Electrical Properties and Biological Synaptic Simulation of Ag/MXene/SiO2/Pt RRAM Devices
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. Electrical Characteristics
3.2. Biological Synaptic Simulation
4. Discussion
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
- Xia, Q.; Yang, J.J. Memristive crossbar arrays for brain-inspired computing. Nat. Mater. 2019, 18, 309–323. [Google Scholar] [CrossRef] [PubMed]
- Tang, J.; Yuan, F.; Shen, X.; Wang, Z.; Rao, M.; He, Y.; Sun, Y.; Li, X.; Zhang, W.; Li, Y.; et al. Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges. Adv. Mater. 2019, 31, 1902761. [Google Scholar] [CrossRef] [PubMed]
- Sun, Z.; Ambrosi, E.; Bricalli, A.; Ielmini, D. Logic Computing with Stateful Neural Networks of Resistive Switches. Adv. Mater. 2018, 30, 1802554. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhang, T.; Yang, K.; Xu, X.; Cai, Y.; Yang, Y.; Huang, R. Memristive Devices and Networks for Brain-Inspired Computing. Phys. Status Solidi RRL 2019, 13, 1900029. [Google Scholar] [CrossRef]
- Kuzum, D.; Yu, S.; Philip Wong, H.-S. Synaptic electronics: Materials, devices and applications. Nanotechnology 2013, 24, 382001. [Google Scholar] [CrossRef] [PubMed]
- Wu, Q.; Wang, H.; Luo, Q.; Banerjee, W.; Cao, J.; Zhang, X.; Wu, F.; Liu, Q.; Li, L.; Liu, M. Fully imitation synaptic metaplasticity based on memristor device. Nanoscale 2018, 10, 5875–5881. [Google Scholar] [CrossRef]
- Ambrogio, S.; Balatti, S.; Milo, V.; Carboni, R.; Wang, Z.-Q.; Calderoni, A.; Ramaswamy, N.; Ielmini, D. Neuromorphic Learning and Recognition with One-Transistor-One-Resistor Synapses and Bistable Metal Oxide RRAM. IEEE Trans. Electron Devices 2016, 63, 1508–1515. [Google Scholar] [CrossRef] [Green Version]
- Ielmini, D. Brain-inspired computing with resistive switching memory (RRAM): Devices, synapses and neural networks. Microelectron. Eng. 2018, 190, 44–53. [Google Scholar] [CrossRef]
- Dai, S.; Zhao, Y.; Wang, Y.; Zhang, J.; Fang, L.; Jin, S.; Shao, Y.; Huang, J. Recent Advances in Transistor-Based Artificial Synapses. Adv. Funct. Mater. 2019, 29, 1903700. [Google Scholar] [CrossRef]
- Hur, J.; Jang, B.C.; Park, J.; Moon, D.-I.; Bae, H.; Park, J.-Y.; Kim, G.-H.; Jeon, S.-B.; Seo, M.; Kim, S.; et al. A Recoverable Synapse Device Using a Three-Dimensional Silicon Transistor. Adv. Funct. Mater. 2018, 28, 1804844. [Google Scholar] [CrossRef]
- Kuzum, D.; Jeyasingh, R.G.D.; Lee, B.; Wong, H.-S.P. Nanoelectronic Programmable Synapses Based on Phase Change Materials for Brain-Inspired Computing. Nano Lett. 2012, 12, 2179–2186. [Google Scholar] [CrossRef]
- Burr, G.W.; Shelby, R.M.; Sidler, S.; di Nolfo, C.; Jang, J.; Boybat, I.; Shenoy, R.S.; Narayanan, P.; Virwani, K.; Giacometti, E.U.; et al. Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element. IEEE Trans. Electron Devices 2015, 62, 3498–3507. [Google Scholar] [CrossRef]
