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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]
Citations
Cites
A note on two problems in connexion with graphs
Dijkstra, E. (1959) · Numerische Mathematik · doi:10.1007/bf01386390 · Cited by 24,351
Electronic Processes in Non-Crystalline Materials
Mott, N. F., Davis, E. A., & Lösche, A. (1972) · De Gruyter eBooks · doi:10.1515/9783112653326-018 · Cited by 9,331
Redox‐Based Resistive Switching Memories – Nanoionic Mechanisms, Prospects, and Challenges
Waser, R., Dittmann, R., Staikov, G., & Szot, K. (2009) · Advanced Materials · doi:10.1002/adma.200900375 · Cited by 5,080
Electronic processes in non-crystalline materials
Jonscher, A. K. (1972) · Thin Solid Films · doi:10.1016/0040-6090(72)90068-5 · Cited by 4,021
Electronic Processes in Ionic Crystals
Mott, N. F., & Gurney, R. W. (1940) · CERN Document Server (European Organization for Nuclear Research) · Cited by 2,466
Lee, M.-J., Lee, C. B., Lee, D., Lee, S. R., Chang, M., Hur, J. H., Kim, Y.-B., Kim, C.-J., Seo, D. H., Seo, S., et al. (2011) · Nature Materials · doi:10.1038/nmat3070 · Cited by 2,228
Atomic structure of conducting nanofilaments in TiO2 resistive switching memory
Kwon, D.-H., Kim, K. M., Jang, J. H., Jeon, J. M., Lee, M. H., Kim, G. H., Li, X.-S., Park, G.-S., Lee, B., Han, S., et al. (2010) · Nature Nanotechnology · doi:10.1038/nnano.2009.456 · Cited by 2,092
Electronic Processes in Ionic Crystals
Fröhlich, H. (1949) · Nature · doi:10.1038/164377a0 · Cited by 1,310
Resistive Random Access Memory (ReRAM) Based on Metal Oxides
Akinaga, H., & Shima, H. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2010.2070830 · Cited by 1,037
Yu, S., Wu, Y., Jeyasingh, R., Kuzum, D., & Wong, H.-S. P. (2011) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2011.2147791 · Cited by 805
Self-citation
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Resistive switching memories based on metal oxides: mechanisms, reliability and scaling
Ielmini, D. (2016) · Semiconductor Science and Technology · doi:10.1088/0268-1242/31/6/063002 · Cited by 912
“Increasing the compliance current results in more defects being generated in the insulating layer, thus causing the presence of more parallel CFs [84].”
BackgroundMethodInfluentialA comprehensive review on emerging artificial neuromorphic devices
Zhu, J., Zhang, T., Yang, Y., & Huang, R. (2020) · Applied Physics Reviews · doi:10.1063/1.5118217 · Cited by 783
Yu, S., Gao, B., Fang, Z., Yu, H. Y., Kang, J., & Wong, H. ‐. P. (2013) · Advanced Materials · doi:10.1002/adma.201203680 · Cited by 526
Self-citationA novel true random number generator based on a stochastic diffusive memristor
Jiang, H., Belkin, D., Savel’ev, S. E., Lin, S., Wang, Z., Li, Y., Joshi, S., Midya, R., Li, C., Rao, M., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00869-x · Cited by 459
Brain-inspired computing with memristors: Challenges in devices, circuits, and systems
Zhang, Y., Wang, Z., Zhu, J., Yang, Y., Rao, M., Song, W., Zhuo, Y., Zhang, X., Cui, M., Shen, L., et al. (2020) · Applied Physics Reviews · doi:10.1063/1.5124027 · Cited by 389
Tian, H., Zhao, L., Wang, X., Yeh, Y., Yao, N., Rand, B. P., & Ren, T.-L. (2017) · ACS Nano · doi:10.1021/acsnano.7b05726 · Cited by 377
Yu, S., Chen, H.-Y., Gao, B., Kang, J., & Wong, H. ‐. P. (2013) · ACS Nano · doi:10.1021/nn305510u · Cited by 338
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Chen, Y. (2020) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2019.2961505 · Cited by 306
A Compact Model for Metal–Oxide Resistive Random Access Memory With Experiment Verification
Jiang, Z., Wu, Y., Yu, S., Yang, L., Song, K., Karim, Z., & Wong, H. ‐. P. (2016) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2016.2545412 · Cited by 280
“Several physical models have been reported for the SET and RESET processes of RRAM [24], [25].”
