar issues, regarding the charge transfer properties of Pt and Ta NCs within the TiO 2 dielectric matrix [ 61 ].…”

Section: Discussionmentioning

confidence: 99%

“…Micromachines 2021, 12, x 14 of 18 with similar issues, regarding the charge transfer properties of Pt and Ta NCs within the TiO2 dielectric matrix [61].…”

Section: Discussionmentioning

confidence: 99%

Emulating Artificial Synaptic Plasticity Characteristics from SiO2-Based Conductive Bridge Memories with Pt Nanoparticles

Bousoulas

Papakonstantinopoulos

Kitsios

et al. 2021

Micromachines

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The quick growth of information technology has necessitated the need for developing novel electronic devices capable of performing novel neuromorphic computations with low power consumption and a high degree of accuracy. In order to achieve this goal, it is of vital importance to devise artificial neural networks with inherent capabilities of emulating various synaptic properties that play a key role in the learning procedures. Along these lines, we report here the direct impact of a dense layer of Pt nanoparticles that plays the role of the bottom electrode, on the manifestation of the bipolar switching effect within SiO2-based conductive bridge memories. Valuable insights regarding the influence of the thermal conductivity value of the bottom electrode on the conducting filament growth mechanism are provided through the application of a numerical model. The implementation of an intermediate switching transition slope during the SET transition permits the emulation of various artificial synaptic functionalities, such as short-term plasticity, including paired-pulsed facilitation and paired-pulse depression, long-term plasticity and four different types of spike-dependent plasticity. Our approach provides valuable insights toward the development of multifunctional synaptic elements that operate with low power consumption and exhibit biological-like behavior.

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“…40) Along these lines, TiO 2-x -based bilayer devices were fabricated by employing a room-temperature technique, 41,42) while the impact of the embedded Pt NPs was also explored. [43][44][45] A schematic illustration of the fabricated devices is presented in Fig. 1, while a thin film of pure Ti (4 nm) was used as an oxygen scavenging layer to enhance the stability of the switching effect.…”

Section: Analog Conductance Tuning With Bilayer and Trilayer-based Vc...mentioning

confidence: 99%

Material design strategies for emulating neuromorphic functionalities with resistive switching memories

Bousoulas

Kitsios

Chatzinikolaou

et al. 2022

Jpn. J. Appl. Phys.

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Nowadays, the huge power consumption and the inability of the conventional circuits to deal with real-time classification tasks have necessitated the devising of new electronic devices with inherent neuromorphic functionalities. Resistive switching memories arise as an ideal candidate due to their low footprint and small leakage current dissipation, while their intrinsic randomness is smoothly leveraged for implementing neuromorphic functionalities. In this review, valence change memories (VCM) or conductive bridge memories (CBRAM) for emulating neuromorphic characteristics are demonstrated. Moreover, the impact of the device structure and the incorporation of $Pt$ nanoparticles (NPs) is thoroughly investigated. Interestingly, our devices possess the ability to emulate various artificial synaptic functionalities, including paired-pulsed facilitation and paired-pulse depression, long-term plasticity and four different types of spike-dependent plasticity. Our approach provides valuable insights from a material design point of view towards the development of multifunctional synaptic elements that operate with low power consumption and exhibit biological-like behavior.

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Tuning Resistive, Capacitive, and Synaptic Properties of Forming Free TiO_2‐x‐Based RRAM Devices by Embedded Pt and Ta Nanocrystals

Cited by 12 publications

References 32 publications

Toward a Reliable Synaptic Simulation Using Al-Doped HfO₂ RRAM

Toward a Reliable Synaptic Simulation Using Al-Doped HfO₂ RRAM

Emulating Artificial Synaptic Plasticity Characteristics from SiO2-Based Conductive Bridge Memories with Pt Nanoparticles

Material design strategies for emulating neuromorphic functionalities with resistive switching memories

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Tuning Resistive, Capacitive, and Synaptic Properties of Forming Free TiO2‐x‐Based RRAM Devices by Embedded Pt and Ta Nanocrystals

Cited by 12 publications

References 32 publications

Toward a Reliable Synaptic Simulation Using Al-Doped HfO2 RRAM

Toward a Reliable Synaptic Simulation Using Al-Doped HfO2 RRAM

Emulating Artificial Synaptic Plasticity Characteristics from SiO2-Based Conductive Bridge Memories with Pt Nanoparticles

Material design strategies for emulating neuromorphic functionalities with resistive switching memories

Contact Info

Product

Resources

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Tuning Resistive, Capacitive, and Synaptic Properties of Forming Free TiO_2‐x‐Based RRAM Devices by Embedded Pt and Ta Nanocrystals

Toward a Reliable Synaptic Simulation Using Al-Doped HfO₂ RRAM

Toward a Reliable Synaptic Simulation Using Al-Doped HfO₂ RRAM