Emulation of Biological Synapse Characteristics from Cu/AlN/TiN Conductive Bridge Random Access Memory

Cho, Hyojong; Kim, Sungjun

doi:10.3390/nano10091709

Cited by 32 publications

(24 citation statements)

References 46 publications

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“…The extracted relaxation times suggest the successful emulation of the LTP effect since a long-lasting memory performance is achieved and is enhanced as we increase the number of the pre-synaptic signals [ 42 ]. From a physical point of view, we can argue that the formation of relatively big and robust CF that cannot self-rupture, in terms of effective diameter distribution, is the driving force for this effect [ 43 ].…”

Section: Resultsmentioning

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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Section: Resultsmentioning

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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“…OxRRAM is based on the oxygen vacancy or ion migration, and CBRAM is based on the drift of metal ions within the switching layer by electrochemical reaction. Notably, compared with the OxRRAM, CBRAM with active metal electrodes (like Cu) behaves more advantageously for integrated circuits, such as reducing electron migration and RC delay [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Oxygen Concentration Effect on Conductive Bridge Random Access Memory of InWZnO Thin Film

et al. 2021

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In this study, the influence of oxygen concentration in InWZnO (IWZO), which was used as the switching layer of conductive bridge random access memory, (CBRAM) is investigated. With different oxygen flow during the sputtering process, the IWZO film can be fabricated with different oxygen concentrations and different oxygen vacancy distribution. In addition, the electrical characteristics of CBRAM device with different oxygen concentration are compared and further analyzed with an atomic force microscope and X-ray photoelectron spectrum. Furthermore, a stacking structure with different bilayer switching is also systematically discussed. Compared with an interchange stacking layer and other single layer memory, the CBRAM with specific stacking sequence of bilayer oxygen-poor/-rich IWZO (IWZOx/IWZOy, x < y) exhibits more stable distribution of a resistance state and also better endurance (more than 3 × 104 cycles). Meanwhile, the memory window of IWZOx/IWZOy can even be maintained over 104 s at 85 °C. Those improvements can be attributed to the oxygen vacancy distribution in switching layers, which may create a suitable environment for the conductive filament formation or rupture. Therefore, it is believed that the specific stacking bilayer IWZO CBRAM might further pave the way for emerging memory applications.

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“…Magnetoresistive randomaccess memory (MRAM) also shows resistance change by controlling the magnetization of magnetic material [6]. RRAM has the advantage of being capable of tunable resistive switching, which is applicable to the various application as storage memory [7][8][9][10][11][12][13][14][15][16][17][18][19][20], logicin-memory [21], and neuromorphic computing [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. Moreover, RRAM shows lowpower operation, high endurance, good retention, high-density integration, and good complementary metal-oxide-semiconductor (CMOS) compatibility in terms of process and material.…”

Section: Introductionmentioning

confidence: 99%

Effects of Oxygen Precursor on Resistive Switching Properties of CMOS Compatible HfO2-Based RRAM

Ryu

Kim

2021

Metals

Self Cite

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In this work, we investigate the resistive switching behaviors of HfO2-based resistive random-access memory (RRAM) in two different oxidants (H2O and O3) in an atomic layer deposition system. Firstly, the surface characteristics of the Ni/HfO2/Si stack are conducted by atomic force microscopy (AFM). A similar thickness is confirmed by scanning electron microscope (SEM) imaging. The surface roughness of the HfO2 film by O3 (O3 sample) is smoother than in the sample by H2O (H2O sample). Next, we conduct electrical characteristics by current–voltage (I–V) and capacitor–voltage (C–V) curves in an initial process. The forming voltage of the H2O sample is smaller than that of the O3 sample because the H2O sample incorporates a lot of H+ in the film. Additionally, the smaller capacitor value of the H2O sample is obtained due to the higher interface trap in H2O sample. Finally, we compare the resistive switching behaviors of both samples by DC sweep. The H2O sample has more increased endurance, with a smaller on/off ratio than the O3 sample. Both have good non-volatile properties, which is verified by the retention test.

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Emulation of Biological Synapse Characteristics from Cu/AlN/TiN Conductive Bridge Random Access Memory

Cited by 32 publications

References 46 publications

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

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

Oxygen Concentration Effect on Conductive Bridge Random Access Memory of InWZnO Thin Film

Effects of Oxygen Precursor on Resistive Switching Properties of CMOS Compatible HfO2-Based RRAM

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