Increase of switching range of resistive memristor for realization of a greater number of synaptic states in a neuroprocessor

Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy


Release:

2019, Vol. 5. №2

Title: 
Increase of switching range of resistive memristor for realization of a greater number of synaptic states in a neuroprocessor


For citation: Bobylev A. N., Udovichenko S. Yu., Busygin A. N., Ebrahim A. H. 2019. “Increase of switching range of resistive memristor for realization of a greater number of synaptic states in a neuroprocessor”. Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, vol. 5, no 2, pp. 124-136. DOI: 10.21684/2411-7978-2019-5-2-124-136

About the authors:

Andrey N. Bobylev, Head of the Laboratory of Electronic and Probe Microscopy. REC “Nanotechnology”, University of Tyumen; eLibrary AuthorID, ScopusID, andreaubobylev@gmail.com; ORCID: 0000-0001-5488-8736

Sergey Yu. Udovichenko, Dr. Sci. (Phys.-Math.), Professor of Department of Applied and Technical Physics; Head of REC “Nanotechnology”, University of Tyumen; eLibrary AuthorID, ResearcherID, ScopusID, udotgu@mail.ru

Alexander N. Busygin, Postgraduate Student, Department of Applied and Technical Physics, Reseacher Laboratory Assistant, REC “Nanotechnology”, University of Tyumen; eLibrary AuthorID, ScopusIDa.n.busygin@utmn.ru; ORCID: 0000-0002-3439-8067

Abdulla H. Ebrahim, Postgraduate Student, Department of Applied and Technical Physics, University of Tyumen; abdulla.ybragim@mail.ru, ORCID: 0000-0002-1709-9882

Abstract:

In a promising nanoelectronic device — a memristor based on metal oxides, there are many intermediate states with different conductivity between the high- and low-conducting states. These states can be used in the processes of associative learning of a neural network based on memristor synapses and simultaneous input pulses processing, which consists of pulses weighing and summation in a neuroprocessor.

Using the method of simultaneous magnetron sputtering of two cathodes in a reactive oxygen environment, the authors obtained thin films of mixed oxides with a different mole fraction of titanium and aluminum. In addition, this article describes the method of obtaining a mixed oxide with a specified metals fraction by controlling the sputtering rates of cathodes using acoustic piezoelectric sensors.

The results show that the addition of Al into titanium oxide improves the electrophysical characteristics of the memristor. The authors proved the existence of an optimal mole fraction of Al impurity, at which the maximum ratio of the resistances of the memristor in the high-resistance and low-resistance states. The results indicate that the method of reactive magnetron deposition of mixed metal oxide by simultaneous sputtering of two cathodes provides a more uniform distribution of elements across the thickness of the active layer compared with the atomic layer deposition method. That increase of uniformity is necessary to improve the stability of the memristor.

It can be expected that in the memristors on the mixed oxides TixSc1−xOy, HfxSc1−xOy, HfxY1−xOy, HfxLu1−xOy, ZrxSc1−xOy, ZrxY1−xOy, ZrxLu1−xOy, an optimal impurity fraction corresponding to the high and low resistances ratio maximum will be observed. Moreover, memristors on pure films of pure hafnium and zirconium oxides have a much larger range of resistive switching than titanium oxide.

References:

