journal article·2023-05-26Open access

Implementation of threshold- and memory-switching memristors based on electrochemical metallization in an identical ferroelectric electrolyte

Chansoo Yoon, Gwangtaek Oh, Sohwi Kim, Jihoon Jeon, Ji Hye Lee, Young Heon Kim, Bae Ho Park

NPG Asia Materials· Volume 15· Issue 1

Abstract

Abstract The use of an identical electrolyte in electrochemical metallization (ECM)-based neuron and synaptic devices has not yet been achieved due to their different resistive-switching characteristics. Herein, we describe ECM devices comprising the same ferroelectric PbZr0.52Ti0.48O3 (PZT) electrolyte, which can sustain both neuron and synaptic behavior depending on the identity of the active electrode. The Ag/PZT/La0.8Sr0.2MnO3 (LSMO) threshold switching memristor shows abrupt and volatile resistive switching characteristics, which lead to neuron devices with stochastic integration-and-fire behavior, auto-recovery, and rapid operation. In contrast, the Ni/PZT/LSMO memory switching memristor exhibits gradual, non-volatile resistive switching behavior, which leads to synaptic devices with a high on/off ratio, low on-state current, low variability, and spike-timing-dependent plasticity (STDP). The divergent behavior of the ECM devices is attributed to greater control of cation migration through the ultrathin ferroelectric PZT. Thus, ECM devices with an identical ferroelectric electrolyte offer promise as essential building blocks in the construction of high-performance neuromorphic computing systems.
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Research context

OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Transition Metal Oxide Nanomaterials

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References (52)

