An Organic Flexible Artificial Bio-Synapses with Long-Term Plasticity for Neuromorphic Computing
Abstract
:1. Introduction
2. Materials and Methods
3. Results and Discussion
4. Conclusions
Supplementary Materials
Author Contributions
Acknowledgments
Conflicts of Interest
References
- Jeong, D.S.; Kim, K.M.; Kim, S.; Choi, B.J.; Hwang, C.S. Neuromorphic Computing: Memristors for Energy—Efficient New Computing Paradigms (Adv. Electron. Mater. 9/2016). Adv. Electron. Mater. 2016, 2. [Google Scholar] [CrossRef]
- Yan, X.; Zhao, J.; Liu, S.; Zhou, Z.; Liu, Q.; Chen, J.; Liu, X.Y. Memristor with Ag-Cluster-Doped TiO2 Films as Artificial Synapse for Neuroinspired Computing. Adv. Funct. Mater. 2018, 28, 1705320. [Google Scholar] [CrossRef]
- Strukov, D.B. Nanotechnology: Smart connections. Nature 2011, 476, 403–405. [Google Scholar] [CrossRef] [PubMed]
- Milo, V.; Ielmini, D.; Chicca, E. In Attractor Networks and Associative Memories with STDP Learning in RRAM Synapses. In Proceedings of the IEEE International Electron Devices Meeting, San Francisco, CA, USA, 2–6 December 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 11–12. [Google Scholar]
- 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] [PubMed]
- Boyn, S.; Grollier, J.; Lecerf, G.; Xu, B.; Locatelli, N.; Fusil, S.; Girod, S.; Carrétéro, C.; Garcia, K.; Xavier, S. Learning through ferroelectric domain dynamics in solid-state synapses. Nat. Commun. 2017, 8, 14736. [Google Scholar] [CrossRef] [PubMed]
- 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. Dev. 2016, 63, 1508–1515. [Google Scholar] [CrossRef]
- Ren, K.; Li, R.; Chen, X.; Wang, Y.; Shen, J.; Xia, M.; Lv, S.; Ji, Z.; Song, Z. Controllable SET process in O-Ti-Sb-Te based phase change memory for synaptic application. Appl. Phys. Lett. 2018, 112, 73106. [Google Scholar] [CrossRef]
- Park, J.; Kim, J.; Kim, S.Y.; Cheong, W.H.; Jang, J.; Park, Y.G.; Na, K.; Kim, Y.T.; Heo, J.H.; Chang, Y.L. Soft, smart contact lenses with integrations of wireless circuits, glucose sensors, and displays. Sci. Adv. 2018, 4, 9841. [Google Scholar] [CrossRef] [PubMed]
- Shang, J.; Xue, W.; Ji, Z.; Liu, G.; Niu, X.; Yi, X.; Pan, L.; Zhan, Q.; Xu, X.H.; Li, R.W. Highly flexible resistive switching memory based on amorphous-nanocrystalline hafnium oxide films. Nanoscale 2017, 9, 7037–7046. [Google Scholar] [CrossRef] [PubMed]
- Park, Y.; Lee, J.S. Artificial Synapses with Short- and Long-Term Memory for Spiking Neural Networks Based on Renewable Materials. ACS Nano 2017, 11, 8962–8969. [Google Scholar] [CrossRef] [PubMed]
- Khiat, A.; Cortese, S.; Serb, A.; Prodromakis, T. Resistive switching of Pt/TiOx/Pt devices fabricated on flexible Parylene-C substrates. Nanotechnology 2017, 28, 25303. [Google Scholar] [CrossRef] [PubMed]
- Brivio, S.; Frascaroli, J.; Spiga, S. Role of Al doping in the filament disruption in HfO2 resistance switches. Nanotechnology 2017, 28, 395202. [Google Scholar] [CrossRef] [PubMed]
- Simanjuntak, F.; Chandrasekaran, S.; Pattanayak, B.; Lin, C.C.; Tseng, T.Y. Peroxide Induced Volatile and Non-volatile Switching Behavior in ZnO-based Electrochemical Metallization Memory Cell. Nanotechnology 2017, 28. [Google Scholar] [CrossRef] [PubMed]
- Lin, Y.D.; Chen, P.S.; Lee, H.Y.; Chen, Y.S.; Rahaman, S.Z.; Tsai, K.H.; Hsu, C.H.; Chen, W.S.; Wang, P.H.; King, Y.C. Retention Model of TaO/HfOx and TaO/AlOx RRAM with Self-Rectifying Switch Characteristics. Nanoscale Res. Lett 2017, 12, 407. [Google Scholar] [CrossRef] [PubMed]
- Zhang, K.; Sun, K.; Wang, F.; Han, Y.; Jiang, Z.; Zhao, J.; Wang, B.; Zhang, H.; Jian, X.; Wong, H.S.P. Ultra-Low Power Ni/HfO2/TiOx/TiN Resistive Random Access Memory With Sub-30-nA Reset Current. IEEE Electr. Dev. Lett. 2015, 36, 1018–1020. [Google Scholar] [CrossRef]
- Chen, Z.; Zhang, F.; Chen, B.; Zheng, Y.; Gao, B.; Liu, L.; Liu, X.; Kang, J. High-performance HfOx/AlOy—Based resistive switching memory cross-point array fabricated by atomic layer deposition. Nanoscale Res. Lett. 2015, 10, 70. [Google Scholar] [CrossRef] [PubMed]