  1. Gudkova S. A. 2011. “Investigation of the structure and properties of two and three component oxides TixAl1−xOy formed by the method of atomic layer deposition”. Cand. Sci. (Phys.-Math.) diss. abstract. Dolgoprudniy: Moscow Institute of Physics and Technology. [In Russian]
  2. Alekhin A. P., Baturin A. S., Grigal I. P., Gudkova S. A., Markeev A. M., Chouprik A. A. 2013. “A memristor based on mixed metals oxide”. RF patent No 2472254. Patent holder: MIPT, publ. 10 January. [In Russian]
  3. Pisarev A. D., Busygin A. N., Bobylev A. N., Udovichenko S. Yu. 2017. “Composite memristor-diode crossbar as a memory storage base”. Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, vol. 3, no 4, pp. 142-149. DOI: 10.21684/2411-7978-2017-3-4-142-149 [In Russian]
  4. Pisarev A. D. 2018. “SPICE-modeling of the processes of associative self learning and unconditional discrimination in the logic unit of a neuroprocessor”. Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, vol. 4, no 3, pp. 132-145. DOI: 10.21684/2411-7978-2018-4-3-132-145 [In Russian]
  5. Alekhin A. P., Chouprik A. A., Gudkova S. A., Markeev A. M. 2011. “Structural and electrical properties of TixAl1−xOy thin films grown by atomic layer deposition”. Journal of Vacuum Science and Technology B, vol. 29, 01A302. DOI: 10.1116/1.3533763
  6. Bobylev A. N., Busygin A. N., Pisarev A. D., Udovichenko S. Yu., Filippov V. A. 2017. “Neuromorphic coprocessor prototype based on mixed metal oxide memristors”. International Journal of Nanotechnology, vol. 14, no 7/8, pp. 698-704. DOI: 10.1504/IJNT.2017.083444
  7. Bobylev A. N., Udovichenko S. Yu. 2016. “The electrical properties of memristor devices TiN/TixAl1−xOy/TiN produced by magnetron sputtering”. Russian Microelectronics, vol. 45, no 6, pp. 396-401. DOI: 10.1134/S1063739716060020
  8. Fujiwara K., Nemoto T., Rozenberg M. J., Nakamura Y., Takagi H. 2008. “Resistance switching and formation of a conductive bridge in metal/binary oxide/metal structure for memory devices”. Japanese Journal of Applied Physics, vol. 47, pp. 6266-6271. DOI: 10.1143/JJAP.47.6266
  9. Gao L., Hoskins B., Strukov D. 2016. “Correlation between diode polarization and resistive switching polarity in Pt/TiO2/Pt memristive device”. Physica Status Solidi (RRL) — Rapid Research Letters, vol. 10, no 5, pp. 426-430. DOI: 10.1002/pssr.201600044
  10. Govoreanu B., Redolfi A., Zhang L., Adelmann C., Popovici M., Clima S., Hody H., Paraschiv V., Radu I. P., Franquet A., Liu J.-C., Swerts J., Richard O., Bender H., Altimime L., Jurczak M. 2013. “Vacancy-modulated conductive oxide resistive ram (vmco-rram): an area-scalable switching current, self-compliant, highly nonlinear and wide on/off-window resistive switching cell”. 2013 IEEE International Electron Devices Meeting, pp. 10.2.1-10.2.4. DOI: 10.1109/IEDM.2013.6724599
  11. Hadiyawarman, Budiman F., Hernowo D. G. O., Pandey R. R., Tanaka H. 2018. “Recent progress on fabrication of memristor and transistor-based neuromorphic devices for high signal processing speed with low power consumption”. Japanese Journal of Applied Physics, vol. 52, no 3S2, pp. 03EA06. DOI: 10.7567/JJAP.57.03EA06
  12. Merolla P. A., Arthur J. V., Alvarez-Icaza R., Cassidy A. S., Sawada J., Akopyan F., Jackson B. L., Imam N., Guo C., Nakamura Y., Brezzo B., Vo I., Esser S. K., Appuswamy R., Taba B., Amir A., Flickner M. D., Risk W. P., Manohar R., Modha D. S. 2014. “A million spiking-neuron integrated circuit with a scalable communication network and interface”. Science, vol. 345, no 6197, pp. 668-672. DOI: 10.1126/science.1254642
  13. Peng C.-S., Chang W.-Y., Lee Y.-H., Lin M.-H., Chen F., Tsai M.-J. 2012. “Improvement of resistive switching stability of HfO2 films with al doping by atomic layer deposition”. Electrochemical and Solid-State Letters, vol. 15, no 4, pp. H88-H90. DOI: 10.1149/2.011204esl
  14. Pickett M. D., Strukov D. B., Borghetti J. L., Yang J. J., Snider G. S., Stewart D. R., Williams R. S. 2009. “Switching dynamics in titanium dioxide memristive devices”. Journal of Applied Physics, vol. 106, 074508. DOI: 10.1063/1.3236506
  15. Waser R., Aono M. 2007. “Nanoionics-based resistive switching memories”. Nature Mater, vol. 6, no 11, pp. 833-840. DOI: 10.1038/nmat2023