Materials selection and fabrication nanotechnology of the composite memristor-diode crossbar — the basis of neuroprocessor hardware implementation

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


Release:

2019, Vol. 5. №4 (20)

Title: 
Materials selection and fabrication nanotechnology of the composite memristor-diode crossbar — the basis of neuroprocessor hardware implementation


For citation: Pisarev A. D., Busygin A. N., Bobylev A. N., Ebrahim A. H., Gubin A. A., Udovichenko S. Yu. 2019. “Materials selection and fabrication nanotechnology of the composite memristor-diode crossbar — the basis of neuroprocessor hardware implementation”. Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, vol. 5, no 4 (20), pp. 200-219. DOI: 10.21684/2411-7978-2019-5-4-200-219

About the authors:

Alexander D. Pisarev, Cand. Sci. (Tech.), Associate Professor, Department of Applied and Technical Physics, Head of Laboratory of Beam-Plasma Technologies, SEC “Nanotechnologies”, University of Tyumen; eLibrary AuthorID, ORCID, Scopus AuthorID, spcb.doc@gmail.com

Alexander N. Busygin, Postgraduate Student, Department of Applied and Technical Physics, Reseacher Laboratory Assistant, REC “Nanotechnology”, University of Tyumen; eLibrary AuthorID, ScopusID, a.n.busygin@utmn.ru

Andrey N. Bobylev, Head of the Laboratory of Electronic and Probe Microscopy. REC “Nanotechnology”, University of Tyumen; eLibrary AuthorID, ScopusID, andreaubobylev@gmail.com

Abdulla H. Ebrahim, Postgraduate Student, Department of Applied and Technical Physics, Research Engineer of REC “Nanotechnology”, University of Tyumen; abdulla.ybragim@mail.ru

Alexey A. Gubin, Master Student, Institute of Environmental and Agricultural Biology, Engineer, REC “Nanotechnology”, University of Tyumen; a.a.gubin@utmn.ru

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

Abstract:

To examine the operation of the memory and logic matrices of the neuroprocessor, it is necessary to produce a laboratory composite memristor-diode crossbar, which is the basis of these matrices. For this purpose, the authors of this article have chosen materials and fabrication nanotechnology of Zener diode semiconductor layers and a memristor layer that provide optimal characteristics of the diode and memristors.
This article shows that magnetron-sputtering method is optimal for fabrication of both diodes and memristors. Thus, all of composite memristor-diode crossbar layers, including conducting paths, can be fabricated in single technological module.
ZnOx was chosen as the n-type semiconductor, the carrier concentration in which is controlled by changing the stoichiometry of the compound during reactive magnetron sputtering. The second p-type layer of the diode was obtained by magnetron sputtering of a silicon target doped with boron. The results show that for the p-Si/ZnOx heterojunction, there is an optimal molar fraction of zinc, which provides the best characteristics of the diode, and an increase in the doping level of the p-Si layer leads to an increase in the nonlinearity of the current-voltage characteristic and a decrease in the voltage of the reversible breakdown.
The greatest stability of electrical parameters — switching voltages and resistances in high-conductive and low-conductive states — was achieved in a memristor with doped titanium oxide W/Ti0.93Al0.07Oy/TiN, which is due not only to the choice of mixed oxide, but also to the choice of its fabrication technology.
The measured current-voltage characteristics of separate cells prove the operability of fabricated memristor-diode crossbar. The authors show that the high resistance of the closed diode leads to the almost complete disappearance of the reverse branch of the memristor current — voltage characteristic, since the small resistance of the memristor is lost against the background high resistance of the diode.
The developed unified nanotechnology for fabricating a combined memristor-diode crossbar allows the production of ultra-large memory and logic matrices of a neuroprocessor based on one technological module with reactive magnetron sputtering.

References:

  1. 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 [In Russian]

  2. Bobylev A. N., Udovichenko S. Yu. 2016. “Electrical properties of a TiN/TixAl1−xOy/TiN memristor device manufactured by magnetron sputtering”. Russian Microelectronics, vol. 45, no 6, pp. 396-401. DOI: 10.7868/S0544126916060028 [In Russian]

  3. Pisarev A. D., Busygin A. N., Bobylev A. N., Udovichenko S. Yu. 2017. “Combined 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. Udovichenko S. Yu., Pisarev A. D., Busygin A. N., Maevsky O. V. 2017. “3D CMOS memristor nanotechnology for creating logical and memory matrices of neuroprocessor”. Nanoindustry, no 5 (76), Pp. 26-34. DOI: 10.22184/1993-8578.2017.76.5.26.34 [In Russian]

  5. Udovichenko S. Yu., Pisarev A. D., Busygin A. N., Maevsky O. V. 2018. “Neuroprocessor based on combined memristor-diode crossbar”. Nanoindustry, vol. 11, no 5 (84), pp. 344-355. DOI: 10.22184/1993-8578.2018.84.5.344.355 [In Russian]