MethodSelf-citationChallenges and Applications of Emerging Nonvolatile Memory Devices
Banerjee, W. (2020) · Electronics · doi:10.3390/electronics9061029 · Cited by 285
“Previously, SET/RESET characteristics [60], effect of switching parameters [61], switching mechanism [62], filament structure, and growth [63] processes were studied in detail.”
BackgroundA general memristor-based partial differential equation solver
Zidan, M. A., Jeong, Y., Lee, J., Chen, B., Huang, S., Kushner, M. J., & Lu, W. D. (2018) · Nature Electronics · doi:10.1038/s41928-018-0100-6 · Cited by 268
Statistical Fluctuations in HfOx Resistive-Switching Memory: Part I - Set/Reset Variability
Ambrogio, S., Balatti, S., Cubeta, A., Calderoni, A., Ramaswamy, N., & Ielmini, D. (2014) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2014.2330200 · Cited by 232
“to variability and fluctuation effects, such as the switching statistics [5]–[10] and noise [11]–[14].”
BackgroundUltra-fast switching memristors based on two-dimensional materials
Nibhanupudi, S. S. T., Roy, A., Veksler, D. B., Coupin, M. J., Matthews, K. C., Disiena, M. N., Ansh, A., Singh, J. V., Gearba-Dolocan, I. R., Warner, J. H., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46372-y · Cited by 243
“Moreover, since the fi lament formation/dissolution in memristors is a stochastic process 51 – 53 , we employ statistical analysis of the transient switching characteristics to study these memristors.”
Yu, S., Guan, X., & Wong, H. ‐. P. (2012) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2012.2184544 · Cited by 226
“tunneling gap distance, as explained in Part I of the paper [14].”
BackgroundMethodInfluentialSelf-citationLi, Y., Long, S., Liu, Q., Lv, H., & Liu, M. (2017) · Small · doi:10.1002/smll.201604306 · Cited by 214
A Physics-Based Compact Model of Metal-Oxide-Based RRAM DC and AC Operations
Huang, P. C., Liu, X. Y., Chen, B., Li, H., Wang, Y. J., Deng, Y. X., Wei, K. L., Zeng, L., Gao, B., Du, G., et al. (2013) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2013.2287755 · Cited by 206
“The generation probability (Pg) of Vo under electric field during dt is governed by the following equation [12]:”
BackgroundHfO2-Based OxRAM Devices as Synapses for Convolutional Neural Networks
Garbin, D., Vianello, E., Bichler, O., Rafhay, Q., Gamrat, C., Ghibaudo, G., de Salvo, B., & Perniola, L. (2015) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2015.2440102 · Cited by 211
“, the minimum distance between traps [19], and G0 is defined as the quantum conductance equal to 2q2/h [20].”
BackgroundIn-memory Learning with Analog Resistive Switching Memory: A Review and Perspective
Xi, Y., Gao, B., Tang, J., Chen, A., Chang, M., Hu, X. S., der Spiegel, J. V., Qian, H., & Wu, H. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2020.3004543 · Cited by 200
“Agarwal et al. [119] focused on the impact of read noise and write noise on neural network learning, which is very common in weight updates [130], [131].”
BackgroundNanoionic memristive phenomena in metal oxides: the valence change mechanism
Dittmann, R., Menzel, S., & Waser, R. M. (2021) · Advances In Physics · doi:10.1080/00018732.2022.2084006 · Cited by 208
Resistive memory device requirements for a neural algorithm accelerator
Agarwal, S., Plimpton, S. J., Hughart, D. R., Hsia, A. H., Richter, I., Cox, J. A., James, C. D., & Marinella, M. J. (2016) · 2016 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727298 · Cited by 213
“Write noise occurs every time the state of a resistive memory changes [13, 33, 34].”
