Top

Journal of Computational Electronics

Published in:

10-09-2024

Simulations of RF wave-induced modulation of filament growth and bipolar resistive switching in conductive bridging RAM

Authors: Yifei Yin, Toshihiro Nakaoka

Published in: Journal of Computational Electronics | Issue 6/2024

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

We have simulated Ag–Ge–Te-based conductive bridge RAM (CBRAM) under RF electromagnetic wave input to investigate the RF effects on heat transfer and electrochemical reaction. The RF simulations agreed with the experimental transmission coefficient S₂₁ between 0.4 and 1 GHz, indicating an effective, uniform electric field applied in the RF-applicable CBRAMs. The heat transfer simulations showed a minimal temperature increase of about 1 K under the RF wave at 10 MHz and 10 dBm, indicating negligible thermal effects. The electrochemical simulations were based on the Nernst–Planck equation, taking into account the Ag ion transport in the Ag–GeTe electrolyte by diffusion and migration. Electrode kinetics were calculated for charge transfer reactions using the Butler–Volmer equation. The cathode electrode moved at a velocity equal to the rate of Ag electrodeposition on the cathode. The electrode movement represented filament growth. The electrochemical simulations successfully reproduced filament growth, bipolar resistive switching, experimental currents, and SET/RESET voltages. In addition, the electrochemical simulations under RF waves showed a decrease in the magnitude of SET and RESET voltages, consistent with experimental observations. The RF-induced SET/RESET voltage modulation was attributed to redox reactions that changed the average ion concentration during RF cycles, accelerating filament growth and dissolution.

previous article Gated-anode diodes for RF and microwave rectifiers for WPT applications: a simulation study on DC and RF characteristics

next article Study of the ISO-FDTD algorithm for processing higher-order dielectric function in SF-FDTD

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Park, S., Noh, J., Choo, M.-L., Sheri, A.M., Chang, M., Kim, Y.-B., Kim, C.J., Jeon, M., Lee, B.-G., Lee, B.H., Hwang, H.: Nanoscale RRAM-based synaptic electronics: toward a neuromorphic computing device. Nanotechnology 24, 384009 (2013). https://doi.org/10.1088/0957-4484/24/38/384009CrossRef

Li, Y., Wang, Z., Midya, R., Xia, Q., Yang, J.J.: Review of memristor devices in neuromorphic computing: materials sciences and device challenges. J. Phys. D Appl. Phys. 51, 50 (2018). https://doi.org/10.1088/1361-6463/aade3fCrossRef

Burr, G.W., Shelby, R.M., Sebastian, A., Kim, S., Kim, S., Sidler, S., Virwani, K., Ishii, M., Narayanan, P., Fumarola, A., Sanches, L.L., Boybat, I., Le Gallo, M., Moon, K., Woo, J., Hwang, H., Leblebici, Y.: Neuromorphic computing using non-volatile memory. Adv Phys X 2, 89–124 (2017). https://doi.org/10.1080/23746149.2016.1259585CrossRef

Szandała, T.: Review and comparison of commonly used activation functions for deep neural networks. Bio-inspired neurocomputing. In: Bhoi, A.K., Mallick, P.K., Liu, C.-M., Balas, V.E. (eds.) Bio-inspired neurocomputing, pp. 203–224. Springer, Berlin, Heidelberg (2021)CrossRef

Duan, X., Cao, Z., Gao, K., Yan, W., Sun, S., Zhou, G., Wu, Z., Ren, F., Sun, B.: Memristor-based neuromorphic chips. Adv. Mater. 234, 2310704 (2024). https://doi.org/10.1002/adma.202310704CrossRef

Sung, C., Hwang, H., Yoo, I.K.: Perspective: a review on memristive hardware for neuromorphic computation. J. Appl. Phys. 124, 151903 (2018). https://doi.org/10.1063/1.5037835CrossRef