  6. Abe H., Fujishima M., Komiyama T., Chonan Y., Yamaguchi H., Aoyama T. 2012. “Heterojunction characteristics of ZnO and CuO substrates formed by direct bonding”. Physica Status Solidi C, vol. 9, no 6, pp. 1396-1399. DOI: 10.1002/pssc.201100666

  7. Kasap S.O. 2018. Principles of Electronic Materials and Devices. 4th edition. New York: McGraw-Hill.

  8. Klimin V. S., Tominov R. V., Avilov V. I., Dukhan D. D., Rezvan A. A., Zamburg E. G., Smirnov V. A., Ageev O. A. 2019. “Nanoscale profiling and memristor effect of ZnO thin films for RRAM and neuromorphic devices application”. International Conference on Micro- and Nano-Electronics 2018, vol. 11022, art. 110220E. DOI: 10.1117/12.2522322

  9. Lee M.-J., Park Y., Kang B.-S., Ahn S.-E., Lee C., Kim K., Xianyu W., Stefanovich G., Lee J.-H., Chung S.-J., Kim Y.-H., Lee C.-S., Park J.-B., Baek I.-G., Yoo I.-K. 2007. “2-stack 1D-1R cross-point structure with oxide diodes as switch elements for high density resistance RAM applications”. IEEE International Electron Devices Meeting, pp. 771-774. Washington. DOI: 10.1109/IEDM.2007.4419061

  10. Lupan O., Pauporté Th., Tiginyanu I. M., Ursaki V. V., Heinrich H., Chow L. 2011. “Optical properties of ZnO nanowire arrays electrodeposited on n- and p-type Si(1 1 1): effects of thermal annealing”. Materials Science and Engineering. B, Solid-State Materials for Advanced Technology, vol. 176, no 16, pp. 1277-1284. DOI: 10.1016/j.mseb.2011.07.017

  11. Maevsky O. V., Pisarev A. D., Busygin A. N., Udovichenko S. Yu. 2018. “Complementary memristive diode cells for the memory matrix of a neuromorphic processor”. International Journal of nanotechnology, vol. 15, no 4/5, pp. 388-393. DOI: 10.1504/IJNT.2018.094795

  12. Matveyev Yu., Kirtaev R., Fetisova A., Zakharchenko S., Negrov D., Zenkevich A. 2016. “Crossbar nanoscale HfO2-based electronic synapses” Nanoscale Research Letters, vol. 11, art. 147. DOI: 10.1186/s11671-016-1360-6

  13. Orlov O. M., Chuprik A. A., Baturin A. S., Gornev E. S, Bulakh K. V., Egorov K. V., Kuzin A. A., Negrov D. V., Zaitsev S. A., Markeev A. M., Lebedinskii Yu. Yu., Zablotskii A. V. 2014. “Nonvolatile memory cells based on the effect of resistive switching in depth-graded ternary HfxAl1−xOy oxide films”. Russian Microelectronics, vol. 43, no 4, pp. 239-245. DOI: 10.1134/S1063739714040088

  14. Prezioso M., Merrikh-Bayat F., Hoskins B. D., Adam G. C., Likharev K. K., Strukov D. B. 2015. “Training and operation of an integrated neuromorphic network based on metal-oxide memristors”. Nature, vol. 521. pp. 61-64. DOI: 10.1038/nature14441

  15. Shulaker M. M., Hills G, Park R. S., Howe R. T., Saraswat K., Wong H.-S. P., Mitra S. 2017. “Three-dimensional integration of nanotechnologies for computing and data storage on a single chip”. Nature, vol. 547, pp. 74-78. DOI: 10.1038/nature22994

  16. Vinet M., Batude P., Tabone C., Previtali B., LeRoyer C., Pouydebasque A., Clavelier L., Valentian A., Thomas O., Michaud S., Sanchez L., Baud L., Roman A., Carron V., Nemouchi F., Mazzocchi V., Grampeix H., Amara A., Deleonibus S., Faynot O. 2011. “3D monolithic integration: technological challenges and electrical results”. Microelectronic Engineering, vol. 88, no 4, pp. 331-335. DOI: 10.1016/j.mee.2010.10.022

  17. Wong S., Hu C.-M. 1991. “SPICE macro model for the simulation of zener diode I-V characteristics”. IEEE Circuits and Devices Magazine, vol. 7, no 4, pp. 9-12. DOI: 10.1109/101.134564

  18. Zhang H., Gao B., Sun B., Chen G., Zeng L., Liu L., Liu X., Lu J., Han R., Kang J., Yu B. 2010 “Ionic doping effect in ZrO2 resistive switching memory”. Applied Physics Letters, vol. 96, art. 123502. DOI: 10.1063/1.3364130