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Tsao SC, Chang KH, Fu Y, Tai HH, Lin TH, Wu MC, Wang JC. Heterogeneous Integration of Memristive and Piezoresistive MDMO-PPV-Based Copolymers in Nociceptive Transmission with Fast and Slow Pain for an Artificial Pain-Perceptual System. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2311040. [PMID: 38864224 DOI: 10.1002/smll.202311040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 06/04/2024] [Indexed: 06/13/2024]
Abstract
Nociceptive pain perception is a remarkable capability of organisms to be aware of environmental changes and avoid injury, which can be accomplished by specialized pain receptors known as nociceptors with 4 vital properties including threshold, no adaptation, relaxation, and sensitization. Bioinspired systems designed using artificial devices are investigated to imitate the efficacy and functionality of nociceptive transmission. Here, an artificial pain-perceptual system (APPS) with a homogeneous material and heterogeneous integration is proposed to emulate the behavior of fast and slow pain in nociceptive transmission. Retention-differentiated poly[2-methoxy-5-(3,7-dimethyoctyoxyl)-1,4-phenylenevinylene] (MDMO-PPV) memristors with film thicknesses of 160 and 80 nm are manufactured and adopted as A-δ and C nerve fibers of nociceptor conduits, respectively. Additionally, a nociceptor mimic, the ruthenium nanoparticles (Ru-NPs)-doped MDMO-PPV piezoresistive pressure sensor, is fabricated with a noxiously stimulated threshold of 150 kPa. Under the application of pricking and dull noxious stimuli, the current flows predominantly through the memristor to mimic the behavior of fast and slow pain, respectively, in nociceptive transmission with postsynaptic potentiation properties, which is analogous to biological pain perception. The proposed APPS can provide potential advancements in establishing the nervous system, thus enabling the successful development of next-generation neurorobotics, neuroprosthetics, and precision medicine.
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Affiliation(s)
- Shih-Cheng Tsao
- Department of Electronic Engineering, Chang Gung University, Guishan Dist., Taoyuan, 33302, Taiwan
| | - Kuo-Hsuan Chang
- Department of Neurology, Chang Gung Memorial Hospital, Linkou, Guishan Dist., Taoyuan, 33305, Taiwan
- College of Medicine, Chang Gung University, Guishan Dist., Taoyuan, 33302, Taiwan
| | - Yi Fu
- Department of Electronic Engineering, Chang Gung University, Guishan Dist., Taoyuan, 33302, Taiwan
| | - Han-Hsiang Tai
- Department of Electronic Engineering, Chang Gung University, Guishan Dist., Taoyuan, 33302, Taiwan
| | - Ting-Han Lin
- Department of Chemical and Materials Engineering, Chang Gung University, Guishan Dist., Taoyuan, 33302, Taiwan
| | - Ming-Chung Wu
- Department of Chemical and Materials Engineering, Chang Gung University, Guishan Dist., Taoyuan, 33302, Taiwan
- Center for Sustainability and Energy Technologies, Chang Gung University, Guishan Dist., Taoyuan, 33302, Taiwan
- Division of Neonatology, Department of Pediatrics, Chang Gung Memorial Hospital, Linkou, Guishan Dist., Taoyuan, 33305, Taiwan
| | - Jer-Chyi Wang
- Department of Electronic Engineering, Chang Gung University, Guishan Dist., Taoyuan, 33302, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Linkou, Guishan Dist., Taoyuan, 33305, Taiwan
- Department of Electronic Engineering, Ming Chi University of Technology, Taishan Dist., New Taipei City, 243303, Taiwan
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Yu Y, Ding Z, Ren Y, Wang X, Quan H, Jia H, Jiang C. Understanding the Resistive Switching Behaviors of Top Electrode (Au, Cu, and Al)-Dependent TiO 2-Based Memristive Devices. ACS OMEGA 2024; 9:24601-24609. [PMID: 38882132 PMCID: PMC11170736 DOI: 10.1021/acsomega.4c00320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 04/26/2024] [Accepted: 04/30/2024] [Indexed: 06/18/2024]
Abstract
Memristor-based neuromorphic computing is promising toward their potential application of handling complex parallel tasks in the period of big data. To implement brain-inspired applications of spiking neural networks, new physical architecture designs are needed. Here, a serial memristive structure (SMS) consisting of memristive devices with different top electrodes is proposed. Top electrodes Au, Cu, and Al are selected for nitrogen-doped TiO2 nanorod array-based memristive devices. The typical I-V cycles, retention, on/off ratio, and variations of cycle to cycle of top electrode-dependent memristive devices have been studied. Devices with Cu and Al electrodes exhibit a retention of over 104 s. And the resistance states of the device with the Al top electrode are reliable. Furthermore, the conductive mechanism underlining the I-V curves is discussed in detail. The interface-type mechanism and block conductance mechanism are illustrated, which are related to electron migration and ion/anion migration, respectively. Finally, the SMS has been constructed using memristive devices with Al and Cu top electrodes, which can mimic the spiking pulse-dependent plasticity of a synapse and a neuron body. The SMS provides a new approach to implement a fundamental physical unit for neuromorphic computing.