BackgroundRRAM Crossbar Array With Cell Selection Device: A Device and Circuit Interaction Study
Deng, Y., Huang, P., Chen, B., Yang, X., Gao, B., Wang, J., Zeng, L., Du, G., Kang, J., & Liu, X. (2013) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2012.2231683 · Cited by 184
Technological Exploration of RRAM Crossbar Array for Matrix-Vector Multiplication
Xia, L., Gu, P., Li, B., Tang, T., Yin, X., Huangfu, W., Yu, S., Cao, Y. K., Wang, Y., & Yang, H. (2016) · Journal of Computer Science and Technology · doi:10.1007/s11390-016-1608-8 · Cited by 184
“Moreover, some researchers further analyze the causes and consequences of some non-ideal factors of RRAM and propose some models to describe the performance of RRAM[23-25].”
BackgroundSelf-citationEmerging Applications for High K Materials in VLSI Technology
Clark, R. D. (2014) · Materials · doi:10.3390/ma7042913 · Cited by 174
“be implemented in manufacturing [150,151].”
Multi-level control of conductive nano-filament evolution in HfO 2 ReRAM by pulse-train operations
Zhao, L., Chen, H.-Y., Wu, S., Jiang, Z., Yu, S., Hou, T., Wong, H. ‐. P., & Nishi, Y. (2014) · Nanoscale · doi:10.1039/c4nr00500g · Cited by 163
Self-citationVoltage and Power-Controlled Regimes in the Progressive Unipolar RESET Transition of HfO2-Based RRAM
Long, S., Perniola, L., Cagli, C., Buckley, J., Lian, X., Miranda, E. A., Pan, F., Liu, M., & Suñé, J. (2013) · Scientific Reports · doi:10.1038/srep02929 · Cited by 157
Metal oxide-resistive memory using graphene-edge electrodes
Lee, S., Sohn, J., Jiang, Z., Chen, H.-Y., & Wong, H. ‐. P. (2015) · Nature Communications · doi:10.1038/ncomms9407 · Cited by 156
“emical Society (2013). In RRAM devices, the resistive switching is attributed to the formation (SET) and the subsequent rupture (RESET) of nanoscale conductive filaments involving oxygen ion migration(55, 57, 58). The number and the size of the conducting filament paths determine the two resistance states of the RRAM:…”
Self-citationFilament Growth and Resistive Switching in Hafnium Oxide Memristive Devices
Dirkmann, S., Kaiser, J. H., Wenger, C., & Mussenbrock, T. (2018) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.7b19836 · Cited by 150
True Random Number Generation by Variability of Resistive Switching in Oxide-Based Devices
Balatti, S., Ambrogio, S., Wang, Z., & Ielmini, D. (2015) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2015.2426492 · Cited by 147
“In this work, we propose a physical RNG based on the switching variability in a single RRAM device[10-14].”
BackgroundMethodAnalytical Modeling of Oxide-Based Bipolar Resistive Memories and Complementary Resistive Switches
Ambrogio, S., Balatti, S., Gilmer, D. C., & Ielmini, D. (2014) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2014.2325531 · Cited by 144
“One of the critical concerns for RRAM, in fact, is the limited resistance window, which makes single-bit and multilevel operation difficult in the presence of programming variability [20], [21] and read noise [22].”
BackgroundSharath, S. U., Vogel, S., Molina‐Luna, L., Hildebrandt, E., Wenger, C., Kurian, J., Duerrschnabel, M. T., Niermann, T., Niu, G., Calka, P., et al. (2017) · Advanced Functional Materials · doi:10.1002/adfm.201700432 · Cited by 134
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Zangeneh, M., & Joshi, A. (2013) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2013.2277715 · Cited by 124
“The variation of switching parameters in RRAM devices using a trap-assisted tunneling current solver considering the stochastic generation and recombination of oxygen vacancies is proposed in [21].”