Show source-provided references
1. Tuma, T., Pantazi, A., Gallo, L. M., Sebastian, A. & Eleftheriou, E. Stochastic phase-change neurons. Nat. Nanotechnol. 11, 693 (2016).10.1038/nnano.2016.70 ↗
2. Wang, Z. et al. Fully memristive neural networks for pattern classification with unsupervised learning. Nat. Electron. 11, 137–145 (2018).10.1038/s41928-018-0023-2 ↗
3. Zhang, X. et al. An artificial neuron based on a threshold switching memristor. IEEE Electron Device Lett. 39, 308–311 (2018).10.1109/LED.2017.2782752 ↗
4. Wang, Z. et al. Capacitive neural network with neuro-transistors. Nat. Commun. 9, 3208 (2018).
5. Zhang, X. et al. An artificial spiking afferent nerve based on Mott memristors for neurorobotics. Nat. Commun. 11, 51 (2020).
6. Pickett, M. D., Medeiros-Ribeiro, G. & Williams, R. S. A scalable neuristor built with Mott memristors. Nat. Mater. 12, 114–117 (2013).10.1038/nmat3510 ↗
7. Stoliar, P. et al. A leaky-integrate-and-fire neuron analog realized with a Mott insulator. Adv. Func. Mater. 27, 1604740 (2017).10.1002/adfm.201604740 ↗
8. Chen, P.-Y. et al. Mitigating effects of non-ideal synaptic device characteristics for on-chip learning. IEEE/ACM Int. Conf. Comput.-Aided Des. 194–199 (2015).10.1109/ICCAD.2015.7372570 ↗
9. Kim, S. et al. Pattern recognition using carbon nanotube synaptic transistors with an adjustable weight update protocol. ACS Nano 11, 2814–2822 (2017).10.1021/acsnano.6b07894 ↗
10. Choi, S. et al. SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations. Nat. Mater. 17, 335–340 (2018).10.1038/s41563-017-0001-5 ↗
11. Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 61 (2015).10.1038/nature14441 ↗
12. Fuller, E. J. et al. Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing. Science 364, 570–574 (2019).10.1126/science.aaw5581 ↗
13. Yeon, H. et al. Alloying conducting channels for reliable neuromorphic computing. Nat. Nanotechnol. 15, 574–579 (2020).10.1038/s41565-020-0694-5 ↗
14. Yoon, C. et al. Synaptic plasticity selectively activated by polarization-dependent energy-efficient ion migration in an ultrathin ferroelectric tunnel junction. Nano Lett. 17, 1949–1955 (2017).10.1021/acs.nanolett.6b05308 ↗
15. Wang, Z. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 16, 101–108 (2017).10.1038/nmat4756 ↗
16. Gao, B. et al. Ultra-low-energy three-dimensional oxide-Based electronic synapses for implementation of robust high-Accuracy neuromorphic computation systems. ACS Nano 8, 6998–7004 (2014).10.1021/nn501824r ↗
17. Yu, S. et al. A Low energy oxide-based electronic synaptic device for neuromorphic visual systems with tolerance to device variation. Adv. Matter 25, 1774–1779 (2013).10.1002/adma.201203680 ↗
18. van de Burgt, Y. et al. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 16, 414–418 (2017).10.1038/nmat4856 ↗
19. Sharbati, M. T. et al. Low-power, electrochemically tunable graphene synapses for neuromorphic computing. Adv. Mater. 30, 1802353 (2018).10.1002/adma.201802353 ↗
20. Yang, C.-S. et al. All-solid-state synaptic transistor with ultralow conductance for neuromorphic computing. Adv. Func. Mater. 28, 1804170 (2018).10.1002/adfm.201804170 ↗
21. Li, J. et al. Reproducible ultrathin ferroelectric domain switching for high-performance neuromorphic computing. Adv. Mater. 32, 1905764 (2020).10.1002/adma.201905764 ↗
22. Chanthbouala, A. et al. A ferroelectric memristor. Nat. Mater. 11, 860–864 (2012).10.1038/nmat3415 ↗
23. Bao, L. et al. Dual-gated MoS2 neuristor for neuromorphic computing. ACS Appl. Mater. Interf.11, 41482–41489 (2019).10.1021/acsami.9b10072 ↗
24. Izyumskaya, N. et al. Processing, structure, properties, and applications of PZT thin films. Crit. Rev. Solid State 32, 111–202 (2007).10.1080/10408430701707347 ↗
25. Chen, P.-Y., Peng, X. & Yu, S. NeuroSim+: An integrated device-to-algorithm framework for benchmarking synaptic devices and array architectures. IEDM Tech. Dig. 6.1.1–6.1.4 (2017).
26. Sun, W. et al. Understanding memristive switching via in situ characterization and device modeling. Nat. Commun. 10, 3453 (2019).
27. Cha, J.-H. et al. Conductive-bridging random-access memories for emerging neuromorphic computing. Nanoscale 12, 14339 (2020).10.1039/D0NR01671C ↗
28. Goux, L. & Valov, I. Electrochemical processes and device improvement in conductive bridge RAM cells. Phys. Status Solidi A. 213, 274–288 (2016).10.1002/pssa.201532813 ↗
