Effect of swift heavy ion irradiation on the resistive random access memory performance of sputter deposited zinc rich zinc oxide thin films

Kaushik, Shikha; Singhal, Rahul; Meena, Ravi Kumar; Chawla, Amit Kumar; Avasthi, D.K.

doi:10.1016/j.mssp.2022.107108

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…Memristors can be prepared using many different resistance switching (RS) materials, such as oxides [6][7][8][9][10][11][12], sulphides [13,14], carbon [15,16], amorphous silicon [17,18] and organic materials [19,20]. Zinc oxide (ZnO) is a popular choice due to its low cost, wide band gap (3.37 eV), low synthesis temperature, controllable electrical behaviour, chemical stability, electrochemical activity, biocompatibility and eco-friendliness [21,22].…”

Section: Introductionmentioning

confidence: 99%

Synthesis of composite films for ZnO-based memristors with superior stability

Cao,

Liu,

et al. 2024

Mater. Res. Express

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Memristors have unique non-volatile characteristics that potentially can emulate biological synapses for applications in neural computing systems. However, unsolved random formation of conductive filaments in these devices can cause various unreliability problems. In this work, films of a composite of ZnO nanoparticles and carbon nanotubes were prepared as a functional layers for memristors by an in-situ growing strategy (ZnO@CNT-IS) using a straightforward high-temperature annealing treatment. This approach allowed for the formation of a high-quality films with uniform loading of ZnO nanoparticles on the carbon nanotubes, which contributed to a lower formation energy for oxygen vacancies and increased electron transfer rate. As a result, the memristors exhibited faster switching response speed, lower power consumption, and a stabilised switching ratio even after 2000 switching cycles. Based on the analog switching behaviour, the ZnO@CNT-IS-based devices showed significant biological synapse functions and plasticity, indicating their potential for high-density storage and neuromorphic computing.

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