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Affiliation(s)
- Yantao Yu
- College of Physics and Electronic Information, Luoyang Normal University, Luoyang 471934, China
| | - Zizhao Ding
- Powder Metallurgy Research Institute, Central South University, Changsha 410083, China
| | - Yaoying Ren
- College of Physics and Electronic Information, Luoyang Normal University, Luoyang 471934, China
| | - Xiangfei Wang
- College of Physics and Electronic Information, Luoyang Normal University, Luoyang 471934, China
| | - Hongguang Quan
- College of Physics and Electronic Information, Luoyang Normal University, Luoyang 471934, China
| | - Hong Jia
- College of Physics and Electronic Information, Luoyang Normal University, Luoyang 471934, China
| | - Chao Jiang
- Powder Metallurgy Research Institute, Central South University, Changsha 410083, China
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Verma D, Chen TC, Liu B, Lai CS. Bi 2O 2Se-based CBRAM integrated artificial synapse. Heliyon 2023; 9:e22512. [PMID: 38107308 PMCID: PMC10724560 DOI: 10.1016/j.heliyon.2023.e22512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 10/30/2023] [Accepted: 11/14/2023] [Indexed: 12/19/2023] Open
Abstract
Integrating two-dimensional (2D) semiconducting materials into memristor structures has paved the way for emerging 2D materials to be employed in a vast field of memory applications. Bismuth oxyselenide (Bi2O2Se), a 2D material with high electron mobility, has attracted significant research interest owing to its great potential in various fields of advanced applications. Here, we explore the out-of-plane intrinsic switching behavior of few-layered Bi2O2Se via a cross point device for application in conductive bridge random access memory (CBRAM) and artificial synapses for neuromorphic computing. Via state-of-the-art methods, CVD-grown Bi2O2Se nanoplate is applied as a switching material (SM) in an Al/Cu/Bi2O2Se/Pd CBRAM structure. The device exhibits ∼90 consecutive DC cycles with a tight distribution of the SET/RESET voltages under a compliance current (CC) of 1 mA, a retention of over 10 ks, and multilevel switching characteristics showing four distinct states at Vread values of 0.1, 0.2, 0.25, and 0.3 V. Moreover, an artificial synapse is realized with potentiation and depression by modulating the conductance. The switching mechanism is explained via Cu migration through Bi2O2Se based on HRTEM analysis. The present structure shows potential for future integrated memory applications.