Islam, R., Li, H., Chen, P.Y., Wan, W., Chen, H.Y., Gao, B., Wu, H., Yu, S., Saraswat, K., Wong, H.S.P.: Device and materials requirements for neuromorphic computing. J. Phys. D Appl. Phys. 52, 113001 (2019). https://doi.org/10.1088/1361-6463/ab1b66CrossRef

Ambrogio, S., Narayanan, P., Tsai, H., Shelby, R.M., Boybat, I., di Nolfo, C., Sidler, S., Giordano, M., Bodini, M., Farinha, N.C.P., Killeen, B., Cheng, C., Jaoudi, Y., Burr, G.W.: Equivalent-accuracy accelerated neural-network training using analogue memory. Nature 558, 60–67 (2018). https://doi.org/10.1038/s41586-018-0180-5CrossRef

Lim, S., Kwak, M., Hwang, H.: Improved synaptic behavior of CBRAM using internal voltage divider for neuromorphic systems. IEEE Trans. Electron Devices 65(9), 3976–3981 (2018). https://doi.org/10.1109/TED.2018.2857494CrossRef

10.

Zhang, B., Zima, V., Mikysek, T., Podzemna, V., Rozsival, P., Wagner, T.: Multilevel resistive switching in Cu and Ag doped CBRAM device. J. Mater. Sci. 29, 16836–16841 (2018). https://doi.org/10.1007/s10854-018-9778-5CrossRef

11.

Keene, S.T., Lubrano, C., Kazemzadeh, S., Melianas, A., Tuchman, Y., Polino, G., Scogmaniglio, P., Cina, L., Salleo, A., van de Burgt, Y., Santoro, F.: A biohybrid synapse with neurotransmitter-mediated plasticity. Nat. Mater. 19, 969–973 (2020). https://doi.org/10.1038/s41563-020-0703-yCrossRef

12.

Hu, W., Jiang, J., Xie, D., Liu, B., Yang, J., He, J.: Proton–electron-coupled MoS2 synaptic transistors with a natural renewable biopolymer neurotransmitter for brain-inspired neuromorphic learning. J. Mater. Chem. C 7, 682–691 (2019). https://doi.org/10.1039/C8TC04740ECrossRef

13.

Tan, H., Majumdar, S., Qin, Q., Lahtinen, J., van Dijken, S.: Mimicking neurotransmitter release and long-term plasticity by oxygen vacancy migration in a tunnel junction memristor. Adv. Intell. Syst. 1, 1900036 (2019). https://doi.org/10.1002/aisy.201900036CrossRef

14.

Kim, D., Lee, J.S.: Neurotransmitter-induced excitatory and inhibitory functions in artificial synapses. Adv. Funct. Mater. 32, 2200497 (2022). https://doi.org/10.1002/adfm.202200497CrossRef

15.

Kuśmierz, Ł, Isomura, T., Toyoizumi, T.: Learning with three factors: modulating Hebbian plasticity with errors. Curr. Opin. Neurobiol. 46, 170–177 (2017). https://doi.org/10.1016/j.conb.2017.08.020CrossRef

16.

Brzosko, Z., Mierau, S.B., Paulsen, O.: Neuromodulation of spike-timing-dependent plasticity: past, present, and future. Neuron 103, 563–581 (2019). https://doi.org/10.1016/j.neuron.2019.05.041CrossRef

17.

Isomura, T., Shimazaki, H., Friston, K.J.: Canonical neural networks perform active inference. Commun. Biol. 5, 55 (2022). https://doi.org/10.1038/s42003-021-02994-2CrossRef

18.

Yin, Y., Uchida, C., Tsukamoto, K., Hayashi, H., Nakaoka, T.: Modulated conductive bridge memory characteristics by radio frequency input and non-volatile switching of frequency multiplication. Electron. Lett. 58, 804–806 (2022). https://doi.org/10.1049/ell2.12601CrossRef

19.

Yin, Y., Tsukamoto, K., Hayashi, H., Nakaoka, T.: RF-wave induced CBRAM characteristic modulation specific to sheet-like conductive filaments. Mater. Res. Express 10, 115201 (2023). https://doi.org/10.1088/2053-1591/ad0dcfCrossRef

20.

