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On the Switching Parameter Variation of Metal-Oxide RRAM—Part I: Physical Modeling and Simulation Methodology
Abstract
The variation of switching parameters is one of the major challenges to both the scaling and volume production of metal-oxide-based resistive random-access memories (RRAMs). In this two-part paper, the source of such parameter variation is analyzed by a physics-based simulator, which is equipped with the capability to simulate a large number ( ~1000) of cyclic SET-RESET operations. By comparing the simulation results with experimental data, it is found that the random current fluctuation experimentally observed in the RESET processes is caused by the competition between trap generation and recombination, whereas the variation of the high resistance states and the tail bits are directly correlated to the randomness of the trap dynamics. A combined strategy with a bilayer dielectric material and a write-verification technique is proposed to minimize the resistance variation. We describe the simulation methodology and discuss the dc results in Part I. The corroboration of the model and the device optimization strategy will be discussed in Part II.
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“The intrinsic variability of switching behavior in memristors has been a major obstacle to their adoption as the next generation of universal memory.”
From A novel true random number generator based on a stochastic diffusive memristor (2017) · cites this paper as [25]
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“On the other hand, this natural stochasticity can be valuable for hardware security applications.”
From A novel true random number generator based on a stochastic diffusive memristor (2017) · cites this paper as [27]
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Items 101–200 of 355
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“Previous research efforts were mainly focused on the intrinsic device-level variability and corresponding physical mechanisms, which were correlated with the choice for the materials, stack configuration, as well as operation conditions [16]–[21].”
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Analysis of the row grounding technique in a memristor‐based crossbar array
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“A coherent explanation of this effect can be given in the context of RS Kinetic Monte Carlo simulation [4, 11, 31-34].”
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“The Huang-Rhys factor, S, was set to 17, the phonon energy to 0.07 eV, and the electron effective mass of HfO x was set to the band mass 0.1 according to similar reports (Guan et al., 2012a).”
MethodInfluentialMemristor compact model with oxygen vacancy concentrations as state variables
Zeumault, A., Alam, S., Faruk, M. O., & Aziz, A. (2022) · Journal of Applied Physics · doi:10.1063/5.0087038 · Cited by 28
“For example, using the device geometry and thermal resistance (Rth = ∆x κA ) reported in one comprehensive modeling approach of HfO2 memristors [31] the effective thermal conductivity of the HfO2 evaluates to 100 Wm−1 K−1, which is ≈ 100 times larger than the measured value [50].”
BackgroundMethodMemristor-based ternary content addressable memory (mTCAM) for data-intensive computing
Zheng, L., Shin, S., & Kang, S.-M. S. (2014) · Semiconductor Science and Technology · doi:10.1088/0268-1242/29/10/104010 · Cited by 28
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“However, the metal oxidebased RRAM devices usually have a large variability [8-10], which hinders application in industries.”
BackgroundAnalysis on the Filament Structure Evolution in Reset Transition of Cu/HfO2/Pt RRAM Device
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Short Time High-Resistance State Instability of TaOx-Based RRAM Devices
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Modeling resistive switching materials and devices across scales
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“els published so far on this topic [10], [11], [31], [32], the formation/annihilation of V•• O s is considered to occur due to an oxygen exchange reaction at one of the electrodes (anode/cathode) solely.”
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Atomic Layer Deposition Films for Resistive Random‐Access Memories
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Atomically Thin CBRAM Enabled by 2-D Materials: Scaling Behaviors and Performance Limits
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“1 nW, which is on the orders of magnitude lower than conventional CBRAM [8], [9], [11], [12].”
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Nishi, Y., Fleck, K., Böttger, U., Waser, R. M., & Menzel, S. (2015) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2015.2411748 · Cited by 24
“The variability can be attributed to the randomness of the defect distributions in the film, being an intrinsic feature of the resistive switching phenomena [4].”