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Affiliation(s)
- Dharmendra Verma
- Department of Electronic Engineering, Chang Gung University, Taoyuan 33302, Taiwan
| | - Tsung-Cheng Chen
- Department of Electronic Engineering, Chang Gung University, Taoyuan 33302, Taiwan
| | - Bo Liu
- Faculty of Information Technology, College of Microelectronics, Beijing University of Technology, Beijing 100124, People’s Republic of China
| | - Chao-Sung Lai
- Department of Electronic Engineering, Chang Gung University, Taoyuan 33302, Taiwan
- Department of Nephrology, Chang Gung Memorial Hospital, Linkou 33302, Taiwan
- Department of Materials Engineering, Ming-Chi University of Technology, New Taipei City 24301, Taiwan
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Tzouvadaki I, Gkoupidenis P, Vassanelli S, Wang S, Prodromakis T. Interfacing Biology and Electronics with Memristive Materials. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2210035. [PMID: 36829290 DOI: 10.1002/adma.202210035] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 01/31/2023] [Indexed: 06/18/2023]
Abstract
Memristive technologies promise to have a large impact on modern electronics, particularly in the areas of reconfigurable computing and artificial intelligence (AI) hardware. Meanwhile, the evolution of memristive materials alongside the technological progress is opening application perspectives also in the biomedical field, particularly for implantable and lab-on-a-chip devices where advanced sensing technologies generate a large amount of data. Memristive devices are emerging as bioelectronic links merging biosensing with computation, acting as physical processors of analog signals or in the framework of advanced digital computing architectures. Recent developments in the processing of electrical neural signals, as well as on transduction and processing of chemical biomarkers of neural and endocrine functions, are reviewed. It is concluded with a critical perspective on the future applicability of memristive devices as pivotal building blocks in bio-AI fusion concepts and bionic schemes.
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Affiliation(s)
- Ioulia Tzouvadaki
- Centre for Microsystems Technology, Ghent University-IMEC, Ghent, 9052, Belgium
| | | | - Stefano Vassanelli
- NeuroChip Laboratory and Padova Neuroscience Centre, University of Padova, Padova, 35129, Italy
| | - Shiwei Wang
- Centre for Electronics Frontiers, The University of Edinburgh, Edinburgh, EH9 3JL, UK
| | - Themis Prodromakis
- Centre for Electronics Frontiers, The University of Edinburgh, Edinburgh, EH9 3JL, UK
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Kundale SS, Kamble GU, Patil PP, Patil SL, Rokade KA, Khot AC, Nirmal KA, Kamat RK, Kim KH, An HM, Dongale TD, Kim TG. Review of Electrochemically Synthesized Resistive Switching Devices: Memory Storage, Neuromorphic Computing, and Sensing Applications. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:1879. [PMID: 37368309 DOI: 10.3390/nano13121879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/09/2023] [Accepted: 06/13/2023] [Indexed: 06/28/2023]
Abstract
Resistive-switching-based memory devices meet most of the requirements for use in next-generation information and communication technology applications, including standalone memory devices, neuromorphic hardware, and embedded sensing devices with on-chip storage, due to their low cost, excellent memory retention, compatibility with 3D integration, in-memory computing capabilities, and ease of fabrication. Electrochemical synthesis is the most widespread technique for the fabrication of state-of-the-art memory devices. The present review article summarizes the electrochemical approaches that have been proposed for the fabrication of switching, memristor, and memristive devices for memory storage, neuromorphic computing, and sensing applications, highlighting their various advantages and performance metrics. We also present the challenges and future research directions for this field in the concluding section.
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Affiliation(s)
- Somnath S Kundale
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur 416004, India
| | - Girish U Kamble
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur 416004, India
| | - Pradnya P Patil
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur 416004, India
| | - Snehal L Patil
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur 416004, India
| | - Kasturi A Rokade
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur 416004, India
| | - Atul C Khot
- School of Electrical Engineering, Korea University, Anam-dong, Seoul 02841, Republic of Korea
| | - Kiran A Nirmal
- School of Electrical Engineering, Korea University, Anam-dong, Seoul 02841, Republic of Korea
| | - Rajanish K Kamat
- Department of Electronics, Shivaji University, Kolhapur 416004, India
