STUDY OF RESISTIVE SWITCHING OF TRANSPARENT ZINC OXIDE MEMRISTIVE STRUCTURES FOR MACHINE VISION OF ROBOTIC SYSTEMS
DOI:
https://doi.org/10.18522/2311-3103-2026-1-%25pKeywords:
Nanomaterials, ZnO thin films, RF magnetron sputtering, structural properties, neuromorphic systems, machine vision, transparent memristor, resistive switchingAbstract
The development of neuromorphic machine vision systems for robotic systems requires the creation of transparent memristive structures that combine optical transparency, stable bipolar resistive switching, and compatibility with crossbar array technology. A key challenge is to establish patterns in the influence of ZnO thin film deposition modes on their structural and electrical properties, which determine the characteristics of memristive structures. The aim of this study was to determine the optimal RF magnetron sputtering power for a ZnO ceramic target, ensuring the formation of transparent ITO/ZnO/ITO memristive structures with stable resistive switching, and to create a crossbar array based on these structures. ZnO thin films were deposited using RF magnetron sputtering at powers ranging from 25 to 100 W. Structural (SEM, AFM) and electrical (Hall effect) studies of the resulting ZnO films were conducted. Transparent ITO/ZnO/ITO memristive structures and a crossbar array of 16 structures with a cell size of 2000 × 2000 nm were fabricated on glass substrates using magnetron sputtering and lithography, and their current-voltage characteristics were measured. Increasing the magnetron sputtering power from 25 to 100 W resulted in an increase in the grain size from 12,8 to 35,7 nm and in the surface roughness of the ZnO films from 2,8 to 11,4 nm. At a sputtering power of 75 W, the charge carrier concentration in the ZnO films reached a maximum value of 2.7 × 1015 cm-3, which is necessary for stable resistive switching of the structure. The obtained ITO/ZnO/ITO memristive structures were shown to exhibit stable bipolar switching for 1000 cycles between the states HRS = 537,4 ± 26,7 Ohm and LRS = 291,4 ± 38,5 Ohm (HRS/LRS ratio ~ 1,8). The fabricated transparent crossbar array showed stable resistive switching for 20000 cycles (LRS = 13,8 ± 1,4 kOhm, HRS = 34,8 ± 2,6 kOhm, HRS/LRS ratio ~ 2,5). The obtained results can be used in the development of technological processes for the fabrication of transparent memristor crossbars for neuromorphic structures of machine vision in robotic systems
References
1. Jing Yang, Lingxiang Hu, Liufeng Shen, Jingrui Wang, Peihong Cheng, Huanming Lu, Fei Zhuge, Zhizhen Ye. Optically driven intelligent computing with ZnO memristor, Fundamental Research, 2024, Vol. 4, pp. 158-166.
2. Usman Bature Isyaku, Mohd Haris Bin Md Khir, I. Md Nawi, M.A. Zakariya, Furqan Zahoor. ZnO Based Resistive Random Access Memory Device: A Prospective Multifunctional Next-Generation Memory, IEEE Access, 2021, Vol. 9, pp. 105012.
3. P. Praveen, T. Priya Rose, K.J. Saji. Top electrode dependent resistive switching in M/ZnO/ITO memristors, M = Al, ITO, Cu, and Au, Microelectronics Journal, 2022, Vol. 121, pp. 105388.
4. Saenko A.V., Tominov R.V., Jityaev I.L., Vakulov Z.E., Avilov V.I., Polupanov N.V., Smirnov V.A. Transparent Zinc Oxide Memristor Structures: Magnetron Sputtering of Thin Films, Resistive Switching Investigation, and Crossbar Array Fabrication, Nanomaterials, 2024, Vol. 14, pp. 1901.
5. Cristian L. Teran, Jorge A. Calderon, Heiddy P. Quiroz, A. Dussan. Optical properties and bipolar resistive switching of ZnO thin films deposited via DC magnetron sputtering, Chinese Journal of Phys-ics, 2021, Vol. 74, pp. 1-8.
6. Chander Prakash, Lovi Raj Gupta, Amrinder Mehta, Hitesh Vasudev, Roman Tominov, Ekaterina Korman, Alexander Fedotov, Vladimir Smirnov, Kavindra Kumar Kesari. Computing of neuromorphic materials: an emerging approach for bioengineering solutions, Materials Advances, 2023, Vol. 4,
pp. 5882-5919.
7. Yuxin Shi, Yanna Zhang, Guoqiang Li. Recent progress in transparent memristors, Journal of Physics D: Applied Physics, 2023, Vol. 56, pp. 313001.
8. Huiling Zhang, Ruping Liu, Huiqing Zhao, Zhicheng Sun, Zilong Liu, Liang He, Ye Li. Research Pro-gress of Biomimetic Memristor Flexible Synapse, Coatings, 2022, Vol. 12, pp. 21.