MethodCycle-to-Cycle Intrinsic RESET Statistics in ${\rm HfO}_{2}$-Based Unipolar RRAM Devices
Long, S., Lian, X., Ye, T., Cagli, C., Perniola, L., Miranda, E. A., Liu, M., & Suñé, J. (2013) · IEEE Electron Device Letters · doi:10.1109/led.2013.2251314 · Cited by 114
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Balatti, S., Ambrogio, S., Wang, Z., Sills, S. E., Calderoni, A., Ramaswamy, N., & Ielmini, D. (2015) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2015.2463104 · Cited by 108
The effect of oxygen vacancy on switching mechanism of ZnO resistive switching memory
Hu, C. F., Wang, Q., Bai, S., Xu, M., He, D., Lyu, D., & Qi, J. (2017) · Applied Physics Letters · doi:10.1063/1.4976512 · Cited by 103
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Jiang, H., Li, C., Zhang, R., Yan, P., Lin, P., Li, Y., Yang, J. J., Holcomb, D. E., & Xia, Q. (2018) · Nature Electronics · doi:10.1038/s41928-018-0146-5 · Cited by 93
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Privitera, S., Bersuker, G. I., Butcher, B., Kalantarian, A., Lombardo, S. A., Bongiorno, C., Geer, R. E., Gilmer, D. C., & Kirsch, P. D. (2013) · Microelectronic Engineering · doi:10.1016/j.mee.2013.03.145 · Cited by 90
Hsu, C.-W., Wang, Y., Wan, C.-C., Wang, I., Chou, C.-T., Lai, W.-L., Lee, Y., & Hou, T. (2014) · Nanotechnology · doi:10.1088/0957-4484/25/16/165202 · Cited by 87
Kubicek, M., Schmitt, R., Messerschmitt, F., & Rupp, J. L. M. (2015) · ACS Nano · doi:10.1021/acsnano.5b02752 · Cited by 85
“Such current fluctuations are attributed to nanoscale conductive filaments in oxygen anionic resistive switches.(43) 45 Assuming a locally highly confined region carrying most of the current under a high electric field, then even very small changes, i.”
BackgroundBricalli, A., Ambrosi, E., Laudato, M., Maestro-Izquierdo, M., Rodriguez, R., & Ielmini, D. (2017) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2017.2777986 · Cited by 86
“In particular, RRAM generally suffers from programming variability [2]–[4] and noise-induced distribution broadening [3], [5], [6], which”
BackgroundForming‐Free Grain Boundary Engineered Hafnium Oxide Resistive Random Access Memory Devices
Petzold, S., Zintler, A., Eilhardt, R., Piros, E., Kaiser, N., Sharath, S. U., Vogel, T., Major, M., McKenna, K. P., Molina‐Luna, L., et al. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201900484 · Cited by 84
Aldana, S., García‐Fernández, P., Romero‐Zaliz, R., González, M. B., Jiménez-Molinos, F., Gómez‐Campos, F. M., Campabadal, F., & Roldán, J. B. (2020) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ab7bb6 · Cited by 87
“The simulation domain determines the geometric features of the simulator, 2D [1, 10, 23, 32] or 3D [5, 7, 11] codes have been developed.”
BackgroundInfluentialElectronic Instabilities Leading to Electroformation of Binary Metal Oxide‐based Resistive Switches
Sharma, A. A., Noman, M., Abdelmoula, M., Skowroński, M., & Bain, J. A. (2014) · Advanced Functional Materials · doi:10.1002/adfm.201400461 · Cited by 84
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Nayak, A., Satomi, U., Tai, S., Tsuruoka, T., Waser, R. M., Aono, M., Valov, I., & Hasegawa, T. (2018) · Advanced Materials · doi:10.1002/adma.201703261 · Cited by 84
Bousoulas, P., Stathopoulos, S., Tsialoukis, D., & Tsoukalas, D. (2016) · IEEE Electron Device Letters · doi:10.1109/led.2016.2575065 · Cited by 84
“this method [6].”
Wang, D.-T., Dai, Y., Xu, J. L., Chen, L., Sun, Q., Zhou, P., Wang, P., Ding, S., & Zhang, D. W. (2016) · IEEE Electron Device Letters · doi:10.1109/led.2016.2570279 · Cited by 80
“Variation in the resistive switching parameters is the critical issue of RRAM hindering its practical application, and similar arguments exist as well when it comes to flexible RRAM [8].”
BackgroundSet Variability and Failure Induced by Complementary Switching in Bipolar RRAM
Balatti, S., Ambrogio, S., Gilmer, D. C., & Ielmini, D. (2013) · IEEE Electron Device Letters · doi:10.1109/led.2013.2261451 · Cited by 79
“Low-power operation, however, leads to inevitable switching variability, due to individual defect generation/migration events affecting the device resistance R [4], [5].”