- Yang, N.; Ren, Z.-Q.; Hu, C.-Z.; Guan, Z.; Tian, B.-B.; Zhong, N.; Xiang, P.-H.; Duan, C.-G.; Chu, J.-H. Ultra-wide temperature electronic synapses based on self-rectifying ferroelectric memristors. Nanotechnology 2019, 30, 464001. [Google Scholar] [CrossRef]
- Boyn, S.; Grollier, J.; Lecerf, G.; Xu, B.; Locatelli, N.; Fusil, S.; Girod, S.; Carrétéro, C.; Garcia, K.; Xavier, S.; et al. Learning through ferroelectric domain dynamics in solid-state synapses. Nat. Commun. 2017, 8, 14736. [Google Scholar] [CrossRef] [PubMed]
- Wang, Z.; Joshi, S.; Savel’ev, S.E.; Jiang, H.; Midya, R.; Lin, P.; Hu, M.; Ge, N.; Strachan, J.P.; Li, Z.; et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 2017, 16, 101–108. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- van de Burgt, Y.; Lubberman, E.; Fuller, E.J.; Keene, S.T.; Faria, G.C.; Agarwal, S.; Marinella, M.J.; Alec Talin, A.; Salleo, A. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 2017, 16, 414–418. [Google Scholar] [CrossRef]
- Yan, X.; Zhao, Q.; Chen, A.P.; Zhao, J.; Zhou, Z.; Wang, J.; Wang, H.; Zhang, L.; Li, X.; Xiao, Z.; et al. Vacancy-Induced Synaptic Behavior in 2D WS2 Nanosheet–Based Memristor for Low-Power Neuromorphic Computing. Small 2019, 15, 1901423. [Google Scholar] [CrossRef]
- Valentian, A.; Rummens, F.; Vianello, E.; Mesquida, T.; Boissac, L.-M.B.; Bichler, O.; Reita, C. Fully Integrated Spiking Neural Network with Analog Neurons and RRAM Synapses. In Proceedings of the 2019 IEEE International Electron Devices Meeting (IEDM 2019), San Francisco, CA, USA, 7–11 December 2019; pp. 314–317. [Google Scholar]
- Yu, S.; Gao, B.; Fang, Z.; Yu, H.; Kang, J.; Wong, H.-S.P. A Low Energy Oxide-Based Electronic Synaptic Device for Neuromorphic Visual Systems with Tolerance to Device Variation. Adv. Mater. 2013, 25, 1774–1779. [Google Scholar] [CrossRef]
- Whitlock, J.R. Learning Induces Long-Term Potentiation in the Hippocampus. Science 2006, 313, 1093–1097. [Google Scholar] [CrossRef] [Green Version]
- Milo, V.; Zambelli, C.; Olivo, P.; Pérez, E.; Mahadevaiah, M.K.; Ossorio, O.G.; Wenger, C.; Ielmini, D. Multilevel HfO2-based RRAM devices for low-power neuromorphic networks. APL Mater. 2019, 7, 081120. [Google Scholar]
- Lin, C.-Y.; Chen, J.; Chen, P.-H.; Chan, T.-C.; Wu, Y.; Eshraghian, J.K.; Moon, J.; Yoo, S.; Wang, Y.-H.; Chen, W.-C.; et al. Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices. Small 2020, 16, 2003964. [Google Scholar] [CrossRef]
- Prezioso, M.; Merrikh-Bayat, F.; Hoskins, B.D.; Adam, G.C.; Likharev, K.K.; Strukov, D.B. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 2015, 521, 61–64. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Shen, Z.; Zhao, C.; Qi, Y.; Xu, W.; Liu, Y.; Mitrovic, I.Z.; Yang, L.; Zhao, C. Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application. Nanomaterials 2020, 10, 1437. [Google Scholar] [CrossRef] [PubMed]