BackgroundAldana, S., Roldán, J. B., García‐Fernández, P., Suñé, J., Romero‐Zaliz, R., Jiménez-Molinos, F., Long, S., Gómez‐Campos, F. M., & Liu, M. (2018) · Journal of Applied Physics · doi:10.1063/1.5020148 · Cited by 24
Zhao, Y., Huang, P. C., Zhou, Z., Liu, C., Qin, S., Liu, L., Liu, X., Wong, H. ‐. P., & Kang, J. (2019) · IEEE Electron Device Letters · doi:10.1109/led.2019.2901754 · Cited by 24
“The probability of the atom migration (Pm) during the diffusion process can be described by [24], [26]”
BackgroundSelf-citationBi2O2Se-Based Memristor-Aided Logic
Liu, B., Zhao, Y., Verma, D., Wang, L. A., Liang, H., Zhu, H., Li, L., Hou, T., & Lai, C. (2021) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c00177 · Cited by 23
Dependence of the SET switching variability on the initial state in HfOx‐based ReRAM
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An overview of the switching parameter variation of RRAM
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“The cycleto-cycle and device-to-device fluctuation of switching parameters will become the crucial issue which will affect the application of the RRAM array, thus increasing the complexity of peripheral circuits and hindering the largescale integration and practical application of RRAM [26–28].”
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“reduce the average performance and generate cost increases due to the need for complex overhead circuits [5, 9, 10].”
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Berco, D., & Tseng, T. (2015) · Journal of Computational Electronics · doi:10.1007/s10825-015-0736-7 · Cited by 22
Atomically thin graphene plane electrode for 3D RRAM
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Self-citationHybrid CMOS-RRAM True Random Number Generator Exploiting Coupled Entropy Sources
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“The order of toggling of the inputs is dictated by the switching characteristics of RRAM1 and RRAM2, which are stochastic and highly unpredictable [29], [30], [31], [32], [34], [35], [36], [37].”
BackgroundConductive Filament Expansion in TaOxBipolar Resistive Random Access Memory during Pulse Cycling
Ninomiya, T., Katayama, K., Muraoka, S., Yasuhara, R., Mikawa, T., & Wei, Z. (2013) · Japanese Journal of Applied Physics · doi:10.7567/jjap.52.114201 · Cited by 21
Graphene resistive random memory — the promising memory device in next generation
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Plasticity and learning behavior emulated in a ZnO based transparent artificial synapse
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“the minimum distance between traps [9], and is defined as the conductance quantum equal to 2q/h.”
BackgroundMethodInterface Engineering for 3-Bit per Cell Multilevel Resistive Switching in AlN Based Memristor
Mohanty, S. K., Reddy, P. K., Prasad, O. K., Wu, C., Chang, K., & Lin, J.-C. (2021) · IEEE Electron Device Letters · doi:10.1109/led.2021.3125151 · Cited by 20
Operation methods of resistive random access memory
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“…a few challenges hold back the practical commercialization of RRAM, including the not good enough device performances, the lack of deep understanding of the physical and chemical mechanism of RS processes, and the obvious cycle-to-cycle and device-to-device fluctuation of RS parameters [16,17].”
BackgroundLeveraging Stochastic Memristor Devices in Neuromorphic Hardware Systems
Hu, M., Wang, Y., Wen, W., Wang, Y., & Li, H. H. (2016) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2016.2547780 · Cited by 20
Rescuing RRAM-Based Computing From Static and Dynamic Faults
Lin, J., Wen, C.-D., Hu, X., Tang, T., Lin, I.-C., Wang, Y., & Xie, Y. (2020) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2020.3037316 · Cited by 19
Modeling and simulation of graphene-oxide-based RRAM
Lim, E. W., Ahmadi, M. T., & Ismail, R. (2016) · Journal of Computational Electronics · doi:10.1007/s10825-016-0813-6 · Cited by 19
Chen, L., He, Z., Wang, T., Dai, Y., Zhu, H., Sun, Q., & Zhang, D. W. (2018) · Electronics · doi:10.3390/electronics7060080 · Cited by 19
“This could be due to the potentiati g process being a positive feedback between the formation rate of metallic Ag filament, the temperature and local field strength [22,23], while the depressing process is a negative feedback and a slow decay of conductance can be observed.”