BackgroundNitrogen-induced improvement of resistive switching uniformity in a HfO2-based RRAM device
Xie, H., Liu, Q., Li, Y., Lv, H., Wang, M., Liu, X., Sun, H., Yang, X., Long, S., Liu, S., et al. (2012) · Semiconductor Science and Technology · doi:10.1088/0268-1242/27/12/125008 · Cited by 78
“In addition, the increase of Vstop will increase RHRS because more oxygen ions are driven back to the bulk oxide and result in a larger ruptured CF length [21, 22].”
BackgroundHighly Uniform Resistive Switching in HfO2 Films Embedded with Ordered Metal Nanoisland Arrays
Wang, J., Li, L., Huyan, H., Pan, X. Q., & Nonnenmann, S. S. (2019) · Advanced Functional Materials · doi:10.1002/adfm.201808430 · Cited by 79
A CMOS-compatible electronic synapse device based on Cu/SiO 2 /W programmable metallization cells
Chen, W., Fang, R., Balaban, M. B., Yu, W., Gonzalez-Velo, Y., Barnaby, H. J., & Kozicki, M. N. (2016) · Nanotechnology · doi:10.1088/0957-4484/27/25/255202 · Cited by 76
“65 g cm) and connects the pre-doped Cu regions to form a conductive network [16, 17].”
BackgroundResistive RAM-Centric Computing: Design and Modeling Methodology
Li, H., Wu, T. F., Mitra, S., & Wong, H. ‐. P. (2017) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2017.2709812 · Cited by 77
“The variability of the gap distance, CF length, and CF radius due to the stochastic nature of the processes of formation/migration/dissolution of the CF results in a statistical distribution of HRS resistance (RH RS) and LRS resistance (RL RS) after the write operations [53]–[55].”
Self-citationUnsupervised Learning on Resistive Memory Array Based Spiking Neural Networks
Guo, Y., Wu, H., Gao, B., & Qian, H. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00812 · Cited by 78
“…(LeCun et al., 1989), the conductance of memristive devices can be fine-tuned to the desired value during the training process, using write-verification scheme (Guan et al., 2012; Yao et al., 2017), which introduces operation overheads to modulate the device conductance more precisely.”
BackgroundFang, R., Gonzalez Velo, Y., Chen, W., Holbert, K. E., Kozicki, M. N., Barnaby, H., & Yu, S. (2014) · Applied Physics Letters · doi:10.1063/1.4875748 · Cited by 74
Self-citationPhysical Unclonable Function Exploiting Sneak Paths in Resistive Cross-point Array
Gao, L., Chen, P.-Y., Liu, R., & Yu, S. (2016) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2016.2578720 · Cited by 74
“Due to the randomness of the defect generation and annihilation in oxide, the shape of the conductive filament varies from device to device, and even from cycle to cycle within one device [23].”
BackgroundSelf-citationUnderstanding switching variability and random telegraph noise in resistive RAM
Ambrogio, S., Balatti, S., Cubeta, A., Calderoni, A., Ramaswamy, N., & Ielmini, D. (2013) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2013.6724732 · Cited by 71
Technological exploration of RRAM crossbar array for matrix-vector multiplication
Gu, P., Li, B., Tang, T., Yu, S., Cao, Y. K., Wang, Y., & Yang, H. (2015) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/aspdac.2015.7058989 · Cited by 71
Self-citationGarbin, D., Bichler, O., Vianello, E., Rafhay, Q., Gamrat, C., Perniola, L., Ghibaudo, G., & DeSalvo, B. (2014) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2014.7047126 · Cited by 76
Zhao, L., Clima, S., Magyari‐Köpe, B., Jurczak, M., & Nishi, Y. (2015) · Applied Physics Letters · doi:10.1063/1.4926337 · Cited by 66
Kim, S., Park, J., Kim, T., Hong, K., Hwang, Y., Park, B., & Kim, H. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202100273 · Cited by 64
The ultimate switching speed limit of redox-based resistive switching devices
Menzel, S., von Witzleben, M., Havel, V., & Böttger, U. (2018) · Faraday Discussions · doi:10.1039/c8fd00117k · Cited by 64
Phenomenological modeling of memristive devices
Bayat, F. M., Hoskins, B. D., & Strukov, D. B. (2015) · Applied Physics A · doi:10.1007/s00339-015-8993-7 · Cited by 61
A Robust 8-Bit Non-Volatile Computing-in-Memory Core for Low-Power Parallel MAC Operations
Zhang, S., Huang, K., & Shen, H. (2020) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2020.2971642 · Cited by 62
“As reported in RRAM devices, different reading voltages will cause 0% to 40% resistance deviations, and some even exceed 50% in LRS [10], [12], [13], [30], [39].”