- Park, J.; Kwak, M.; Moon, K.; Woo, J.; Lee, D.; Hwang, H. TiOx-Based RRAM Synapse With 64-Levels of Conductance and Symmetric Conductance Change by Adopting a Hybrid Pulse Scheme for Neuromorphic Computing. IEEE Electron Device Lett. 2016, 37, 1559–1562. [Google Scholar] [CrossRef]
- Naguib, M.; Kurtoglu, M.; Presser, V.; Lu, J.; Niu, J.; Heon, M.; Hultman, L.; Gogotsi, Y.; Barsoum, M.W. Two-Dimensional Nanocrystals Produced by Exfoliation of Ti3AlC2. Adv. Mater. 2011, 23, 4248–4253. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Naguib, M.; Mashtalir, O.; Carle, J.; Presser, V.; Lu, J.; Hultman, L.; Gogotsi, Y.; Barsoum, M.W. Two-Dimensional Transition Metal Carbides. ACS Nano 2012, 6, 1322–1331. [Google Scholar] [CrossRef] [PubMed]
- Soundiraraju, B.; George, B.K. Two-Dimensional Titanium Nitride (Ti2N) MXene: Synthesis, Characterization, and Potential Application as Surface-Enhanced Raman Scattering Substrate. ACS Nano 2017, 11, 8892–8900. [Google Scholar] [CrossRef]
- Alhabeb, M.; Maleski, K.; Anasori, B.; Lelyukh, P.; Clark, L.; Sin, S.; Gogotsi, Y. Guidelines for Synthesis and Processing of Two-Dimensional Titanium Carbide (Ti3C2Tx MXene). Chem. Mater. 2017, 29, 7633–7644. [Google Scholar] [CrossRef]
- Naguib, M.; Gogotsi, Y. Synthesis of two-dimensional materials by selective extraction. Acc. Chem. Res. 2015, 48, 128–135. [Google Scholar] [CrossRef]
- Shen, L.; Jeon, J.; Jang, S.K.; Xu, J.; Choi, Y.J.; Park, J.-H.; Hwang, E.; Lee, S. Surface group modification and carrier transport property of layered transition metal carbides (Ti2CTx, T: -OH, -F and –O). Nanoscale 2015, 7, 19390–19396. [Google Scholar]
- Naguib, M.; Come, J.; Dyatkin, B.; Presser, V.; Taberna, P.-L.; Simon, P.; Barsoum, M.W.; Gogotsi, Y. MXene: A promising transition metal carbide anode for lithium-ion batteries. Electrochem. Commun. 2012, 16, 61–64. [Google Scholar] [CrossRef] [Green Version]
- Lian, X.; Shen, X.; Zhang, M.; Xu, J.; Gao, F.; Wan, X.; Hu, E.; Guo, Y.; Zhao, J.; Tong, Y. Resistance switching characteristics and mechanisms of MXene/SiO2 structure-based memristor. Appl. Phys. Lett. 2019, 115, 063501. [Google Scholar] [CrossRef]
- Chen, Y.; Wang, Y.; Luo, Y.; Liu, X.; Wang, Y.; Gao, F.; Xu, J.; Hu, E.; Samanta, S.; Wan, X.; et al. Realization of Artificial Neuron Using MXene Bi-Directional Threshold Switching Memristors. IEEE Electron Device Lett. 2019, 40, 1686–1689. [Google Scholar] [CrossRef]
- Gogotsi, Y.; Anasori, B. The Rise of MXenes. ACS Nano 2019, 13, 8491–8494. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lee, J.; Du, C.; Sun, K.; Kioupakis, E.; Lu, W.D. Tuning Ionic Transport in Memristive Devices by Graphene with Engineered Nanopores. ACS Nano 2016, 10, 3571–3579. [Google Scholar] [CrossRef] [PubMed]
- Li, D.; Wu, B.; Zhu, X.; Wang, J.; Ryu, B.; Lu, W.D.; Lu, W.; Liang, X. MoS2 Memristors Exhibiting Variable Switching Characteristics toward Biorealistic Synaptic Emulation. ACS Nano 2018, 12, 9240–9252. [Google Scholar] [CrossRef] [PubMed]