BackgroundExperimental and computational analysis of thermal environment in the operation of HfO2 memristors
Pahinkar, D. G., Basnet, P., West, M. P., Zivasatienraj, B., Weidenbach, A. S., Doolittle, W. A., Vogel, E. M., & Graham, S. L. (2020) · AIP Advances · doi:10.1063/1.5141347 · Cited by 19
Data-driven RRAM device models using Kriging interpolation
Hossen, I., Anders, M. A., Wang, L., & Adam, G. C. (2022) · Scientific Reports · doi:10.1038/s41598-022-09556-4 · Cited by 20
3-D Resistive Memory Arrays: From Intrinsic Switching Behaviors to Optimization Guidelines
Li, H., Gao, B., Chen, H.-Y., Chen, Z., Huang, P. C., Liu, R., Zhao, L., Jiang, Z., Liu, L., Liu, X., et al. (2015) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2015.2468602 · Cited by 18
“Deeper RESET states from higher access voltages will induce wider gaps in the RRAM for the following SET operations [27].”
BackgroundSelf-citationChen, Y., Chang, T., Yang, P., Huang, J.-J., Tseng, H.-C., Huang, H., Yang, J.-B., Chu, A., Gan, D., Tsai, M., et al. (2013) · IEEE Electron Device Letters · doi:10.1109/led.2012.2232276 · Cited by 18
High-Endurance Bipolar ReRAM-Based Non-Volatile Flip-Flops with Run-Time Tunable Resistive States
Biglari, M., Lieske, T., & Fey, D. (2018) · IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH) · doi:10.1145/3232195.3232217 · Cited by 18
“Switching parameters of ReRAM cells undergo significant variations from one cell to another, but also in the same cell in different cycles [3].”
BackgroundKinetic Monte Carlo Simulation of Interface-Controlled Hafnia-Based Resistive Memory
Xu, X., Rajendran, B., & Anantram, M. P. (2019) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2019.2953917 · Cited by 18
“Guan et al. [25] and Yu et al. [26] modeled the current using a tunneling formalism while the vacancy generation and recombination rates are determined by the concentration of oxygen ions.”
BackgroundBenefit of Al2O3/HfO2 bilayer for BEOL RRAM integration through 16kb memory cut characterization
Azzaz, M., Benoist, A., Vianello, E., Garbin, D., Jalaguier, E., Cagli, C., Charpin, C., Bernasconi, S., Jeannot, S., Dewolf, T., et al. (2015) · European Solid-State Device Research Conference · doi:10.1109/essderc.2015.7324765 · Cited by 17
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“achieving better reliability and uniformity [4].”
Li, H. K., Chen, T. P., Hu, S., Liu, P. P., Liu, Y., Lee, P. S., Wang, X. P., Li, H. Y., & Lo, G. Q. (2015) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2015.2445339 · Cited by 17
“C can be described with C = εrε0 A/tox, where ε0 is the permittivity in vacuum, and εr and tox are the dielectric constant and thickness of the leakage gap, respectively; and R can be assumed to be R = R0exp(αtox), where R0 and α are two constants as it has been suggested by Monte Carlo simulation that R has an expone…”
MethodNoh, J., Jo, M., Kang, C. Y., Gilmer, D. C., Kirsch, P. D., Lee, J. C., & Lee, B. H. (2013) · IEEE Electron Device Letters · doi:10.1109/led.2013.2271831 · Cited by 16
“Since early ReRAM devices showed very unstable and scattered characteristics, the mechanism of resistance transition has been studied extensively [3]–[5].”
