The Effect of Growth Parameters on Electrophysical and Memristive Properties of Vanadium Oxide Thin Films
Abstract
:1. Introduction
2. Materials and Methods
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Kriegeskorte, N. Deep neural networks: A new framework for modeling biological vision and brain information processing. Annu. Rev. Vis. Sci. 2015, 1, 417–446. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Spoerer, C.J.; McClure, P.; Kriegeskorte, N. Recurrent convolutional neural networks: A better model of biological object recognition. Front. Psychol. 2017, 8, 1551. [Google Scholar] [CrossRef] [PubMed]
- Cichy, R.M.; Kaiser, D. Deep neural networks as scientific models. Trends Cogn. Sci. 2019, 23, 305–317. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hasson, U.; Nastase, S.A.; Goldstein, A. Direct fit to nature: An evolutionary perspective on biological and artificial neural networks. Neuron 2020, 105, 416–434. [Google Scholar] [CrossRef]
- Bornholdt, S.; Graudenz, D. General asymmetric neural networks and structure design by genetic algorithms. Neural Netw. 1992, 5, 327–334. [Google Scholar] [CrossRef]
- Lu, C.H.; Lin, C.S.; Chao, H.L.; Shen, J.S.; Hsiung, P.A. Reconfigurable multi-core architecture-a plausible solution to the von Neumann performance bottleneck. Int. J. Adapt. Innov. Syst. 2015, 2, 217–231. [Google Scholar] [CrossRef]
- Zanotti, T.; Puglisi, F.M.; Pavan, P. Smart logic-in-memory architecture for low-power non-von neumann computing. IEEE J. Electron. Devices Soc. 2020, 8, 757–764. [Google Scholar] [CrossRef]
- Wright, C.D.; Hosseini, P.; Diosdado, J.A.V. Beyond von-Neumann computing with nanoscale phase-change memory devices. Adv. Funct. Mater. 2013, 23, 2248–2254. [Google Scholar] [CrossRef] [Green Version]
- Tominov, R.V.; Vakulov, Z.E.; Avilov, V.I.; Khakhulin, D.A.; Fedotov, A.A.; Zamburg, E.G.; Ageev, O.A. Synthesis and Memristor Effect of a Forming-Free ZnO Nanocrystalline Films. Nanomaterials 2020, 10, 1007. [Google Scholar] [CrossRef]
- Jain, A.; Srikanth, S.; DeBenedictis, E.P.; Krishna, T. Merge network for a non-von Neumann accumulate accelerator in a 3D chip. In Proceedings of the 2018 IEEE International Conference on Rebooting Computing (ICRC), McLean, VA, USA, 7–9 November 2018; Volume 1, pp. 1–11. [Google Scholar]
- Jo, S.H.; Chang, T.; Ebong, I.; Bhadviya, B.B.; Mazumder, P.; Lu, W. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 2010, 10, 1297–1301. [Google Scholar] [CrossRef]
- Indiveri, G.; Liu, S.C. Memory and information processing in neuromorphic systems. Proc. IEEE 2015, 103, 1379–1397. [Google Scholar] [CrossRef] [Green Version]
- Hu, M.; Li, H.; Chen, Y.; Wu, Q.; Rose, G.S.; Linderman, R.W. Memristor crossbar-based neuromorphic computing system: A case study. IEEE Trans. Neural Netw. Learn. Syst. 2014, 25, 1864–1878. [Google Scholar] [CrossRef] [PubMed]
- Ambrogio, S.; Balatti, S.; Milo, V.; Carboni, R.; Wang, Z.Q.; Calderoni, A.; 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]
- Chu, M.; Kim, B.; Park, S.; Hwang, H.; Jeon, M.; Lee, B.H.; Lee, B.G. Neuromorphic hardware system for visual pattern recognition with memristor array and CMOS neuron. IEEE Trans. Ind. Electron. 2014, 62, 2410–2419. [Google Scholar] [CrossRef]
- Ren, Y.; Milo, V.; Wang, Z.; Xu, H.; Ielmini, D.; Zhao, X.; Liu, Y. Analytical modeling of organic–inorganic CH3NH3PbI3 Perovskite resistive switching and its application for Neuromorphic recognition. Adv. Theory Simul. 2018, 1, 1700035. [Google Scholar] [CrossRef]
- Avilov, V.; Polupanov, N.; Tominov, R.; Solodovnik, M.; Konoplev, B.; Smirnov, V.; Ageev, O. Resistive Switching of GaAs Oxide Nanostructures. Materials 2020, 13, 3451. [Google Scholar] [CrossRef]