López-Soriano, S., Methapettyparambu Purushothama, J., Vena, A., Perret, E.: CBRAM technology: transition from a memory cell to a programmable and non-volatile impedance for new radiofrequency applications. Sci. Rep. 12, 4105 (2022). https://doi.org/10.1038/s41598-022-08127-xCrossRef

21.

Wainstein, N., Adam, G., Yalon, E., Kvatinsky, S.: Radiofrequency switches based on emerging resistive memory technologies—a survey. Proc. IEEE 109, 77–95 (2021). https://doi.org/10.1109/JPROC.2020.3011953CrossRef

22.

Singh, T., Khaira, N.K., Repeta, M., Mansour, R.R.: Phase-change RF devices for future communications: phase-change materials and devices for reconfigurable RF front-ends: state-of-the-art and future perspectives. IEEE Microw. Mag. 25, 18–38 (2024). https://doi.org/10.1109/MMM.2023.3332322CrossRef

23.

Kim, D., Yang, S.J., Wainstein, N., Skrzypczak, S., Ducournau, G., Pallecchi, E., Happy, H., Yalon, E., Kim, M., Akinwande, D.: Emerging memory electronics for non-volatile radiofrequency switching technologies. Nat. Rev. Electr. Eng. 1, 10–23 (2024). https://doi.org/10.1038/s44287-023-00001-wCrossRef

24.

Menzel, S., Kaupmann, P., Waser, R.: Understanding filamentary growth in electrochemical metallization memory cells using kinetic Monte Carlo simulations. Nanoscale 7, 12673 (2015). https://doi.org/10.1039/C5NR02258DCrossRef

25.

Qin, S., Liu, Z., Zhang, G., Zhang, J., Sun, Y., Wu, H., et al.: Atomistic study of dynamics for metallic filament growth in conductive-bridge random access memory. Phys. Chem. Chem. Phys. 17, 8627–8632 (2015). https://doi.org/10.1039/C4CP04903ACrossRef

26.

Menzel, S.: Comprehensive modeling of electrochemical metallization memory cells. J. Comput. Electron. 16, 1017 (2017). https://doi.org/10.1007/s10825-017-1051-2CrossRef

27.

Buttberg, M., Valov, I., Menzel, S.: Simulating the filament morphology in electrochemical metallization cells. Neuromorph. Comput. Eng. 3, 024010 (2023). https://doi.org/10.1088/2634-4386/acdbe5CrossRef

28.

Dorion P, Cueto O, Reyboz M, Vianello E, Barbé JC, Grigoriu A, Maday Y (2013) Simulation of CBRAM devices with the level set method. In: International Conference on Simulation of Semiconductor Processes and Devices (SISPAD) (IEEE), pp. 340–343. https://doi.org/10.1109/SISPAD.2013.6650644

29.

Dorion P, Cueto O, Reyboz M, Barbé JC, Grigoriu A, Maday Y (2014) Advanced simulation of CBRAM devices with the level set method. In: International Conference on Simulation of Semiconductor Processes and Devices (SISPAD) (IEEE), pp. 33–36. https://doi.org/10.1109/SISPAD.2014.6931556

30.

Zhao, J., Chen, Q., Zhao, X., Yang, G., Ma, G., Wang, H.: Self-compliance and high-performance GeTe-based CBRAM with Cu electrode. Microelectron. J. 131, 105649 (2023). https://doi.org/10.1016/j.mejo.2022.105649CrossRef

31.

Devulder, W., Opsomer, K., Rampelberg, G., De Schutter, B., Devloo-Casier, K., Jurczak, M., Gouxb, L., Detavernier, C.: Improved thermal stability and retention properties of Cu–Te based CBRAM by Ge alloying. J Mater Chem C 3, 12469–12476 (2015). https://doi.org/10.1039/C5TC02776DCrossRef

32.

COMSOL Multiphysics®v. 6.1. COMSOL AB, Stockholm, Sweden. https://www.comsol.com

33.

