Bipolar and rectifying resistive switching dynamics in E-beam evaporated SnOx based memristor

Singh, Chandra Prakash; Singh, Vivek Pratap; Ranjan, Harsh; Pandey, Saurabh Kumar

doi:10.1016/j.ceramint.2023.11.186

Ceramics International

2024

DOI: 10.1016/j.ceramint.2023.11.186

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Bipolar and rectifying resistive switching dynamics in E-beam evaporated SnOx based memristor

Chandra Prakash Singh,

Vivek Pratap Singh,

Harsh Ranjan

et al.

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Cited by 8 publications

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“…Studies have shown enhanced memristor performance with SnO 2 . Singh et al , noted distinct bipolar behaviors in SnO x films with Ag and Pt electrodes. Jin et al improved switching stability with nitrogen annealing, achieving devices with over 10∧5 s retention time and consistent resistance over 100 cycles.…”

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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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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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Experimental demonstration and analysis of crossbar array memristor for brain-inspired computing

Singh,

Ranjan

et al. 2024

Applied Materials Today

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Fabrication perspective of Fe3O4-based cross-cell memristive device for synaptic applications

Singh,

Ranjan

et al. 2024

Current Applied Physics

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Bipolar and rectifying resistive switching dynamics in E-beam evaporated SnOx based memristor

Cited by 8 publications

References 32 publications

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

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

Experimental demonstration and analysis of crossbar array memristor for brain-inspired computing

Fabrication perspective of Fe3O4-based cross-cell memristive device for synaptic applications

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Bipolar and rectifying resistive switching dynamics in E-beam evaporated SnOx based memristor

Cited by 8 publications

References 32 publications

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

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

Experimental demonstration and analysis of crossbar array memristor for brain-inspired computing

Fabrication perspective of Fe3O4-based cross-cell memristive device for synaptic applications

Contact Info

Product

Resources

About

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

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