- Diehl, P.U.; Pedroni, B.U.; Cassidy, A.; Merolla, P.; Neftci, E.; Zarrella, G. Truehappiness: Neuromorphic emotion recognition on truenorth. In Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada, 24–29 July 2016; Volume 1, pp. 4278–4285. [Google Scholar]
- Sun, S.; Li, J.; Li, Z.; Liu, H.; Li, Q.; Xu, H. Low-consumption neuromorphic memristor architecture based on convolutional neural networks. In Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 8–13 July 2018; Volume 1, pp. 1–6. [Google Scholar]
- Avilov, V.I.; Smirnov, V.A.; Tominov, R.V.; Sharapov, N.A.; Avakyan, A.A.; Polyakova, V.V.; Ageev, O.A. Atomic force microscopy of titanium oxide nanostructures with forming-free resistive switching. IOP Conf. Ser. Mater. Sci. Eng. 2019, 699, 012004. [Google Scholar] [CrossRef]
- Boahen, K.A. Communicating neuronal ensembles between neuromorphic chips. In Neuromorphic Systems Engineering; Springer: Boston, MA, USA, 1998; Volume 1, pp. 229–259. [Google Scholar]
- Smirnov, V.A.; Tominov, R.V.; Avilov, V.I.; Avakyan, A.A.; Ageev, O.A. Forming-free resistive switching in nanocrystalline HfO2 films. IOP Conf. Ser. Mater. Sci. Eng. 2019, 699, 012053. [Google Scholar] [CrossRef]
- Ageev, O.; Konoplev, B. Nanotechnology in Microelectronics, 1st ed.; Nauka Publisher: Moscow, Russia, 2019; p. 511. [Google Scholar]
- Klimin, V.S.; Tominov, R.V.; Avilov, V.I.; Dukhan, D.D.; Rezvan, A.A.; Zamburg, E.G.; Ageev, O.A. Nanoscale profiling and memristor effect of ZnO thin films for RRAM and neuromorphic devices application. Int. Soc. Opt. Photonics 2018, 11022, 110220E. [Google Scholar]
- Mikolajick, T.; Dehm, C.; Hartner, W.; Kasko, I.; Kastner, M.J.; Nagel, N.; Mazure, C.N. FeRAM technology for high density applications. Microelectron. Reliab. 2001, 41, 947–950. [Google Scholar] [CrossRef]
- Suzuki, M. Review on Future Ferroelectric Nonvolatile Memory: FeRAM. J. Ceram. Soc. Jpn. 1995, 103, 1099–1111. [Google Scholar] [CrossRef] [Green Version]
- Engel, B.N.; Akerman, J.; Butcher, B.; Dave, R.W.; DeHerrera, M.; Durlam, M.; Slaughter, J.M. A 4-Mb toggle MRAM based on a novel bit and switching method. IEEE Trans. Magn. 2005, 41, 132–136. [Google Scholar] [CrossRef]
- Huai, Y. Spin-transfer torque MRAM (STT-MRAM): Challenges and prospects. AAPPS Bull. 2008, 18, 33–40. [Google Scholar]
- Choi, Y.; Song, I.; Park, M.H.; Chung, H.; Chang, S.; Cho, B.; Shin, J. A 20 nm 1.8 V 8 Gb PRAM with 40 MB/s program bandwidth. In Proceedings of the 2012 IEEE International Solid-State Circuits Conference, San Francisco, CA, USA, 19–23 February 2012; Volume 1, pp. 46–48. [Google Scholar]
- Demin, V.A.; Surazhevsky, I.A.; Emelyanov, A.V.; Kashkarov, P.K.; Kovalchuk, M.V. Sneak, discharge, and leakage current issues in a high-dimensional 1T1M memristive crossbar. J. Comput. Electron. 2020, 19, 565–575. [Google Scholar] [CrossRef]
- Khakhulin, D.A.; Vakulov, Z.E.; Smirnov, V.A.; Tominov, R.V.; Yoon, J.G.; Ageev, O.A. Resistive switching in ZnO/ZnO: In nanocomposite. J. Phys. Conf. Ser. 2017, 917, 092008. [Google Scholar] [CrossRef]
- Tominov, R.V.; Zamburg, E.G.; Khakhulin, D.A.; Klimin, V.S.; Smirnov, V.A.; Chu, Y.H.; Ageev, O.A. Investigation of resistive switching of ZnxTiyHfzOi nanocomposite for RRAM elements manufacturing. J. Phys. Conf. Ser. 2017, 917, 032023. [Google Scholar] [CrossRef]
- Smirnov, V.A.; Tominov, R.V.; Avilov, V.I.; Alyabieva, N.I.; Vakulov, Z.E.; Zamburg, E.G.; Ageev, O.A. Investigation into the memristor effect in nanocrystalline ZnO films. Semiconductors 2019, 53, 72–77. [Google Scholar] [CrossRef]