BackgroundStatistical characteristics of reset switching in Cu/HfO2/Pt resistive switching memory
Zhang, M., Long, S., Wang, G., Liu, R., Xu, X., Li, Y., Xu, D., Liu, Q., Lv, H., Miranda, E. A., et al. (2014) · Nanoscale Research Letters · doi:10.1186/1556-276x-9-694 · Cited by 16
Aziz, T., Wei, S., Sun, Y., Ma, L., Pei, S., Dong, S., Ren, W., Liu, Q., Cheng, H., & Sun, D. (2021) · Nanoscale · doi:10.1039/d0nr07888c · Cited by 16
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Hydrogen-Induced Oxygen Vacancy Bistability and Its Impact on RRAM Device Operation
Jung, K., Magyari‐Köpe, B., & Nishi, Y. (2017) · IEEE Electron Device Letters · doi:10.1109/led.2017.2693368 · Cited by 16
Area-Selective Atomic Layer Deposition for Resistive Random-Access Memory Devices
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Self-citationGraphene-based RRAM devices for neural computing
Rajalekshmi, T. R., Das, R. R., Reghuvaran, C., & James, A. P. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1253075 · Cited by 14
“This aligns with the switching mechanism observed in HfO 2 RRAM devices utilizing a top electrode composed of TiN in conjunction with a passive bottom electrode (Yu et al., 2012).”
BackgroundMethodKnabe, J., Berg, F., Goß, K., Böttger, U., & Dittmann, R. (2023) · physica status solidi (a) · doi:10.1002/pssa.202300409 · Cited by 14
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Precise weight tuning in quantum dot-based resistive-switching memory for neuromorphic systems
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A distributed nanocluster based multi-agent evolutionary network
Xu, L., Zhu, J., Chen, B., Yang, Z., Liu, K., Dang, B., Zhang, T., Yang, Y., & Huang, R. (2022) · Nature Communications · doi:10.1038/s41467-022-32497-5 · Cited by 14
“It is worthwhile noting that the physiochemical processes involved and the inhomogeneity of cluster distribution have introduced intrinsic stochasticity into the Ag nanoclusters-based multi-Article agent evolutionary network 45 .”
Mechanism of memristive switching in OxRAM
Menzel, S., & Waser, R. M. (2019) · Elsevier eBooks · doi:10.1016/b978-0-08-102584-0.00005-x · Cited by 14
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“The recombination of V O is modeled as the energy relaxation process and follows [16]: where n o 2 − is the available O 2 − number and E r is the relaxation energy that is typically low due to the instability of anti-Frenkel pairs [24], [25].”
BackgroundMethodRecent progress of resistive switching random access memory (RRAM)
Wu, Y., Yu, S., Guan, X., & Wong, H. ‐. P. (2012) · IEEE Silicon Nanoelectronics Workshop · doi:10.1109/snw.2012.6243331 · Cited by 13
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“This trend confirms the exponential activation of the vacancy generation rate (this generation of vacancies in the dielectric material is responsable of the set event, by creation of the conducting filament) with the electric field, that can be found in several modeling papers [12,13] : A small increase of Vset compen…”
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A high-precision time-domain RRAM state control approach
Bagheri-Soulla, A. A., & Ghaznavi‐Ghoushchi, M. B. (2018) · Microelectronics Journal · doi:10.1016/j.mejo.2017.12.013 · Cited by 13
Cycling-induced degradation of metal-oxide resistive switching memory (RRAM)
Wang, Z., Ambrogio, S., Balatti, S., Sills, S. E., Calderoni, A., Ramaswamy, N., & Ielmini, D. (2015) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2015.7409649 · Cited by 13
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Abbaspour, E., Menzel, S., & Jungemann, C. (2015) · International Conference on Simulation of Semiconductor Processes and Devices · doi:10.1109/sispad.2015.7292317 · Cited by 12
“All of them assume that Frenkel defect pairs consisting of oxygen interstitials and oxygen vacancies can be generated within the bulk film [5], [8]–[10].”
BackgroundPuglisi, F. M., Deleruyelle, D., Portal, J., Pavan, P., & Larcher, L. (2016) · physica status solidi (a) · doi:10.1002/pssa.201532828 · Cited by 12
Modular Structure of Compact Model for Memristive Devices
Zheng, L., Shin, S., & Kang, S.-M. S. (2014) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2014.2309871 · Cited by 12
“Spatial variations are usually stochastic due to the nature of the fabrication process [59]–[61].”