29. Valov, I., Waser, R., Jameson, J. R. & Kozicki, M. N. Electrochemical metallization memories-fundamentals, applications, prospects. Nanotechnology 22, 254003 (2011).10.1088/0957-4484/22/25/254003 ↗
30. Valov, I. Interfacial interactions and their impact on redox-based resistive switching memories (ReRAMs). Semicond. Sci. Technol. 32, 093006 (2017).10.1088/1361-6641/aa78cd ↗
31. Lee, J. & Lu, W. D. On-demand reconfiguration of nanomaterials: when electronics meets ionics. Adv. Mater. 30, 1702770 (2018).10.1002/adma.201702770 ↗
32. Valov, I. & Lu, W. D. Nanoscale electrochemistry using dielectric thin film as solid electrolytes. Nanoscale 8, 13828 (2016).10.1039/C6NR01383J ↗
33. Yuan, F. et al. Real-time observation of the electrode-size-dependent evolution dynamic of the conducting filaments in a SiO2 layer. ACS Nano. 11, 4097–4104 (2017).10.1021/acsnano.7b00783 ↗
34. Liu, Q. et al. Real-time observation on dynamic growth/dissolution of conductive filaments in oxide-electrolyte-based ReRAM. Adv. Mater. 24, 1844–1849 (2012).10.1002/adma.201104104 ↗
35. Yang, Y. et al. Electrochemical dynamics of nanoscale metallic inclusions in dielectrics. Nat. Commun. 5, 4232 (2014).
36. Waser, R., Dittmann, R., Staikov, G. & Szot, K. Redox-based resistive switching memories-nanoionic mechanisms, prospects, and challenges. Adv. Mater. 21, 2632–2663 (2009).10.1002/adma.200900375 ↗
37. Valov, I. & Yang, Y. Memristors with alloyed electrodes. Nat. Nanotechnol. 15, 510–516 (2020).10.1038/s41565-020-0702-9 ↗
38. Menzel, S., Böttger, U., Wimmer, M. & Salinga, M. Physics of the switching kinetics in resistive memories. Adv. Funct. Mater. 25, 6306–6325 (2015).10.1002/adfm.201500825 ↗
39. Nestler, T. et al. Separators and electrolytes for rechargeable batteries: Fundamentals and perspectives. Phys. Sci. Rev. 4, 20170115 (2018).
40. Li, H. et al. Fast Diffusion of multivalent ions facilitated by concerted interactions in dual-ion battery systems. Adv. Energy Mater. 8, 1801475 (2018).10.1002/aenm.201801475 ↗
41. Lin, K.-L. et al. Electrode dependence of filament formation in HfO2 resistive-switching memory. J. Appl. Phys. 109, 084104 (2011).10.1063/1.3567915 ↗
42. Kim, S., Jung, S., Kim, M.-H., Cho, S. & Park, B.-G. Resistive switching characteristics of silicon nitride-based RRAM depending on top electrode metals. IEICE Trans. Electron. E98.C, 429 (2015).10.1587/transele.E98.C.429 ↗
43. Tsuruoka, T., Hasegawa, T., Terabe, K. & Aono, M. Operating mechanism and resistive switching characteristics of two-and three-terminal atomic switches using a thin metal oxide layer. J. Electroceram. 39, 143–156 (2017).10.1007/s10832-016-0063-9 ↗
44. Hughes, M. A. et al. High speed chalcogenide glass electrochemical metallization cells with various active metals. Nanotechnology 29, 315202 (2018).10.1088/1361-6528/aac483 ↗
45. Tsuruoka, T., Terabe, K., Hasegawa, T. & Aono, M. Temperature effects on the switching kinetics of a Cu-Ta2O5-based atomic switch. Nanotechnology 22, 254013 (2011).10.1088/0957-4484/22/25/254013 ↗
46. van den Hurk, J. et al. Physical origins and suppression of Ag dissolution in GeSx-based ECM cells. Phys. Chem. Chem. Phys. 16, 18217–18225 (2014).10.1039/C4CP01759E ↗
47. Zhang, Y. et al. Highly compact artificial memristive neuron with low energy consumption. Small 14, 1802188 (2018).10.1002/smll.201802188 ↗
48. Yu, S., et al Scaling-up resistive synaptic arrays for neuro-inspired architecture: challenges and prospect. In 2015 IEEE International Electron Devices Meeting 17.3 (IEEE, 2015).10.1109/IEDM.2015.7409718 ↗
49. Burr, G. W. et al. Neuromorphic computing using non-volatile memory. Adv. Phys. 2, 89–124 (2017).
50. Xu, R. et al. Vertical MoS2 double-layer memristor with electrochemical metallization as an atomic-scale synapse with switching thresholds approaching 100 mV. Nano Lett. 19, 2411–2417 (2019).10.1021/acs.nanolett.8b05140 ↗
51. Jang, B. C. et al. Polymer analog memristive synapse with atomic-scale conductive filament for flexible neuromorphic computing system. Nano Lett. 19, 839–849 (2019).10.1021/acs.nanolett.8b04023 ↗
52. Boyn, S. et al. Learning through ferroelectric domain dynamics in solid-state synapses. Nat. Commun. 8, 14736 (2017).10.1038/ncomms14736 ↗