High on/off ratio SiO<sub>2</sub>-based memristors for neuromorphic computing: understanding the switching mechanisms through theoretical and electrochemical aspects

Qin, Fei; Zhang, Yuxuan; Guo, Ziqi; Park, Tae Joon; Park, Hongsik; Kim, Chung Soo; Park, Jeongmin; Fu, Xingyu; No, Kwangsoo; Song, Han Wook; Ruan, Xiulin; Lee, Sunghwan

doi:10.1039/d3ma01142a

Cited by 3 publications

(1 citation statement)

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“…The materials include organics [22], perovskites [23][24][25][26], polymers [27], binary oxides [28], and other 2D materials [29,30]. Some of the widely used materials of memristors for neuromorphic computation are TiO x [31], TaO x [32], HfO x [33], SiO x [34,35] and IGZO [36]. Integrating two-dimensional (2D) materials into electronic circuits holds the promise of moving beyond the limitations of traditional metal-oxide-based technologies, especially in light of the extensive research on using memristors in neuromorphic circuits [37].…”

Section: Methodsmentioning

confidence: 99%

Review of memristor based neuromorphic computation: opportunities, challenges and applications

2024

Eng. Res. Express

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The memristor is regarded as one of the promising possibilities for next-generationcomputing systems due to its small size, easy construction, and low power consumption.Memristor-based novel computing architectures have demonstratedconsiderable promise for replacing or enhancing traditional computing platformsthat encounter difficulties in the big-data era. Additionally, the striking resemblancebetween the mechanisms governing the programming of memristance andthe manipulation of synaptic weight at biological synapses may be used to createunique neuromorphic circuits that function according to biological principles.Nevertheless, getting memristor-based computing into practice presentsmany technological challenges. This paper reviews the potential for memristorresearch at the device, circuit, and system levels, mainly using memristors todemonstrate neuromorphic computation. Here, the common issues obstructingthe development and widespread use of memristor-based computing systems arealso carefully investigated. This study speculates on the prospective applicationsof memristors, which can potentially transform the field of electronics altogether.

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Section: Methodsmentioning

confidence: 99%

Review of memristor based neuromorphic computation: opportunities, challenges and applications

2024

Eng. Res. Express

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Aromaticity‐Dependent Memristive Switching

Cechosz,

Alluhaibi,

Mazur

et al. 2024

Adv Elect Materials

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In recent years, memristors have drawn attention as non‐volatile memory devices for advanced computing engineering. The features of memristors, such as hysteresis, high resistance state to low resistance state ratio, retention time, etc., are determined by the material, structure of the device, the switching mechanism, and the kinetics resulting from the characteristics of the materials. Here, a resistive switching is proposed based on the aromaticity change of the material. A device has been built based on tetracyclone that undergoes the transition from an antiaromatic to an aromatic state upon applied potential. This phenomenon results in the presence of two distinguishable, and more importantly, stable resistive states of the memristor. On the basis of theoretical and experimental investigation, it is demonstrated that the working principle of the device depends on the redox change of the molecules. This study provides the foundation for a new kind of memristive switching mechanism: the formation of virtual conductivity paths. They can be a key to improving the performance of semiconductor memory devices in crucial factors such as stability, reversibility, and nonvolatility because these paths are related to electron delocalization and do not involve any significant structural changes in the device (e.g., diffusion).

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Ni3C nanosheets and PVA nanocomposite based memristor for low-cost and flexible non-volatile memory devices

Koncha,

Das,

Badhulika

2024

Materials Science in Semiconductor Processing

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High on/off ratio SiO₂-based memristors for neuromorphic computing: understanding the switching mechanisms through theoretical and electrochemical aspects

Abstract: Memristors have emerged as promising elements for brain-inspired computing applications, yet the understanding of their switching mechanisms, particularly in valence change memristors, remains a topic of ongoing debate. We report...

Cited by 3 publications

References 81 publications

Review of memristor based neuromorphic computation: opportunities, challenges and applications

Review of memristor based neuromorphic computation: opportunities, challenges and applications

Aromaticity‐Dependent Memristive Switching

Ni3C nanosheets and PVA nanocomposite based memristor for low-cost and flexible non-volatile memory devices

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High on/off ratio SiO2-based memristors for neuromorphic computing: understanding the switching mechanisms through theoretical and electrochemical aspects

Abstract: Memristors have emerged as promising elements for brain-inspired computing applications, yet the understanding of their switching mechanisms, particularly in valence change memristors, remains a topic of ongoing debate. We report...

Cited by 3 publications

References 81 publications

Review of memristor based neuromorphic computation: opportunities, challenges and applications

Review of memristor based neuromorphic computation: opportunities, challenges and applications

Aromaticity‐Dependent Memristive Switching

Ni3C nanosheets and PVA nanocomposite based memristor for low-cost and flexible non-volatile memory devices

Contact Info

Product

Resources

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High on/off ratio SiO₂-based memristors for neuromorphic computing: understanding the switching mechanisms through theoretical and electrochemical aspects