- Kong, L.A.; Sun, J.; Qian, C.; Fu, Y.; Wang, J.; Yang, J.; Gao, Y. Long-term synaptic plasticity simulated in ionic liquid/polymer hybrid electrolyte gated organic transistors. ORG Electron. 2017, 47, 126–132. [Google Scholar] [CrossRef]
- Van, D.B.Y.; Lubberman, E.; Fuller, E.J.; Keene, S.T.; Faria, G.C.; Agarwal, S.; Marinella, M.J.; Alec, T.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]
- Wang, Y.; Zhu, C.; Pfattner, R.; Yan, H.; Jin, L.; Chen, S.; Molinalopez, F.; Lissel, F.; Liu, J.; Rabiah, N.I. A highly stretchable, transparent, and conductive polymer. Sci. Adv. 2017, 3, e1602076. [Google Scholar] [CrossRef] [PubMed]
- Yu, J.C.; Jang, J.I.; Bo, R.L.; Lee, G.W.; Han, J.T.; Song, M.H. Highly Efficient Polymer-Based Optoelectronic Devices Using PEDOT:PSS and a GO Composite Layer as a Hole Transport Layer. ACS Appl. Mater. Interfaces 2014, 6, 2067–2073. [Google Scholar] [CrossRef] [PubMed]
- Wang, Z.; Zeng, F.; Yang, J.; Chen, C.; Pan, F. Resistive switching induced by metallic filaments formation through poly(3,4-ethylene-dioxythiophene): poly(styrenesulfonate). ACS Appl. Mater. Interfaces 2012, 4, 447–453. [Google Scholar] [CrossRef] [PubMed]
- Zeng, F.; Li, S.; Yang, J.; Pan, F.; Guo, D. Learning processes modulated by the interface effects in a Ti/conducting polymer/Ti resistive switching cell. RSC Adv. 2014, 4, 14822–14828. [Google Scholar] [CrossRef]
- Choi, H.Y.; Wu, C.; Chang, H.B.; Kim, T.W. Organic electronic synapses with pinched hystereses based on graphene quantum-dot nanocomposites. NPG Asia Mater. 2017, 9, e413. [Google Scholar] [CrossRef]
- Chia, P.; Chua, L.; Sivaramakrishnan, S.; Zhuo, J.; Zhao, L.; Sim, W.; Yeo, Y.; Ho, P.K. Injection-induced De-doping in a Conducting Polymer during Device Operation: Asymmetry in the Hole Injection and Extraction Rates. Adv. Mater. 2007, 19, 4202–4207. [Google Scholar] [CrossRef]
- Lipomi, D.J.; Bao, Z. Stretchable and ultraflexible organic electronics. MRS Bull. 2017, 42, 93–97. [Google Scholar] [CrossRef]
- An, B.W.; Shin, J.H.; Kim, S.Y.; Kim, J.; Ji, S.; Park, J.; Lee, Y.; Jang, J.; Park, Y.G.; Cho, E. Smart Sensor Systems for Wearable Electronic Devices. Polymers (Basel) 2017, 9, 303. [Google Scholar] [CrossRef]
- Kim, S.; Du, C.; Sheridan, P.; Ma, W.; Choi, S.; Lu, W.D. Experimental Demonstration of a Second-Order Memristor and Its Ability to Biorealistically Implement Synaptic Plasticity. Nano Lett. 2015, 15, 2203–2211. [Google Scholar] [CrossRef] [PubMed]
- Liu, X.; Ji, Z.; Tu, D.; Shang, L.; Liu, J.; Liu, M.; Xie, C. Organic nonpolar nonvolatile resistive switching in poly(3,4-ethylene-dioxythiophene): Polystyrenesulfonate thin film. ORG Electron. 2009, 10, 1191–1194. [Google Scholar] [CrossRef]
- Yu, S. Neuro-Inspired Computing With Emerging Nonvolatile Memory. Proc. IEEE 2018, 106, 260–285. [Google Scholar] [CrossRef]
- Ohno, T.; Hasegawa, T.; Tsuruoka, T.; Terabe, K.; Gimzewski, J.K.; Aono, M. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 2011, 10, 591–595. [Google Scholar] [CrossRef] [PubMed]
© 2018 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
Wang, T.-Y.; He, Z.-Y.; Chen, L.; Zhu, H.; Sun, Q.-Q.; Ding, S.-J.; Zhou, P.; Zhang, D.W. An Organic Flexible Artificial Bio-Synapses with Long-Term Plasticity for Neuromorphic Computing. Micromachines 2018, 9, 239. https://doi.org/10.3390/mi9050239
Wang T-Y, He Z-Y, Chen L, Zhu H, Sun Q-Q, Ding S-J, Zhou P, Zhang DW. An Organic Flexible Artificial Bio-Synapses with Long-Term Plasticity for Neuromorphic Computing. Micromachines. 2018; 9(5):239. https://doi.org/10.3390/mi9050239
Chicago/Turabian StyleWang, Tian-Yu, Zhen-Yu He, Lin Chen, Hao Zhu, Qing-Qing Sun, Shi-Jin Ding, Peng Zhou, and David Wei Zhang. 2018. "An Organic Flexible Artificial Bio-Synapses with Long-Term Plasticity for Neuromorphic Computing" Micromachines 9, no. 5: 239. https://doi.org/10.3390/mi9050239
APA StyleWang, T. -Y., He, Z. -Y., Chen, L., Zhu, H., Sun, Q. -Q., Ding, S. -J., Zhou, P., & Zhang, D. W. (2018). An Organic Flexible Artificial Bio-Synapses with Long-Term Plasticity for Neuromorphic Computing. Micromachines, 9(5), 239. https://doi.org/10.3390/mi9050239