- Department of Physics, Dr. Homi Bhabha State University, 15, Madam Cama Road, Mumbai 400032, India
| | - Kyeong Heon Kim
- Department of Convergence Electronic Engineering, Gyeongsang National University, Jinjudae-ro 501, Jinju 52828, Republic of Korea
| | - Ho-Myoung An
- Department of Electronics, Osan University, 45, Cheonghak-ro, Osan-si 18119, Republic of Korea
| | - Tukaram D Dongale
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur 416004, India
- School of Electrical Engineering, Korea University, Anam-dong, Seoul 02841, Republic of Korea
| | - Tae Geun Kim
- School of Electrical Engineering, Korea University, Anam-dong, Seoul 02841, Republic of Korea
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Andreeva NV, Ryndin EA, Mazing DS, Vilkov OY, Luchinin VV. Organismic Memristive Structures With Variable Functionality for Neuroelectronics. Front Neurosci 2022; 16:913618. [PMID: 35774561 PMCID: PMC9238295 DOI: 10.3389/fnins.2022.913618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 05/16/2022] [Indexed: 11/13/2022] Open
Abstract
In this paper, we report an approach to design nanolayered memristive compositions based on TiO2/Al2O3 bilayer structures with analog non-volatile and volatile tuning of the resistance. The structure of the TiO2 layer drives the physical mechanism underlying the non-volatile resistance switching, which can be changed from electronic to ionic, enabling the synaptic behavior emulation. The presence of the anatase phase in the amorphous TiO2 layer induces the resistive switching mechanism due to electronic processes. In this case, the switching of the resistance within the range of seven orders of magnitude is experimentally observed. In the bilayer with amorphous titanium dioxide, the participation of ionic processes in the switching mechanism results in narrowing the tuning range down to 2–3 orders of magnitude and increasing the operating voltages. In this way, a combination of TiO2/Al2O3 bilayers with inert electrodes enables synaptic behavior emulation, while active electrodes induce the neuronal behavior caused by cation density variation in the active Al2O3 layer of the structure. We consider that the proposed approach could help to explore the memristive capabilities of nanolayered compositions in a more functional way, enabling implementation of artificial neural network algorithms at the material level and simplifying neuromorphic layouts, while maintaining all benefits of neuromorphic architectures.
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Affiliation(s)
- Natalia V. Andreeva
- Department of Micro- and Nanoelectronics, Faculty of Electronics, Saint Petersburg State Electrotechnical University “LETI”, Saint Petersburg, Russia
- *Correspondence: Natalia V. Andreeva,
| | - Eugeny A. Ryndin
- Department of Micro- and Nanoelectronics, Faculty of Electronics, Saint Petersburg State Electrotechnical University “LETI”, Saint Petersburg, Russia
| | - Dmitriy S. Mazing
- Department of Micro- and Nanoelectronics, Faculty of Electronics, Saint Petersburg State Electrotechnical University “LETI”, Saint Petersburg, Russia
| | - Oleg Y. Vilkov
- Department of Solid State Electronics, Saint Petersburg State University, Saint Petersburg, Russia
| | - Victor V. Luchinin
- Department of Micro- and Nanoelectronics, Faculty of Electronics, Saint Petersburg State Electrotechnical University “LETI”, Saint Petersburg, Russia
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Choi SH, Park SO, Seo S, Choi S. Reliable multilevel memristive neuromorphic devices based on amorphous matrix via quasi-1D filament confinement and buffer layer. SCIENCE ADVANCES 2022; 8:eabj7866. [PMID: 35061541 PMCID: PMC8782456 DOI: 10.1126/sciadv.abj7866] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 11/30/2021] [Indexed: 05/19/2023]
Abstract
Conductive-bridging random access memory (CBRAM) has garnered attention as a building block of non-von Neumann architectures because of scalability and parallel processing on the crossbar array. To integrate CBRAM into the back-end-of-line (BEOL) process, amorphous switching materials have been investigated for practical usage. However, both the inherent randomness of filaments and disorders of amorphous material lead to poor reliability. In this study, a highly reliable nanoporous-defective bottom layer (NP-DBL) structure based on amorphous TiO2 is demonstrated (Ag/a-TiO2/a-TiOx/p-Si). The stoichiometries of DBL and the pore size can be manipulated to achieve the analog conductance updates and multilevel conductance by 300 states with 1.3% variation, and 10 levels, respectively. Compared with nonporous TiO2 CBRAM, endurance, retention, and uniformity can be improved by 106 pulses, 28 days at 85°C, and 6.7 times, respectively. These results suggest even amorphous-based systems, elaborately tuned structural variables, can help design more reliable CBRAMs.