Chen, C., Jost, P., Volker, H., Kaminski, M., Wirtssohn, M., Engelmann, U., Krüger, K., Schlich, F., Schlockermann, C., Lobo, R.P.S.M., Wuttig, M.: Dielectric properties of amorphous phase-change materials. Phys. Rev. B 95, 094111 (2017). https://doi.org/10.1103/PhysRevB.95.094111CrossRef

34.

Taibl, S., Fafilek, G., Fleig, J.: Impedance spectra of Fe-doped SrTiO₃ thin films upon bias voltage: inductive loops as a trace of ion motion. Nanoscale 8, 13954–13966 (2016). https://doi.org/10.1039/C6NR00814CCrossRef

35.

Ghosh, K., Kusiak, A., Noé, P., Cyrille, M.-C., Battaglia, J.-L.: Thermal conductivity of amorphous and crystalline GeTe thin film at high temperature: experimental and theoretical study. Phys. Rev. B 101, 214305 (2020). https://doi.org/10.1103/PhysRevB.101.214305CrossRef

36.

Warzoha, R.J., Donovan, B.F., Vu, N.T., Champlain, J.G., Mack, S., Ruppalt, L.B.: Nanoscale thermal transport in amorphous and crystalline GeTe thin-films. Appl. Phys. Lett. 115, 023104 (2019). https://doi.org/10.1063/1.5098334CrossRef

37.

Imanishi, Y., Nakaoka, T.: Direct measurement of “ready-made” cations in a Ge₂Sb_3.4Te_6.2 film. J. Mater. Sci. 54, 7072–7077 (2019). https://doi.org/10.1007/s10853-019-03405-yCrossRef

38.

Park, H., Watanabe, T., Yoda, I., Shohmitsu, Y., Kawasaki, S., Nakaoka, T.: Reversible and irreversible resistance changes for gamma-ray irradiation in silver-diffused germanium telluride. SN Appli Sci 2, 1–11 (2020). https://doi.org/10.1007/s42452-020-03927-0CrossRef

39.

Imanishi, Y., Kida, S., Nakaoka, T.: Direct observation of Ag filament growth and unconventional SET-RESET operation in GeTe amorphous films. AIP Adv. 6, 075003 (2016). https://doi.org/10.1063/1.4958633CrossRef

Title: Simulations of RF wave-induced modulation of filament growth and bipolar resistive switching in conductive bridging RAM
Authors: Yifei Yin
Toshihiro Nakaoka
Publication date: 10-09-2024
Publisher: Springer US
Published in: Journal of Computational Electronics / Issue 6/2024
Print ISSN: 1569-8025
Electronic ISSN: 1572-8137
DOI: https://doi.org/10.1007/s10825-024-02228-8

Springer Professional

Simulations of RF wave-induced modulation of filament growth and bipolar resistive switching in conductive bridging RAM

Abstract

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 6/2024

Study of the ISO-FDTD algorithm for processing higher-order dielectric function in SF-FDTD

Chaotic computing cell based on nanostructured phase-change materials

Conductance characteristics of naphthopyran as a light-sensitive molecular optical junction: a joint NEGF-DFT and TD-DFT study

A nanoelectromechanical energy-reversible switch: theoretical study and verification by experiment of its applicability to adiabatic computing

Modeling to study the shape, dimensionality and crystal structure dependence of energy band gap in nanosized semiconductors

On efficient modeling of drain current for designing high-power GaN HEMT-based circuits

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Study of the ISO-FDTD algorithm for processing higher-order dielectric function in SF-FDTD

Chaotic computing cell based on nanostructured phase-change materials

Conductance characteristics of naphthopyran as a light-sensitive molecular optical junction: a joint NEGF-DFT and TD-DFT study

A nanoelectromechanical energy-reversible switch: theoretical study and verification by experiment of its applicability to adiabatic computing

Modeling to study the shape, dimensionality and crystal structure dependence of energy band gap in nanosized semiconductors

On efficient modeling of drain current for designing high-power GaN HEMT-based circuits