BackgroundConductive filament structure in HfO2 resistive switching memory devices
Privitera, S., Bersuker, G. I., Lombardo, S. A., Bongiorno, C., & Gilmer, D. C. (2015) · Solid-State Electronics · doi:10.1016/j.sse.2015.05.044 · Cited by 59
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Huang, P. C., Zhu, D., Chen, S., Zhou, Z., Chen, Z., Gao, B., Liu, L., Liu, X., & Kang, J. (2017) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2016.2643162 · Cited by 57
“Hence, the O2− transportation rate and VO recombination rate decrease rapidly due to their exponential dependence on electrical filed [33], [34].”
BackgroundAldana, S., García‐Fernández, P., Rodriguez-Fernandez, A., Romero‐Zaliz, R., González, M. B., Jiménez-Molinos, F., Campabadal, F., Gómez‐Campos, F. M., & Roldán, J. B. (2017) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/aa7939 · Cited by 59
Nanometer-Scale ${\rm HfO}_{x}$ RRAM
Zhang, Z., Wu, Y., Wong, H. ‐. P., & Wong, S. S. (2013) · IEEE Electron Device Letters · doi:10.1109/led.2013.2265404 · Cited by 56
Self-citationReliability Improvement and Effective Switching Layer Model of Thin‐Film MoS2 Memristors
Huang, Y., Gu, Y., Mohan, S., Dolocan, A., Ignacio, N. D., Kutagulla, S., Matthews, K. C., Londoño‐Calderón, A., Chang, Y., Chen, Y., et al. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202214250 · Cited by 55
Stress-induced leakage current and trap generation in HfO2thin films
Mannequin, C., Gonon, P., Vallée, C., Latu-Romain, L., Bsiesy, A., Grampeix, H., Salaün, A., & Jousseaume, V. (2012) · Journal of Applied Physics · doi:10.1063/1.4756993 · Cited by 54
Roldán, J. B., Alonso, F., Aguilera, A. M., Maldonado, D., & Lanza, M. (2019) · Journal of Applied Physics · doi:10.1063/1.5079409 · Cited by 54
Field-enhanced route to generating anti-Frenkel pairs in HfO 2
Schie, M., Menzel, S., Robertson, J., Waser, R. M., & Souza, R. A. D. (2018) · Physical Review Materials · doi:10.1103/physrevmaterials.2.035002 · Cited by 53
Crossbar Nanoscale HfO2-Based Electronic Synapses
Matveyev, Y., Kirtaev, R. V., Fetisova, A., Zakharchenko, S., Negrov, D. V., & Zenkevich, A. V. (2016) · Nanoscale Research Letters · doi:10.1186/s11671-016-1360-6 · Cited by 52
“The probability of an oxygen vacancy generation during the time interval dt depends on the local electric field force Feq acting on the oxygen ion and is given by the formula [24]:”
BackgroundDuncan, D. L., Magyari‐Köpe, B., & Nishi, Y. (2016) · IEEE Electron Device Letters · doi:10.1109/led.2016.2524450 · Cited by 51
Ninomiya, T., Muraoka, S., Wei, Z., Yasuhara, R., Katayama, K., & Takagi, T. (2013) · IEEE Electron Device Letters · doi:10.1109/led.2013.2258653 · Cited by 50
“The conductive filament (CF) model is widely accepted as the mechanism of resistive switching [2]–[4].”
