A Fabrication Method for Memristors with Graphene Top Electrodes and their Characterization

Year 2024, Volume: 11 Issue: 1, 7 - 14, 31.03.2024

Selin Onay , Ömer Refet Çaylan , Zarife Göknur Büke , Itır Köymen

https://doi.org/10.17350/HJSE19030000326

Cited By: 1

Abstract

In recent years, there has been extensive research on the memristor, a non-volatile memory device that demonstrates effective emulation of biological synapses. The implementation of graphene as a top electrode in memristive switching systems presents an intriguing alternative to conventional materials such as Platinum. Graphene, as a carbon-derived material, possesses a remarkable area- to-volume ratio, biocompatibility, adsorption capabilities, and high electrical conductivity and thereby offers a promising avenue for the fabrication of biosensors with superior characteristics. This study reports a novel fabrication method of utilizing graphene as a top electrode in memristive devices. Characterization results of micrometric devices as well as larger memristive devices are also discussed. Larger devices show promising results to be used as memristive sensors. Microstructures have been fabricated successfully through developing a process flow and patterning graphene using photolithography and lift-off. E-beam evaporation and sputtering were used for depositing bottom metal electrodes and active layer respectively. Graphene was produced using the chemical vapor deposition (CVD) method and subsequently transferred using the fishing technique. Ultimately Pt/TiO2/TiOx/Graphene memristive devices were fabricated.

Keywords

memristor, Graphene, Device fabrication, Graphene patterning, Device characterization

Project Number

119E367

Thanks

This work was supported by TUBITAK [grant number: 119E367].

References

• Chua L. Memristor-The missing circuit element. IEEE Trans Circuit Theory. 1971;18(5):507–19.
• Strukov DB, Snider GS, Stewart DR, Williams RS. The missing memristor found. Nature. 2008 May 1;453(7191):80–3.
• Hamdioui S, Taouil M, Du Nguyen HA, Haron A, Xie L, Bertels K. Memristor: the enabler of computation-in-memory architecture for big-data. In: 2015 International Conference on Memristive Systems (MEMRISYS) [Internet]. Paphos, Cyprus: IEEE; 2015 [cited 2023 Aug 14]. p. 1–3. Available from: http://ieeexplore.ieee.org/document/7378391/
• Rady H, Hossam H, Saied MS, Mostafa H. Memristor-Based AES Key Generation for Low Power IoT Hardware Security Modules. In: 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS) [Internet]. Dallas, TX, USA: IEEE; 2019 [cited 2023 Aug 14]. p. 231–4. Available from: https://ieeexplore.ieee.org/document/8885031/
• Zhao C, Shen ZJ, Zhou GY, Zhao CZ, Yang L, Man KL, et al. Neuromorphic Properties of Memristor towards Artificial Intelligence. In: 2018 International SoC Design Conference (ISOCC) [Internet]. Daegu, Korea (South): IEEE; 2018 [cited 2023 Aug 14]. p. 172–3. Available from: https://ieeexplore.ieee.org/document/8649926/
• Zhou Z, Yang F, Wang S, Wang L, Wang X, Wang C, et al. Emerging of two-dimensional materials in novel memristor. Front Phys. 2022 Apr;17(2):23204.
• Rozenberg MJ, Inoue IH, Sánchez MJ. Nonvolatile Memory with Multilevel Switching: A Basic Model. Phys Rev Lett. 2004 Apr 30;92(17):178302.
• Gebregiorgis A, Singh A, Diware S, Bishnoi R, Hamdioui S. Dealing with Non-Idealities in Memristor Based Computation-In-Memory Designs. In: 2022 IFIP/IEEE 30th International Conference on Very Large Scale Integration (VLSI- SoC) [Internet]. Patras, Greece: IEEE; 2022 [cited 2023 Aug 14]. p. 1–6. Available from: https://ieeexplore.ieee.org/document/9939618/
• Kim GS, Song H, Lee YK, Kim JH, Kim W, Park TH, et al. Defect- Engineered Electroforming-Free Analog HfO x Memristor and Its Application to the Neural Network. ACS Appl Mater Interfaces. 2019 Dec 18;11(50):47063–72.
• Hu SG, Liu Y, Chen TP, Liu Z, Yu Q, Deng LJ, et al. Emulating the paired-pulse facilitation of a biological synapse with a NiOx-based memristor. Appl Phys Lett. 2013 May 6;102(18):183510.
• Sun W, Gao B, Chi M, Xia Q, Yang JJ, Qian H, et al. Understanding memristive switching via in situ characterization and device modeling. Nat Commun. 2019 Aug 1;10(1):3453.
• Abbas H, Abbas Y, Truong SN, Min KS, Park MR, Cho J, et al. A memristor crossbar array of titanium oxide for non- volatile memory and neuromorphic applications. Semicond Sci Technol. 2017 Jun 1;32(6):065014.
• Paul S, Harris PG, Pal C, Sharma AK, Ray AK. Low cost zinc oxide for memristors with high On–Off ratios. Mater Lett. 2014 Sep;130:40–2.
• Awais MN, Muhammad NM, Navaneethan D, Kim HC, Jo J, Choi KH. Fabrication of ZrO2 layer through electrohydrodynamic atomization for the printed resistive switch (memristor). Microelectron Eng. 2013 Mar;103:167–72.
• Ali S, Khan S, Khan A, Bermak A. Memristor Fabrication Through Printing Technologies: A Review. IEEE Access. 2021;9:95970–85.
• Kumar S, Wang Z, Huang X, Kumari N, Davila N, Strachan JP, et al. Conduction Channel Formation and Dissolution Due to Oxygen Thermophoresis/Diffusion in Hafnium Oxide Memristors. ACS Nano. 2016 Dec 27;10(12):11205–10.
• Justino CIL, Gomes AR, Freitas AC, Duarte AC, Rocha-Santos TAP. Graphene based sensors and biosensors. TrAC Trends Anal Chem. 2017 Jun 1;91:53–66.
• Colak M, Onay S, Orhan B, Milano G, Koymen I. Simulation of Half- Center Oscillator Circuits Employing Newly Developed Models of Fabricated Memristors. In: 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) [Internet]. Ankara, Turkey: IEEE; 2022 [cited 2023 Aug 18]. p. 504–8. Available from: https://ieeexplore.ieee.org/document/9932824/
• Long ML, Newman J. Image Reversal Techniques With Standard Positive Photoresist. In: Willson CG, editor. Santa Clara; 1984 [cited 2023 Aug 18]. p. 189. Available from: http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.941793
• Rasool HI, Song EB, Allen MJ, Wassei JK, Kaner RB, Wang KL, et al. Continuity of Graphene on Polycrystalline Copper. Nano Lett. 2011 Jan 12;11(1):251–6.
• Zheng Y, Wang H, Hou S, Xia D. Lithographically Defined Graphene Patterns. Adv Mater Technol. 2017 May;2(5):1600237.