An experimental and simulation study of the role of thermal effects on variability in TiN/Ti/HfO2/W resistive switching nonlinear devices

Maldonado, David; Aguilera-Pedregosa, C.; Vinuesa, Guillermo; García, Héctor; Dueñas, S.; Castán, Helena; Aldana, Samuel; González, M.B.; Moreno, Eva María Olmedo; Jiménez-Molinos, F.; Campabadal, F.; Roldán, J.B.

doi:10.1016/j.chaos.2022.112247

Cited by 10 publications

(6 citation statements)

References 71 publications

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“…The values of V set and V reset are similar, although the shape of the I – V curves is different from each other (see the logarithmic version of the plots in the corresponding insets). A traditional variability analysis of V set and V reset (as those reported in refs and ) would indicate that the cycle-to-cycle variability is zero. However, using our advanced holistic approach, we can quantify the variability accounting for all the data in the I – V curves.…”

Section: Results and Discussionmentioning

confidence: 99%

“…The operation of memristive nonvolatile memories made of metal oxide materials, often termed resistive random access memories (RRAM), is characterized by resistive switching (RS) where charge conduction is linked to internal ion movement and concurrent redox reactions in the dielectric and dielectric/electrode interfaces, which can lead to different resistive states in both a digital and an analog context. − Academic studies have shown devices with a good endurance above >10 10 cyclesalthough there is no commercial RRAM with an endurance higher than 10 7 cyclesas well as long data retention time above 10 years and low write energy down to ∼0.1 pJ . Moreover, their technology is complementary metal–oxide–semiconductor (CMOS) compatible, and the devices can be built in compact crossbar structures (with 4 F 2 footprint, where F is the minimum technology haft-pitch) …”

Section: Introductionmentioning

confidence: 99%

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Holistic Variability Analysis in Resistive Switching Memories Using a Two-Dimensional Variability Coefficient

Acal

Maldonado

Aguilera

et al. 2023

ACS Appl. Mater. Interfaces

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We present a new methodology to quantify the variability of resistive switching memories. Instead of statistically analyzing few data points extracted from current versus voltage (I− V) plots, such as switching voltages or state resistances, we take into account the whole I−V curve measured in each RS cycle. This means going from a one-dimensional data set to a two-dimensional data set, in which every point of each I−V curve measured is included in the variability calculation. We introduce a new coefficient (named two-dimensional variability coefficient, 2DVC) that reveals additional variability information to which traditional one-dimensional analytical methods (such as the coefficient of variation) are blind. This novel approach provides a holistic variability metric for a better understanding of the functioning of resistive switching memories.

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Section: Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Holistic Variability Analysis in Resistive Switching Memories Using a Two-Dimensional Variability Coefficient

Acal

Maldonado

Aguilera

et al. 2023

ACS Appl. Mater. Interfaces

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“…[ 128,129 ] As reported in previous sections, variability is always present in RRAMs due to their inherent stochastic behavior; this effect occurs for the different technologies employed to fabricate these devices [ 3,6,7,34,138–140 ] and for different operation conditions where temperature and external fields (e.g., magnetic) are changed. [ 141,142 ] In this respect, there is a clear need to model variability to make EDA tools suitable for IC design. In this section and the following ones, we will focus on the C2C variability by analyzing RS parameters such as V set , V reset , and the device resistances at the LRS and HRS.…”

Section: Variability Physical Modelsmentioning

confidence: 99%

Variability in Resistive Memories

Roldán

Miranda

Maldonado

et al. 2023

Advanced Intelligent Systems

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Research and development efforts in the nonvolatile memory arena are focused on a reduced set of innovative components, among which we can include memristors. [1,2] Memristors are expected to be key players in the electronics landscape of the coming years largely because of the powerful applications that stand upon their unique features. [1,[3][4][5] The switching mechanisms behind memristors differ significantly depending on the physical properties of the structures and the materials involved. [1,[3][4][5][6][7] To list some of these mechanisms, we can highlight those devices based on phase-change materials, which can be switched reversibly between amorphous and crystalline phases with different electrical resistivity (phasechange memories, PCMs); [8] devices that take advantage of the magnetic and electrical properties exhibited by some materials with different architectures (magnetic RAMs, MRAMs); [9] also structures where materials with switchable electrical polarization give rise to hysteresis curves of the polarization versus electrical field that can be engineered for storing information (ferroelectric FET, FFET); [10] and, finally, resistive RAMs (RRAMs) where the dielectric conduction properties are altered by means of the internal ion movement and concurrent redox reactions used to generate different resistive states. [1,3,11,12]

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“…Consequently, exposure to aggressive environments not only affects data retention lifetime but also impairs device operation, leading to the degradation of resistance ratios . Moreover, since the basic operation of memristors involves a limited number of ion migrations, there can be variabilities in the movement of those ions, which can lead to differences in device resistance . The LRS in conductive filament (CF)-based devices is determined by the presence of a percolation path between the electrodes and the data retention is the time this CF takes to vanish under ambient conditions, leading the device to the HRS.…”

Section: Introductionmentioning

confidence: 99%

“… 18 Moreover, since the basic operation of memristors involves a limited number of ion migrations, there can be variabilities in the movement of those ions, which can lead to differences in device resistance. 19 The LRS in conductive filament (CF)-based devices is determined by the presence of a percolation path between the electrodes and the data retention is the time this CF takes to vanish under ambient conditions, 14 leading the device to the HRS. Since the data retention depends on the CF stability, it also depends on the compliance current used for the forming or SET process that determines the CF size.…”

Section: Introductionmentioning

confidence: 99%

Unravelling the Data Retention Mechanisms under Thermal Stress on 2D Memristors

Aldana

Zhang

2023

ACS Omega

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Memristors based on two-dimensional (2D) materials are a rapidly growing research area due to their potential in energy-efficient in-memory processing and neuromorphic computing. However, the data retention of these emerging memristors remains sparsely investigated, despite its crucial importance to device performance and reliability. In this study, we employ kinetic Monte–Carlo simulations to investigate the data retention of a 2D planar memristor. The operation of the memristor depends on field-driven on defect migration, while thermal diffusion gradually evens the defect distribution, leading to the degradation of the high resistance state (HRS) and diminishing the ON/OFF ratio. Notably, we examine the resilience of devices based on single crystals of transition metal dichalcogenides (TMDs) in harsh environments. Specifically, our simulations show that MoS2-based devices have negligible degradation after 10 years of thermal annealing at 400 K. Furthermore, the variability in data retention lifetime across different temperatures is less than 22%, indicating a relatively consistent performance over a range of thermal conditions. We also demonstrate that device miniaturization does not compromise data retention lifetime. Moreover, employing materials with higher activation energy for defect migration can significantly enhance data retention at the cost of increased switching voltage. These findings shed light on the behavior of 2D memristors and pave the way for their optimization in practical applications.

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An experimental and simulation study of the role of thermal effects on variability in TiN/Ti/HfO2/W resistive switching nonlinear devices

Cited by 10 publications

References 71 publications

Holistic Variability Analysis in Resistive Switching Memories Using a Two-Dimensional Variability Coefficient

Holistic Variability Analysis in Resistive Switching Memories Using a Two-Dimensional Variability Coefficient

Variability in Resistive Memories

Unravelling the Data Retention Mechanisms under Thermal Stress on 2D Memristors

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