Exploring conductance modulation and implementation of convolutional neural network in Pt/ZnO/Al2O3/TaN memristors for brain-inspired computing

Ismail, Muhammad; Mahata, Chandreswar; Kang, Myounggon; Kim, Sungjun

doi:10.1016/j.ceramint.2023.03.030

Cited by 9 publications

(6 citation statements)

References 64 publications

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“…Memristors with their high-density integration, and fast switching speed storage capabilities [5,6], show great potential in data storage, radio-frequency communication and neuromorphic computing systems for next-generation efficient information processing systems [7][8][9]. At present, metal oxide materials as the resistive switching layer, such as ZnO x , CeO x , HfO x , ZrO x , and TaO x , have gained magnitude attention in neuromorphic computing [10,11]. However, to date, memristor-based neuromorphic computing systems still face the challenge of designing a device with outstanding multilevel resistive switching behavior.…”

Section: Introductionmentioning

confidence: 99%

Resistive switching modulation by incorporating thermally enhanced layer in HfO₂-based memristor

Li,

Feng,

Zou

et al. 2023

Nanotechnology

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Oxide-based memristors by incorporating thermally enhanced layer (TEL) have showed great potential in electronic devices for high-efficient and high-density neuromorphic computing owing to the improvement of multilevel resistive switching. However, research on the mechanism of resistive switching regulation is still lacking. In this work, based on the method of finite element numerical simulation analysis, a bilayer oxide-based memristor Pt/HfO2 (5 nm)/Ta2O5 (5 nm)/Pt with the Ta2O5 TEL was proposed. The oxygen vacancy concentrates distribution shows that the fracture of conductive filaments (CF) is at the interface where the local temperature is the highest during the reset process. The multilevel resistive switching properties were also obtained by applying different stop voltages. The fracture gap of CF can be enlarged with the increase of the stopping voltage, which is attributed to the heat-gathering ability of the TEL. Moreover, it was found that the fracture position of oxygen CF is dependent on the thickness of TEL, which exhibits a modulation of device RS performance. These results provide a theoretical guidance on the suitability of memristor devices for use in high-density memory and brain-actuated computer systems.

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Section: Introductionmentioning

confidence: 99%

Resistive switching modulation by incorporating thermally enhanced layer in HfO₂-based memristor

Li,

Feng,

Zou

et al. 2023

Nanotechnology

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“…Transition metal oxides (TMOs) encompass a captivating cohort of materials, profoundly explored for their roles in resistive RS memory. This category includes a diverse range of oxide materials, including TiO 2 , ZrO 2 [ 21 ], Ta 2 O 5 [ 22 ], HfO 2 [ 23 ], CeO 2 [ 24 ], and Al 2 O 3 [ 25 ], alongside semiconducting counterparts like ZnO [ 26 ], ITO [ 27 ], and, notably, SnO 2 [ 28 ]. These materials have been vigorously examined for their suitability as RS layers within resistive random-access memories (RRAMs).…”

Section: Introductionmentioning

confidence: 99%

SnO2-Based Memory Device with Filamentary Switching Mechanism for Advanced Data Storage and Computing

Ismail,

Mahata,

Kang

et al. 2023

Nanomaterials

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In this study, we fabricate a Pt/TiN/SnOx/Pt memory device using reactive sputtering to explore its potential for neuromorphic computing. The TiON interface layer, formed when TiN comes into contact with SnO2, acts as an oxygen vacancy reservoir, aiding the creation of conductive filaments in the switching layer. Our SnOx-based device exhibits remarkable endurance, with over 200 DC cycles, ON/FFO ratio (>20), and 104 s retention. Set and reset voltage variabilities are impressively low, at 9.89% and 3.2%, respectively. Controlled negative reset voltage and compliance current yield reliable multilevel resistance states, mimicking synaptic behaviors. The memory device faithfully emulates key neuromorphic characteristics, encompassing both long-term potentiation (LTP) and long-term depression (LTD). The filamentary switching mechanism in the SnOx-based memory device is explained by an oxygen vacancy concentration gradient, where current transport shifts from Ohmic to Schottky emission dominance across different resistance states. These findings exemplify the potential of SnOx-based devices for high-density data storage memory and revolutionary neuromorphic computing applications.