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Andreeva N, Mazing D, Romanov A, Gerasimova M, Chigirev D, Luchinin V. Contact Engineering Approach to Improve the Linearity of Multilevel Memristive Devices. MICROMACHINES 2021; 12:mi12121567. [PMID: 34945416 PMCID: PMC8706226 DOI: 10.3390/mi12121567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/10/2021] [Accepted: 12/14/2021] [Indexed: 11/16/2022]
Abstract
Physical mechanisms underlying the multilevel resistive tuning over seven orders of magnitude in structures based on TiO2/Al2O3 bilayers, sandwiched between platinum electrodes, are responsible for the nonlinear dependence of the conductivity of intermediate resistance states on the writing voltage. To improve the linearity of the electric-field resistance tuning, we apply a contact engineering approach. For this purpose, platinum top electrodes were replaced with aluminum and copper ones to induce the oxygen-related electrochemical reactions at the interface with the Al2O3 switching layer of the structures. Based on experimental results, it was found that electrode material substitution provokes modification of the physical mechanism behind the resistive switching in TiO2/Al2O3 bilayers. In the case of aluminum electrodes, a memory window has been narrowed down to three orders of magnitude, while the linearity of resistance tuning was improved. For copper electrodes, a combination of effects related to metal ion diffusion with oxygen vacancies driven resistive switching was responsible for a rapid relaxation of intermediate resistance states in TiO2/Al2O3 bilayers.
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Li D, Tan R, Mi X, Fang C, Tu Y. An electrochemiluminescent biosensor for noninvasive glucose detection based on cluster-like AuAg hollowed-nanoparticles. Microchem J 2021. [DOI: 10.1016/j.microc.2021.106271] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Toward Reliable Compact Modeling of Multilevel 1T-1R RRAM Devices for Neuromorphic Systems. ELECTRONICS 2021. [DOI: 10.3390/electronics10060645] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this work, three different RRAM compact models implemented in Verilog-A are analyzed and evaluated in order to reproduce the multilevel approach based on the switching capability of experimental devices. These models are integrated in 1T-1R cells to control their analog behavior by means of the compliance current imposed by the NMOS select transistor. Four different resistance levels are simulated and assessed with experimental verification to account for their multilevel capability. Further, an Artificial Neural Network study is carried out to evaluate in a real scenario the viability of the multilevel approach under study.
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Switching Characteristics and Mechanism Using Al2O3 Interfacial Layer in Al/Cu/GdOx/Al2O3/TiN Memristor. ELECTRONICS 2020. [DOI: 10.3390/electronics9091466] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Resistive switching characteristics by using the Al2O3 interfacial layer in an Al/Cu/GdOx/Al2O3/TiN memristor have been enhanced as compared to the Al/Cu/GdOx/TiN structure owing to the insertion of Al2O3 layer for the first time. Polycrystalline grain, chemical composition, and surface roughness of defective GdOx film have been investigated by transmission electron microscope (TEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and atomic force microscopy (AFM). For bipolar resistive switching (BRS) characteristics, the conduction mechanism of high resistance state (HRS) is a space-charge limited current for the Al/Cu/GdOx/TiN device while the Al/Cu/GdOx/Al2O3/TiN device shows Schottky emission. However, both devices show Ohmic at a low resistance state (LRS). After the device has been SET, the Cu filament evidences by both TEM and elemental mapping. Oxygen-rich at the Cu/GdOx interface and Al2O3 layer are confirmed by energy dispersive X-ray spectroscopy (EDS) line profile. The Al/Cu/GdOx/Al2O3/TiN memristor has lower RESET current, higher speed operation of 100 ns, long read pulse endurance of >109 cycles, good data retention, and the memristor with a large resistance ratio of >105 is operated at a low current of 1.5 µA. The complementary resistive switching (CRS) characteristics of the Al/Cu/GdOx/Al2O3/TiN memristor show also a low current operation as compared to the Al/Cu/GdOx/TiN device (300 µA vs. 3.1 mA). The transport mechanism is the Cu ion migration and it shows Ohmic at low field and hopping at high field regions. A larger hopping distance of 1.82 nm at the Cu/GdOx interface is obtained as compared to a hopping distance of 1.14 nm in the Al2O3 layer owing to a larger Cu filament length at the Cu/GdOx interface than the Al2O3 layer. Similarly, the CRS mechanism is explained by using the schematic model. The CRS characteristics show a stable state with long endurance of >1000 cycles at a pulse width of 1 µs owing to the insertion of Al2O3 interfacial layer in the Al/Cu/GdOx/Al2O3/TiN structure.
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