Background$${ SIM}^2{ RRAM}$$ S I M 2 R R A M : a physical model for RRAM devices simulation
Villena, M. A., Roldán, J. B., Jiménez-Molinos, F., Miranda, E. A., Suñé, J., & Lanza, M. (2017) · Journal of Computational Electronics · doi:10.1007/s10825-017-1074-8 · Cited by 53
Egorov, K. V., Lebedinskiǐ, Y. Y., Markeev, A. M., & Orlov, O. M. (2015) · Applied Surface Science · doi:10.1016/j.apsusc.2015.07.217 · Cited by 48
Electronic Conduction in Ti/Poly-TiO2/Ti Structures
Hossein-Babaei, F., & Alaei-Sheini, N. (2016) · Scientific Reports · doi:10.1038/srep29624 · Cited by 47
“Assuming electron hopping between IOVs to be the foremost conduction mechanism 54 , the observed reduction in Δ E a is consistent with the IOV motion for filament formation.”
Set statistics in conductive bridge random access memory device with Cu/HfO2/Pt structure
Zhang, M., Long, S., Wang, G., Xu, X., Li, Y., Liu, Q., Lv, H., Lian, X., Miranda, E. A., Suñé, J., et al. (2014) · Applied Physics Letters · doi:10.1063/1.4901530 · Cited by 47
Effects of RESET Current Overshoot and Resistance State on Reliability of RRAM
Song, J., Lee, D., Woo, J., Koo, Y., Cha, E., Lee, S., Park, J., Moon, K., Misha, S. H., Prakash, A., et al. (2014) · IEEE Electron Device Letters · doi:10.1109/led.2014.2316544 · Cited by 46
“It has already been reported that the current overshoot in the SET process has severe effects on the variability of the LRS [11], [12].”
BackgroundWang, G., Long, S., Yu, Z., Zhang, M., Ye, T., Li, Y., Xu, D., Lv, H., Liu, Q., Wang, M., et al. (2015) · Applied Physics Letters · doi:10.1063/1.4907604 · Cited by 46
Recent trends in hardware security exploiting hybrid CMOS-resistive memory circuits
Sahay, S., & Suri, M. (2017) · Semiconductor Science and Technology · doi:10.1088/1361-6641/aa8f07 · Cited by 44
Detection of the insulating gap and conductive filament growth direction in resistive memories
Yalon, E., Karpov, I. V., Karpov, V. G., Riess, I., Kalaev, D., & Ritter, D. (2015) · Nanoscale · doi:10.1039/c5nr03314d · Cited by 43
Complex Learning in Bio-plausible Memristive Networks
Deng, L., Li, G., Deng, N., Wang, D., Zhang, Z., He, W., Li, H., Pei, J., & Shi, L. (2015) · Scientific Reports · doi:10.1038/srep10684 · Cited by 43
Jing, Z., Yan, B., Yang, Y., & Huang, R. (2022) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2022.3186024 · Cited by 40
“The stochastic dynamics of the conductive filament formation and break introduces randomness among the SET-RESET cycles, i.e, the “C-to-C” variation [25], [26].”
BackgroundComprehensive Compact Phenomenological Modeling of Integrated Metal-Oxide Memristors
Nili, H., Vincent, A. F., Prezesio, M., Mahmoodi, M. R., Kataeva, I., & Strukov, D. B. (2020) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2020.2982128 · Cited by 38
Zhang, L., Fong, X., Chang, C.-H., Kong, Z. H., & Roy, K. (2015) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2015.2427251 · Cited by 38
“Table IV lists the optimization results of using BCH-[127, 15, 27], BCH-[127, 29, 21] alone, concatenated code of BCH-[127, 29, 21] and repetition code-[3, 1, 3], respectively (for convenience, we denote these three codec as ECC-I, ECC-II, and ECC-III).”