BackgroundMethodChung, Y.-T., Su, P.-C., Lin, W., Chen, M., & Wang, T. (2016) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2016.2555333 · Cited by 12
“These numbers are close to the published results [23], [24].”
ResultNakajima, R., Azuma, A., Yoshida, H., Shimizu, T., Ito, T., & Shingubara, S. (2018) · Japanese Journal of Applied Physics · doi:10.7567/jjap.57.06hd06 · Cited by 12
BRoCoM: A Bayesian Framework for Robust Computing on Memristor Crossbar
Gao, D., Yang, Z., Huang, Q., Zhang, G. L., Yin, X., Li, B., Schlichtmann, U., & Zhuo, C. (2022) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2022.3215071 · Cited by 12
“Write Noise: Imperfect programming, i.e., noise during write operations, is another prominent reason causing the stored conductance deviating from its intended value [8], [30], which is related to the stochastic nature of the memristor device [31], [32].”
BackgroundLee, M., Tang, W., Chen, Y., Wu, J., Zhong, H., Xu, Y., Liu, Y., Yang, H., Narayanan, V., & Li, X. (2023) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac56929.2023.10247934 · Cited by 12
“First, during RRAM programming, the stochastic dynamic of conductive filament will result in a variation in the target programmed state, known as cycle-to-cycle (C2C) variations [30].”
BackgroundBerco, D., & Tseng, T. (2015) · Applied Physics Letters · doi:10.1063/1.4938210 · Cited by 11
Studying the switching variability in redox-based resistive switching devices
Abbaspour, E., Menzel, S., & Jungemann, C. (2020) · Journal of Computational Electronics · doi:10.1007/s10825-020-01537-y · Cited by 11
A Predictive Compact Model of Bipolar RRAM Cells for Circuit Simulations
Chiang, M., Hsu, K.-H., Ding, W.-W., & Yang, B.-R. (2015) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2015.2428293 · Cited by 11
“A number of models have been developed to simulate resistive switching during reset process [16], [17].”
BackgroundLong, S., Lian, X., Cagli, C., Perniola, L., Miranda, E. A., Jiménez, D., Lv, H., Liu, Q., Li, L., Huo, Z., et al. (2013) · IEEE International Reliability Physics Symposium proceedings · doi:10.1109/irps.2013.6532023 · Cited by 11
“Furthermore, understanding the SET and RESET switching statistics [23-28] is also required to understand and model the retention time [26] of RRAM in the OFF and ON states, respectively.”
BackgroundAtomic Monte-Carlo simulation for CBRAM with various filament geometries
Zhao, Y., Huang, P. C., Guo, Z. H., Lun, Z., Gao, B., Liu, X. Y., & Kang, J. (2016) · International Conference on Simulation of Semiconductor Processes and Devices · doi:10.1109/sispad.2016.7605170 · Cited by 11
“The migration probability can be described as [9]”
BackgroundInfluentialDuncan, D. L., Magyari‐Köpe, B., & Nishi, Y. (2012) · MRS Proceedings · doi:10.1557/opl.2012.980 · Cited by 11
Low power and high uniformity of HfOx-based RRAM via tip-enhanced electric fields
Li, X., Zhang, B., Wang, B., Xu, X., Yang, Y., Sun, S., Cai, Q., Hu, S., An, X., Li, M., et al. (2019) · Science China Information Sciences · doi:10.1007/s11432-019-9910-x · Cited by 11
So, H., Kim, S., & Kim, S. (2024) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2024.175644 · Cited by 10
NbOx RRAM performance enhancement by surface modification with Au nanoparticles
Jing, X., Qiao, L., Yang, Y., Liu, Y., Zhu, Y., Cheng, J., Wang, H., & Xiong, R. (2024) · Vacuum · doi:10.1016/j.vacuum.2024.113422 · Cited by 10
Hybrid CMOS-OxRAM RNG circuits
Sahay, S., Suri, M., Kumar, A., & Parmar, V. (2016) · IEEE International Conference on Nanotechnology (IEEE-NANO) · doi:10.1109/nano.2016.7751437 · Cited by 10
A cell-based clustering model for the reset statistics in RRAM
Sun, H., Zhang, M., Li, Y., Long, S., Liu, Q., Lv, H., Suñé, J., & Liu, M. (2017) · Applied Physics Letters · doi:10.1063/1.4978756 · Cited by 10
Rodriguez-Fernandez, A., Muñoz-Gorriz, J., Suñé, J., & Miranda, E. A. (2018) · Microelectronics Reliability · doi:10.1016/j.microrel.2018.06.120 · Cited by 10
“(5) is widely used to describe the ion flow in OxRAM and CBRAM (conductive bridge) devices [17].”