- Wang, M.; Cai, S.; Pan, C.; Wang, C.; Lian, X.; Zhuo, Y.; Xu, K.; Cao, T.; Pan, X.; Wang, B.; et al. Robust memristors based on layered two-dimensional materials. Nat. Electron 2018, 1, 130–136. [Google Scholar] [CrossRef] [Green Version]
- Hui, F.; Villena, M.A.; Fang, W.; Lu, A.-Y.; Kong, J.; Shi, Y.; Jing, X.; Zhu, K.; Lanza, M. Synthesis of large-area multilayer hexagonal boron nitride sheets on iron substrates and its use in resistive switching devices. 2D Mater. 2018, 5, 031011. [Google Scholar] [CrossRef]
- Lian, X.; Shen, X.; Lu, L.; He, N.; Wan, X.; Samanta, S.; Tong, Y. Resistance Switching Statistics and Mechanisms of Pt Dispersed Silicon Oxide-Based Memristors. Micromachines 2019, 10, 369. [Google Scholar] [CrossRef] [Green Version]
- Bricalli, A.; Ambrosi, E.; Laudato, M.; Maestro, M.; Rodriguez, R.; Ielmini, D. Resistive Switching Device Technology Based on Silicon Oxide for Improved ON–OFF Ratio—Part II: Select Devices. IEEE Trans. Electron Devices 2018, 65, 122–128. [Google Scholar] [CrossRef] [Green Version]
- Mehonic, A.; Shluger, A.L.; Gao, D.; Valov, I.; Miranda, E.; Ielmini, D.; Bricalli, A.; Ambrosi, E.; Li, C.; Yang, J.J.; et al. Silicon Oxide (SiOx): A Promising Material for Resistance Switching? Adv. Mater. 2018, 30, 1801187. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Shi, Y.; Pan, C.; Chen, V.; Raghavan, N.; Pey, K.L.; Puglisi, F.M.; Pop, E.; Wong, H.-S.P.; Lanza, M. Coexistence of volatile and non-volatile resistive switching in 2D h-BN based electronic synapses. In Proceedings of the 2017 IEEE International Electron Devices Meeting (IEDM 2017), San Francisco, CA, USA, 2–6 December 2017; pp. 5.4.1–5.4.4. [Google Scholar]
- Zhang, M.; Wang, Y.; Gao, F.; Wang, Y.; Shen, X.; He, N.; Zhu, J.; Chen, Y.; Wan, X.; Lian, X.; et al. Formation of new MXene film using spinning coating method with DMSO solution and its application in advanced memristive device. Ceram. Int. 2019, 45, 19467–19472. [Google Scholar] [CrossRef]
- Wang, K.; Zhou, Y.; Xu, W.; Huang, D.; Wang, Z.; Hong, M. Fabrication and thermal stability of two-dimensional carbide Ti3C2 nanosheets. Ceram. Int. 2016, 42, 8419–8424. [Google Scholar] [CrossRef]
- Feng, A.; Yu, Y.; Wang, Y.; Jiang, F.; Yu, Y.; Mi, L.; Song, L. Two-dimensional MXene Ti3C2 produced by exfoliation of Ti3AlC2. Mater. Des. 2017, 114, 161–166. [Google Scholar] [CrossRef]
- Wang, Z.; Rao, M.; Midya, R.; Joshi, S.; Jiang, H.; Lin, P.; Song, W.; Asapu, S.; Zhuo, Y.; Li, C.; et al. Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications. Adv. Funct. Mater. 2018, 28, 1704862. [Google Scholar] [CrossRef]
- Sun, H.; Liu, Q.; Li, C.; Long, S.; Lv, H.; Bi, C.; Huo, Z.; Li, L.; Liu, M. Direct Observation of Conversion between Threshold Switching and Memory Switching Induced by Conductive Filament Morphology. Adv. Funct. Mater. 2014, 24, 5679–5686. [Google Scholar] [CrossRef]
- Thomson, A.M. Facilitation, augmentation and potentiation at central synapses. Trends Neurosci. 2000, 23, 305–312. [Google Scholar] [CrossRef]