BackgroundMethodSo, H., Ji, H., Kim, S., & Kim, S. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202405544 · Cited by 38
Crystal that remembers: several ways to utilize nanocrystals in resistive switching memory
Banerjee, W., Liu, Q., Long, S., Lv, H., & Liu, M. (2017) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/aa7572 · Cited by 37
Kopperberg, N., Wiefels, S., Liberda, S., Waser, R. M., & Menzel, S. (2021) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c14667 · Cited by 38
Zhao, Y., Huang, P. C., Chen, Z., Liu, C., Li, H., Chen, B., Ma, W., Zhang, F., Gao, B., Liu, X., et al. (2016) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2016.2532470 · Cited by 36
“Under electric field, the generation probability (Pa) of VO can be described as [30] Pa = f · exp(−(E1 − φ1)/kB Tloc) (2) where f is the vibration frequency, E1 is the activation energy of Ta2O5, Tloc is the local temperature, and φ is the barrier height reduction induced by electric field.”
BackgroundPostcycling Degradation in Metal-Oxide Bipolar Resistive Switching Memory
Wang, Z., Ambrogio, S., Balatti, S., Sills, S. E., Calderoni, A., Ramaswamy, N., & Ielmini, D. (2016) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2016.2604370 · Cited by 35
“To explore the applications of RRAM, switching variability [7], [9], [10], resistance fluctuation [11]–[13], and cycling endurance failure [14]–[16] need to be addressed and understood.”
BackgroundEfficient and Robust RRAM-Based Convolutional Weight Mapping With Shifted and Duplicated Kernel
Zhang, Y., He, G., Wang, G., & Li, Y. (2020) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2020.2998728 · Cited by 35
“However, RRAM devices suffer from cycle-to-cycle conductance variation during writing, which is termed as “temporal variation” [19].”
BackgroundCompact Two-State-Variable Second-Order Memristor Model
Kim, S., Kim, H., & Choi, S. (2016) · Small · doi:10.1002/smll.201600088 · Cited by 34
Gradual reset and set characteristics in yttrium oxide based resistive random access memory
Petzold, S., Piros, E., Sharath, S. U., Zintler, A., Hildebrandt, E., Molina‐Luna, L., Wenger, C., & Alff, L. (2019) · Semiconductor Science and Technology · doi:10.1088/1361-6641/ab220f · Cited by 34
Fu, J., Liao, Z., & Wang, J. (2022) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2022.3146801 · Cited by 35
Gradient-descent-based learning in memristive crossbar arrays
Nair, M. V., & Dudek, P. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280658 · Cited by 33
“In addition, stochastic behavior has been observed [7] [15].”
BackgroundSelf-Heating During submicrosecond Current Transients in Pr0.7Ca0.3MnO3-Based RRAM
Panwar, N., Khanna, A., Kumbhare, P., Chakraborty, I., & Ganguly, U. (2016) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2016.2632712 · Cited by 33
Resistive Neural Hardware Accelerators
Smagulova, K., Fouda, M., Kurdahi, F., Salama, K., & Eltawil, A. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3268092 · Cited by 30
“This problem is known as a hard breakdown [77, 78].”
BackgroundResistive memory variability: A simplified trap-assisted tunneling model
Garbin, D., Vianello, E., Rafhay, Q., Azzaz, M., Candelier, P., de Salvo, B., Ghibaudo, G., & Perniola, L. (2015) · Solid-State Electronics · doi:10.1016/j.sse.2015.09.004 · Cited by 31
Nano-Intrinsic True Random Number Generation: A Device to Data Study
Kim, J., Nili, H., Truong, N. D., Ahmed, T., Yang, J., Jeong, D. S., Sriram, S., Ranasinghe, D. C., Ippolito, S. J., Chun, H., et al. (2019) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2019.2895045 · Cited by 31
“Therefore, an extremely rich degree of stochasticity is available to be harvested for ReRAM-based TRNGs [34]–[36].”
BackgroundPetzold, S., Miranda, E. A., Sharath, S. U., Muñoz-Gorriz, J., Vogel, T., Piros, E., Kaiser, N., Eilhardt, R., Zintler, A., Molina‐Luna, L., et al. (2019) · Journal of Applied Physics · doi:10.1063/1.5094864 · Cited by 31
Liu, Z., & Wang, L. (2020) · IEEE Access · doi:10.1109/access.2020.2990536 · Cited by 31
“The cycle-to-cycle variations on the other hand results in the temporal stochasticity induced by the randomness in atomic configurations over a long incubation period [169], [170], as revealed in Figure 25(a), which undoubtedly brings about some adverse effects on neuromorphic applications.”
Background
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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