Symmetric Linear Rise and Fall of Conductance in a Trilayer Stack Engineered ReRAM-Based Synapse

Vishwakarma, Kavita; Kishore, R.; Datta, Arnab

doi:10.1021/acsaelm.0c00585

Cited by 6 publications

(3 citation statements)

References 40 publications

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“…The resistive switching effect is exhibited in many classes of materials, such as chalcogenides, perovskites, organic compounds, solid state electrolytes, graphene, and metal oxides [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. Among the latter are binary oxides such as ZnO, HfO 2 , ZrO 2 , NiO, TiO 2 , WO 3 , TaO x , and Gd 2 O 3 , which is of particular interest due to its compatibility with CMOS technology, multi-bit switching, and simple chemistry [ 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ].…”

Section: Introductionmentioning

confidence: 99%

Nanoscale-Resistive Switching in Forming-Free Zinc Oxide Memristive Structures

et al. 2022

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This article presents the results of experimental studies of the impact of electrode material and the effect of nanoscale film thickness on the resistive switching in forming-free nanocrystalline ZnO films grown by pulsed laser deposition. It was demonstrated that the nanocrystalline ZnO film with TiN, Pt, ZnO:In, and ZnO:Pd bottom electrodes exhibits a nonlinear bipolar effect of forming-free resistive switching. The sample with Pt showed the highest resistance values RHRS and RLRS and the highest value of Uset = 2.7 ± 0.4 V. The samples with the ZnO:In and ZnO:Pd bottom electrode showed the lowest Uset and Ures values. An increase in the number of laser pulses from 1000 to 5000 was shown to lead to an increase in the thickness of the nanocrystalline ZnO film from 7.2 ± 2.5 nm to 53.6 ± 18.3 nm. The dependence of electrophysical parameters (electron concentration, electron mobility, and resistivity) on the thickness of the forming-free nanocrystalline ZnO film for the TiN/ZnO/W structure was investigated. The endurance test and homogeneity test for TiN/ZnO/W structures were performed. The structure Al2O3/TiN/ZnO/W with a nanocrystalline ZnO thickness 41.2 ± 9.7 nm was shown to be preferable for the manufacture of ReRAM and memristive neuromorphic systems due to the highest value of RHRS/RLRS = 2307.8 ± 166.4 and low values of Uset = 1.9 ± 0.2 V and Ures = −1.3 ± 0.5 V. It was demonstrated that the use of the TiN top electrode in the Al2O3/TiN/ZnO memristor structure allowed for the reduction in Uset and Ures and the increase in the RHRS/RLRS ratio. The results obtained can be used in the manufacturing of resistive-switching nanoscale devices for neuromorphic computing based on the forming-free nanocrystalline ZnO oxide films.

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Section: Introductionmentioning

confidence: 99%

Nanoscale-Resistive Switching in Forming-Free Zinc Oxide Memristive Structures

et al. 2022

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“…Among them, especially, the linearity and dynamic range are important parameters in hardware implementation because they can directly affect to recognition accuracy of the neuromorphic system [13][14][15]. Additional resistors [13], three-terminal structured synapse devices [14], and non-identical pulse scheme [16][17][18] have been proposed to overcome these limitations. However, the proposed method has other drawbacks such as the need for additional circuits and large power consumption.…”

Section: Introductionmentioning

confidence: 99%

Novel training method for metal-oxide memristive synapse device to overcome trade-off between linearity and dynamic range

Seo¹,

Han²,

Lee³

2022

Nanotechnology

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Synapse devices are essential for the hardware implementation of neuromorphic computing systems. However, it is difficult to realize ideal synapse devices because of issues such as nonlinear conductance change (linearity) and a small number of conductance states (dynamic range). In this study, the correlation between the linearity and dynamic range was investigated. Consequently, we found a trade-off relationship between the linearity and dynamic range and proposed a novel training method to overcome this trade-off.

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“…[1,2] Two types of 3D cross-point memories have been properly documented, including the storagemapped memory using phase change random-access memory (PCRAM) [3][4][5] or resistive random-access memory (ReRAM) [6][7][8][9][10] and memory mapped memory using p-spin torque transfer random-access memory (p-STT-MRAM). [11,12] In addition, ReRAM or conductive bridge random-access memory (CBRAM)-based neurons and synapses [13][14][15][16][17][18][19][20][21] have been extensively studied for artificial neural networks in contrast with the complementary metal oxide semiconductor field effect transistor-based neurons and synapses that have a limited ability to achieve a higher neural density. [22][23][24][25] Moreover, all memories have nonvolatile memory characteristics unlike DRAM.…”

mentioning

confidence: 99%

Bi‐Stable Resistance Generation Mechanism for Oxygenated Amorphous Carbon‐Based Resistive Random‐Access Memory

Jin

Kim

Woo

et al. 2022

Adv Elect Materials

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The oxygenated amorphous carbon (α‐COx)‐based resistive random‐access memory (ReRAM) generates a bi‐stable resistance via electroforming or the rupture of the conductive CC sp2 covalent bond filaments in the α‐COx resistive layer, which can be determined by the dependency of the intensity distribution of the oxygen ion (O2−), for the 3D cross‐point nonvolatile memory as the new memory hierarchical structure for an artificial neural network. The conductive CC sp2 and insulating CC sp3 covalent bonds are formed near the top of the resistive layer by drifting and diffusing O2− toward the bottom and top parts of the layer in the set and reset processes, respectively. The reset process has a different bi‐stable resistance generation mechanism from binary metal oxide‐based ReRAM and conductive bridge random‐access memory. The conductive CC sp2 covalent bond intensity in the α‐COx resistive layer affects the forming voltage and the write and erase endurance cycle of the α‐COx‐based ReRAM cells. The result shows that a lower proportion of conductive CC sp2 covalent bond leads to longer write and erase endurance cycles.

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Symmetric Linear Rise and Fall of Conductance in a Trilayer Stack Engineered ReRAM-Based Synapse

Cited by 6 publications

References 40 publications

Nanoscale-Resistive Switching in Forming-Free Zinc Oxide Memristive Structures

Nanoscale-Resistive Switching in Forming-Free Zinc Oxide Memristive Structures

Novel training method for metal-oxide memristive synapse device to overcome trade-off between linearity and dynamic range

Bi‐Stable Resistance Generation Mechanism for Oxygenated Amorphous Carbon‐Based Resistive Random‐Access Memory

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