- Mikhaylov, A.; Pimashkin, A.; Pigareva, Y.; Gerasimova, S.; Gryaznov, E.; Shchanikov, S.; Erokhin, V. Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics. Front. Neurosci. 2020, 14, 358. [Google Scholar] [CrossRef]
- Shandyba, N.A.; Panchenko, I.V.; Tominov, R.V.; Smirnov, V.A.; Pelipenko, M.I.; Zamburg, E.G.; Chu, Y.H. Size effect on memristive properties of nanocrystalline ZnO film for resistive synaptic devices. J. Phys. Conf. Ser. 2018, 1124, 081036. [Google Scholar] [CrossRef]
- Waser, R.; Dittmann, R.; Staikov, G.; Szot, K. Redox-based resistive switching memories–nanoionic mechanisms, prospects, and challenges. Adv. Mater. 2009, 21, 2632–2663. [Google Scholar] [CrossRef]
- Wei, Z.; Kanzawa, Y.; Arita, K.; Katoh, Y.; Kawai, K.; Muraoka, S.; Mikawa, T. Highly reliable TaOx ReRAM and direct evidence of redox reaction mechanism. In Proceedings of the 2008 IEEE International Electron Devices Meeting, San Francisco, CA, USA, 15–17 December 2008; Volume 1, pp. 1–4. [Google Scholar]
- Kawahara, A.; Azuma, R.; Ikeda, Y.; Kawai, K.; Katoh, Y.; Hayakawa, Y.; Takagi, T. An 8 Mb multi-layered cross-point ReRAM macro with 443 MB/s write throughput. IEEE J. Solid-State Circuits 2012, 48, 178–185. [Google Scholar] [CrossRef]
- Smirnov, V. Nanolithography by local anodic oxidation of thin titanium film. In Piezoelectrics and Nanomaterials: Fundamentals, Developments and Applications, 1st ed.; Parinov, I., Ed.; Nova Science Publisher: Hauppauge, NY, USA, 2015; Volume 1, pp. 85–103. [Google Scholar]
- Kanao, K.; Arie, T.; Akita, S.; Takei, K. An all-solution-processed tactile memory flexible device integrated with a NiO ReRAM. J. Mater. Chem. C 2016, 4, 9261–9265. [Google Scholar] [CrossRef]
- Tominov, R.V.; Smirnov, V.A.; Avilov, V.I.; Fedotov, A.A.; Klimin, V.S.; Chernenko, N.E. Formation of ZnO memristor structures by scratching probe nanolithography. IOP Conf. Ser. Mater. Sci. Eng. 2018, 443, 012036. [Google Scholar] [CrossRef] [Green Version]
- Torre, C.L.; Fleck, K.; Starschich, S.; Linn, E.; Waser, R.; Menzel, S. Dependence of the SET switching variability on the initial state in HfOx-based ReRAM. Phys. Status Solidi 2016, 213, 316–319. [Google Scholar] [CrossRef]
- Kelly, P.J.; Arnell, R.D. Magnetron sputtering: A review of recent developments and applications. Vacuum 2000, 56, 159–172. [Google Scholar] [CrossRef]
- Groner, M.D.; Fabreguette, F.H.; Elam, J.W.; George, S.M. Low-temperature Al2O3 atomic layer deposition. Chem. Mater. 2004, 16, 639–645. [Google Scholar] [CrossRef]
- Yao, B.D.; Chan, Y.F.; Wang, N. Formation of ZnO nanostructures by a simple way of thermal evaporation. Appl. Phys. Lett. 2002, 81, 757–759. [Google Scholar] [CrossRef]
- Li, Y.L.; Kinloch, I.A.; Windle, A.H. Direct spinning of carbon nanotube fibers from chemical vapor deposition synthesis. Science 2004, 304, 276–278. [Google Scholar] [CrossRef]
- Lowndes, D.H.; Geohegan, D.B.; Puretzky, A.A.; Norton, D.P.; Rouleau, C.M. Synthesis of novel thin-film materials by pulsed laser deposition. Science 1996, 273, 898–903. [Google Scholar] [CrossRef] [Green Version]
- Sun, X.W.; Kwok, H.S. Optical properties of epitaxially grown zinc oxide films on sapphire by pulsed laser deposition. J. Appl. Phys. 1999, 86, 408–411. [Google Scholar] [CrossRef]
- Vakulov, Z.E.; Zamburg, E.G.; Khakhulin, D.A.; Ageev, O.A. Thermal stability of ZnO thin films fabricated by pulsed laser deposition. Mater. Sci. Semicond. Process. 2017, 66, 21–25. [Google Scholar] [CrossRef]
- Huotari, J.; Bjorklund, R.; Lappalainen, J.; Spetz, A.L. Pulsed laser deposited nanostructured vanadium oxide thin films characterized as ammonia sensors. Sens. Actuators B Chem. 2015, 217, 22–29. [Google Scholar] [CrossRef]