- Kim, M.-K.; Lee, J.-S. Short-Term Plasticity and Long-Term Potentiation in Artificial Biosynapses with Diffusive Dynamics. ACS Nano 2018, 12, 1680–1687. [Google Scholar] [CrossRef]
- Regehr, W.G. Short-Term Presynaptic Plasticity. Cold Spring Harb. Perspect. Biol. 2012, 4, a005702. [Google Scholar] [CrossRef]
- Zucker, R.S.; Regehr, W.G. Short-Term Synaptic Plasticity. Annu. Rev. Physiol. 2002, 64, 355–405. [Google Scholar] [CrossRef] [Green Version]
- Hu, S.G.; Liu, Y.; Chen, T.P.; Liu, Z.; Yu, Q.; Deng, L.J.; Yin, Y.; Hosaka, S. Emulating the paired-pulse facilitation of a biological synapse with a NiOx-based memristor. Appl. Phys. Lett. 2013, 102, 183510. [Google Scholar] [CrossRef]
- Toyoda, H.; Zhao, M.-G.; Xu, H.; Wu, L.-J.; Ren, M.; Zhuo, M. Requirement of Extracellular Signal-Regulated Kinase/Mitogen-Activated Protein Kinase for Long-Term Potentiation in Adult Mouse Anterior Cingulate Cortex. Mol. Pain 2007, 3, 1744–8069. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hirano, T. Regulation and Interaction of Multiple Types of Synaptic Plasticity in a Purkinje Neuron and Their Contribution to Motor Learning. Cerebellum 2018, 17, 756–765. [Google Scholar] [CrossRef] [PubMed]
- Citri, A.; Malenka, R.C. Synaptic Plasticity: Multiple Forms, Functions, and Mechanisms. Neuropsychopharmacology 2008, 33, 18–41. [Google Scholar] [CrossRef] [Green Version]
- Hsiung, C.-P.; Liao, H.-W.; Gan, J.-Y.; Wu, T.-B.; Hwang, J.-C.; Chen, F.; Tsai, M.-J. Formation and Instability of Silver Nanofilament in Ag-Based Programmable Metallization Cells. ACS Nano 2010, 4, 5414–5420. [Google Scholar] [CrossRef]
- Wang, W.; Wang, M.; Ambrosi, E.; Bricalli, A.; Laudato, M.; Sun, Z.; Chen, X.; Ielmini, D. Surface diffusion-limited lifetime of silver and copper nanofilaments in resistive switching devices. Nat. Commun. 2019, 10, 81. [Google Scholar] [CrossRef] [Green Version]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Lian, X.; Shen, X.; Fu, J.; Gao, Z.; Wan, X.; Liu, X.; Hu, E.; Xu, J.; Tong, Y. Electrical Properties and Biological Synaptic Simulation of Ag/MXene/SiO2/Pt RRAM Devices. Electronics 2020, 9, 2098. https://doi.org/10.3390/electronics9122098
Lian X, Shen X, Fu J, Gao Z, Wan X, Liu X, Hu E, Xu J, Tong Y. Electrical Properties and Biological Synaptic Simulation of Ag/MXene/SiO2/Pt RRAM Devices. Electronics. 2020; 9(12):2098. https://doi.org/10.3390/electronics9122098
Chicago/Turabian StyleLian, Xiaojuan, Xinyi Shen, Jinke Fu, Zhixuan Gao, Xiang Wan, Xiaoyan Liu, Ertao Hu, Jianguang Xu, and Yi Tong. 2020. "Electrical Properties and Biological Synaptic Simulation of Ag/MXene/SiO2/Pt RRAM Devices" Electronics 9, no. 12: 2098. https://doi.org/10.3390/electronics9122098
APA StyleLian, X., Shen, X., Fu, J., Gao, Z., Wan, X., Liu, X., Hu, E., Xu, J., & Tong, Y. (2020). Electrical Properties and Biological Synaptic Simulation of Ag/MXene/SiO2/Pt RRAM Devices. Electronics, 9(12), 2098. https://doi.org/10.3390/electronics9122098