9. Ping-Xing Chen, Debashis Panda, Tseung-Yuen Tseng. All oxide based flexible multi-folded invisible synapse as vision photoreceptor, Scientifc Reports, 2023, Vol. 13, pp. 1454.
10. Raveendra Kiran M., Hidayath Ulla, Satyanarayan M.N., Umesh G. Effects of annealing temperature on the resistance switching behaviour of solution-processed ZnO thin films, Superlattices and Micro-structures, 2020, Vol. 148, 106718.
11. Asutosh Patnaik, Srikant Kumar Mohanty, Narayan Sahoo, Debashis Panda. Effect of oxygen concen-tration in ZnO-based transparent flexible memristor synapse, Journal of Materials Science: Materials in Electronics, 2023, Vol. 34, pp. 1406.
12. Hongxia Li, Wei Dong, Xin Wu, Junhua Xi, Zhenguo Ji. Resistive switching characteristics of ZnO/a-TiO2 bilayer film fabricated on PET/ITO transparent and flexible substrates, Materials Research Bulletin, 2016, Vol. 84, pp. 449-454.
13. Tominov R.V., Vakulov Z.E., Avilov V.I., Shikhovtsov I.A., Varganov V.I., Kazantsev V.B., Lovi Raj Gup-ta, Chander Prakash, Smirnov V.A. Approaches for Memristive Structures Using Scratching Probe Nanolithography: Towards Neuromorphic Applications, Nanomaterials, 2023, Vol. 13, pp. 1583.
14. Abduev A.Kh., Akhmedov A.K., Asvarov A.Sh., Muslimov A.E., Kanevsky V.M. ZnO-based transparent conductive layers obtained by the magnetron sputtering of a composite cermet ZnO:Ga–Zn target:
part 2, Journal of Surface Investigation. X-Ray, Synchrotron and Neutron Techniques, 2021, Vol. 15, pp. 121-127.
15. Sobia Ali Khan, Geun Ho Lee, Chandreswar Mahata, Muhammad Ismail, Hyungjin Kim, Sungjun Kim. Bipolar and Complementary Resistive Switching Characteristics and Neuromorphic System Simulation in a Pt/ZnO/TiN Synaptic Device, Nanomaterials, 2021, Vol. 11, pp. 315.
16. Kavindra Kandpal, Jitendra Singh, Navneet Gupta, Chandra Shekhar. Effect of thickness on the prop-erties of ZnO thin films prepared by reactive RF sputtering, Journal of Materials Science: Materials in Electronics, 2018, Vol. 29, pp. 14501-14507.
17. Zhiqiang Yu, Jinhao Jia, Xinru Qu, Qingcheng Wang, Wenbo Kang, Baosheng Liu, Qingquan Xiao, Tinghong Gao, Quan Xie. Tunable Resistive Switching Behaviors and Mechanism of the W/ZnO/ITO Memory Cell // Molecules. – 2023. – Vol. 28. – P. 5313.
18. Paulina Kaim, Krzysztof Lukaszkowicz, Marek Szindler, Magdalena M. Szindler, Marcin Basiaga, Barbara Hajduk. The influence of magnetron sputtering process temperature on ZnO thin-film proper-ties, Coatings, 2021, Vol. 11, pp. 1507.
19. Ziyu Lv, Yan Wang, Jingrui Chen, Junjie Wang, Ye Zhou, Su-Ting Han. Semiconductor Quantum Dots for Memories and Neuromorphic Computing Systems, Chemical Reviews Journal, 2020, Vol. 120,
pp. 3941-4006.
20. Ziyu Lv, Shirui Zhu, Yan Wang, Yanyun Ren, Mingtao Luo, Hanning Wang, Guohua Zhang, Yongbiao Zhai, Shilong Zhao, Ye Zhou, Minghao Jiang, Yan-Bing Leng, Su-Ting Han. Development of Bio-Voltage Operated Humidity-Sensory Neurons Comprising Self-Assembled Peptide Memristors, Ad-vanced Materials, 2024, Vol. 36, pp. 2405145.
21. Tominov R.V., Vakulov Z. E., Polupanov N.V., Saenko A.V., Avilov V.I., Ageev O.A., Smirnov V.A. Na-noscale-resistive switching in forming-free zinc oxide memristive structures, Nanomaterials, 2022, Vol. 12, pp. 455.
22. Saenko A.V., Vakulov Z.E., Klimin V.S., Bilyk G.E., Malyukov S.P. Effect of Magnetron Sputtering Pow-er on ITO Film Deposition at Room Temperature, Russian Microelectronics, 2023, Vol. 23,
pp. 297-302.
23. Serb A., Bill J., Khiat A., Berdan R., Legenstein R., Prodromakis T. Unsupervised learning in probabilis-tic neural networks with multi-state metal-oxide memristive synapses, Nature communications, 2016, Vol. 7, pp. 12611.