- Ramana, C.V.; Smith, R.J.; Hussain, O.M.; Julien, C.M. On the growth mechanism of pulsed-laser deposited vanadium oxide thin films. Mater. Sci. Eng. B 2004, 111, 218–225. [Google Scholar] [CrossRef]
- Vakulov, Z.; Zamburg, E.; Khakhulin, D.; Geldash, A.; Golosov, D.A.; Zavadski, S.M.; Ageev, O.A. Oxygen pressure influence on properties of nanocrystalline LiNbO3 films grown by laser ablation. Nanomaterials 2020, 10, 1371. [Google Scholar] [CrossRef]
- Vakulov, Z.E.; Varzarev, Y.N.; Gusev, E.Y.; Skrylev, A.V.; Panich, A.E.; Miakonkikh, A.V.; Ageev, O.A. Influence of Pulsed Laser Deposition Modes on Properties of Nanocrystalline LiNbO3 Films. Russ. Microelectron. 2019, 48, 59–65. [Google Scholar] [CrossRef]
- Cong, G.W.; Wei, H.Y.; Zhang, P.F.; Peng, W.Q.; Wu, J.J.; Liu, X.L.; Wang, Z.G. One-step growth of ZnO from film to vertically well-aligned nanorods and the morphology-dependent Raman scattering. Appl. Phys. Lett. 2005, 87, 231903. [Google Scholar] [CrossRef]
- Chrisey, D.; Hubler, G. Pulsed Laser Deposition of Thin Films; John Willey & Sons: Hoboken, NJ, USA, 1994; p. 613. [Google Scholar]
- Bersuker, G.; Gilmer, D.C.; Veksler, D.; Kirsch, P.; Vandelli, L.; Padovani, A.; Larcher, L.; McKenna, K.; Shluger, A.; Iglesias, V.; et al. Metal oxide resistive memory switching mechanism based on conductive filament properties. J. Appl. Phys. 2011, 110, 124518. [Google Scholar] [CrossRef]
- Chiu, F.C. A review on conduction mechanisms in dielectric films. Adv. Mater. Sci. Eng. 2014, 2014, 578168. [Google Scholar] [CrossRef] [Green Version]
- Wang, Y.L.; Chen, X.K.; Li, M.C.; Wang, R.; Wu, G.; Yang, J.P.; Han, W.H.; Cao, S.Z.; Zhao, L.C. Phase composition and valence of pulsed laser deposited vanadium oxide thin films at different oxygen pressures. Surf. Coat. Technol. 2007, 201, 5344–5347. [Google Scholar] [CrossRef]
- Yang, J.J.; Strukov, D.B.; Stewart, D.R. Memristive devices for computing. Nat. Nanotechnol. 2013, 8, 13. [Google Scholar] [CrossRef]
- Soosen, S.M.; Chandran, A.; Koshy, J.; George, K.C. Correlated barrier hopping in ZnO nanorods. J. Appl. Phys. 2011, 109, 113702. [Google Scholar] [CrossRef]
- Hayat, K.; Rafiq, M.A.; Durrani, S.K.; Hasan, M.M. Impedance spectroscopy and investigation of conduction mechanism in BaMnO3 nanorods. Phys. B Condens. Matter 2011, 406, 309–314. [Google Scholar] [CrossRef]
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Tominov, R.V.; Vakulov, Z.E.; Avilov, V.I.; Khakhulin, D.A.; Polupanov, N.V.; Smirnov, V.A.; Ageev, O.A. The Effect of Growth Parameters on Electrophysical and Memristive Properties of Vanadium Oxide Thin Films. Molecules 2021, 26, 118. https://doi.org/10.3390/molecules26010118
Tominov RV, Vakulov ZE, Avilov VI, Khakhulin DA, Polupanov NV, Smirnov VA, Ageev OA. The Effect of Growth Parameters on Electrophysical and Memristive Properties of Vanadium Oxide Thin Films. Molecules. 2021; 26(1):118. https://doi.org/10.3390/molecules26010118
Chicago/Turabian StyleTominov, Roman V., Zakhar E. Vakulov, Vadim I. Avilov, Daniil A. Khakhulin, Nikita V. Polupanov, Vladimir A. Smirnov, and Oleg A. Ageev. 2021. "The Effect of Growth Parameters on Electrophysical and Memristive Properties of Vanadium Oxide Thin Films" Molecules 26, no. 1: 118. https://doi.org/10.3390/molecules26010118
APA StyleTominov, R. V., Vakulov, Z. E., Avilov, V. I., Khakhulin, D. A., Polupanov, N. V., Smirnov, V. A., & Ageev, O. A. (2021). The Effect of Growth Parameters on Electrophysical and Memristive Properties of Vanadium Oxide Thin Films. Molecules, 26(1), 118. https://doi.org/10.3390/molecules26010118