ResultHuang, Y., Gu, Y., Chang, Y., Chen, Y., Akinwande, D., & Lee, J. C. (2023) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2023.3249139 · Cited by 9
“other physics-based MC simulators [28], [29].”
Yang, Z., Xu, C., & Xie, Y. (2015) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/aspdac.2015.7058990 · Cited by 9
“While the spatial variation typically comes from process variation and line edge roughness, which could be mitigated to some extent, the temporal variation results from the stochastic nature of the conductive filament formation and rupture, which results in large variations in a single device from cycle to cycle [7].”
BackgroundHou, K., Chen, S., Zhou, C. Y., Nguyen, L. L., Dananjaya, P. A., Duchamp, M., Bazan, G. C., Lew, W. S., & Leong, W. L. (2021) · Nano Letters · doi:10.1021/acs.nanolett.1c03180 · Cited by 9
Zhang, Y., Zhao, X., Ma, X., Liu, Y., Zhou, X., Zhang, M., Xu, G., & Long, S. (2022) · Science China Materials · doi:10.1007/s40843-021-1956-9 · Cited by 9
Design of RRAM with high storage capacity and high reliability for IoT applications
Wang, J., Ji, L., Bi, J., Liu, M., Xi, K., Majumdar, S., & Mehmood, S. (2022) · Solid-State Electronics · doi:10.1016/j.sse.2022.108292 · Cited by 9
A numerical analysis of progressive and abrupt reset in conductive bridging RRAM
Berco, D., & Tseng, T. (2015) · Journal of Computational Electronics · doi:10.1007/s10825-015-0744-7 · Cited by 9
Raghavan, N., Pey, K. L., Frey, D., & Bosman, M. (2014) · IEEE International Reliability Physics Symposium proceedings · doi:10.1109/irps.2014.6861163 · Cited by 9
Investigation of random telegraph noise amplitudes in hafnium oxide resistive memory devices
Chung, Y.-T., Liu, Y. H., Su, P.-C., Cheng, Y. H., Wang, T., & Chen, M. C. (2014) · IEEE International Reliability Physics Symposium proceedings · doi:10.1109/irps.2014.6861157 · Cited by 9
Chen, P.-S., Chen, Y.-S., Lee, H.-Y., Wu, T.-Y., Tsai, K.-H., Gu, P.-Y., Chen, W.-S., Tsai, C.-H., Chen, F. T., & Tsai, M. (2012) · Microelectronic Engineering · doi:10.1016/j.mee.2012.12.012 · Cited by 9
Metadata
- Authors
- Guan, X., Yu, S., & Wong, H.-S. P.
- Affiliations
- Stanford SystemX Alliance (US)
- Venue
- IEEE Transactions on Electron Devices
- Published
- 2012-04
- Volume
- 59
- Issue
- 4
- Pages
- 1172-1182
- ISSN
- 0018-9383 (ISSN-L)
- 1557-9646
- OpenAlex
- W2067242107
- Cited by
- 355
- References
- 43
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 19 · 2023: 21 · 2022: 18 · 2021: 18 · 2020: 29 · 2019: 23 · 2018: 26 · 2017: 35 · 2016: 39 · 2015: 35 · 2014: 32 · 2013: 23 · 2012: 13
- FWCI
- 21.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Electron Devices (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Semiconductor materials and devices
- Keywords
- resistive random access memory, current fluctuations, reset operation, high resistance state, bilayer dielectric, physics-based device modeling
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