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“…Figure (e) demonstrates stable LRS and HRS achieved by controlling set current and reset cutoff voltage with a 10-mA current compliance to prevent hard breakdown. Variations in reset stop voltages (−1.0, −1.1, −1.2 V) at +1.5 V maintain multilevel storage capabilities, promising applications in artificial synapses . HRS at 0.1 V (Figure (f)) increases with higher reset voltages (−1.0, −1.1, −1.2 V), indicating distinct resistance levels and robust endurance across states. − …”

mentioning

confidence: 99%

“…45−47 Figure 2(g) and 2(h) depicts the resistive switching I−V characteristics of the TiO x N y /SnO x memristor with reset stop voltages controlled under current compliances of 5 mA and 10 mA, respectively. 43,48,49 While the set process exhibits abrupt behavior, achieving precise control over the set voltage for multilevel resistance states poses significant challenges in practical applications. In contrast, during the reset process, the current gradually decreases with incremental negative step voltages (−0.8 V to −1.2 V at 5 mA and −0.8 V to −1.4 V at 10 mA).…”

mentioning

confidence: 99%

Exploration of Analog Synaptic Plasticity and Convolutional Neural Network Simulation in Bilayer TiO_xN_y/SnO_x Memristor for Neuromorphic Systems

Ismail,

Kim,

Lim

et al. 2024

ACS Materials Lett.

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In this study, a TiN/SnO 2 /Pt sandwich structure is explored for its dual functionalities in electronic synapses and multistate memory. The SnO 2 layer is fabricated via reactive sputtering, leading to the formation of a TiN/TiO x N y /SnO x /Pt memristor. This configuration, confirmed by HRTEM and XPS analyses, exhibits several advantageous features: consistent bipolar nonvolatile switching at low operating voltages, endurance up to 500 cycles, an on/off ratio of ∼10 2 , and robust data retention. Set and reset times are approximately 300 and 400 ns, with energy consumption of 3.24 nJ and 3.26 nJ, respectively. The memristor achieves multilevel resistance states, simulating synaptic behaviors such as LTP/LTD, SADP, PPF, and PPD. Utilizing LTP and LTD data, CNN simulation achieved 91.3% recognition accuracy, surpassing the 70.5% accuracy of ANN simulation. These findings suggest the TiN/TiO x N y /SnO x /Pt memristor's potential for artificial neural network applications.T he rapid expansion of data-intensive systems like AI, big data, and Internet of Things (IoT) devices has increased the demand for advanced memristor devices with enhanced durability, speed, and energy efficiency. 1,2 Traditional memory technologies, such as flash memory, are struggling to meet these growing data storage and processing needs. 3 Researchers are thus exploring Resistive Random-Access Memory (RRAM) for its lower power consumption, faster operation, and stability. 4,5 RRAM, with its simple twoterminal capacitor structure, relies on specialized materials between electrodes for switching 6 Metal-oxide-semiconductor materials like TiO 2 , ZrO 2 , CeO 2 , HfO 2 , Al 2 O 3 , SiO 2 , ZnO, ZTO, and IGZO are promising due to their complementary metal-oxide-semiconductor (CMOS) compatibility and low leakage current, offering potential solutions for future dataintensive applications. 7−11 Tin oxide (SnO 2 ) is a promising semiconductor for photoconductive and electronic devices due to its wide bandgap (3.57 eV), high dielectric constant (∼9.86), stability, transparency, and conductivity. 12 Defects like interstitial metal ions and oxygen vacancies favor resistive switching. 13 SnO 2 Gibbs-free energy (−842.91 kJ/mol) supports stable low and high resistance states, and its Frenkel defect energy (7 eV) offers resilience to displacement. 14 SnO 2 is ideal for memristors, crucial for neuromorphic devices 12,15 that enable in-memory computing and mimic human behaviors

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Exploring conductance modulation and implementation of convolutional neural network in Pt/ZnO/Al2O3/TaN memristors for brain-inspired computing

Cited by 9 publications

References 64 publications

Resistive switching modulation by incorporating thermally enhanced layer in HfO₂-based memristor

Resistive switching modulation by incorporating thermally enhanced layer in HfO₂-based memristor

SnO2-Based Memory Device with Filamentary Switching Mechanism for Advanced Data Storage and Computing

Exploration of Analog Synaptic Plasticity and Convolutional Neural Network Simulation in Bilayer TiO_xN_y/SnO_x Memristor for Neuromorphic Systems

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Exploring conductance modulation and implementation of convolutional neural network in Pt/ZnO/Al2O3/TaN memristors for brain-inspired computing

Cited by 9 publications

References 64 publications

Resistive switching modulation by incorporating thermally enhanced layer in HfO2-based memristor

Resistive switching modulation by incorporating thermally enhanced layer in HfO2-based memristor

SnO2-Based Memory Device with Filamentary Switching Mechanism for Advanced Data Storage and Computing

Exploration of Analog Synaptic Plasticity and Convolutional Neural Network Simulation in Bilayer TiOxNy/SnOx Memristor for Neuromorphic Systems

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Resistive switching modulation by incorporating thermally enhanced layer in HfO₂-based memristor

Resistive switching modulation by incorporating thermally enhanced layer in HfO₂-based memristor

Exploration of Analog Synaptic Plasticity and Convolutional Neural Network Simulation in Bilayer TiO_xN_y/SnO_x Memristor for Neuromorphic Systems