BiFeO3/SrTiO3 superlattice-like based ferroelectric memristors with pronounced artificial synaptic plasticity

Memristors are attracting widespread attention for their multilevel storage capacity and synaptic learning ability. Ferroelectric memristors, with resistance switching based on polarization reversal, offer uniform threshold voltage and analog resistance modulation. By preparing superlattice-like fer...

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Veröffentlicht in:Journal of alloys and compounds 2024-12, Vol.1007, p.176364, Article 176364
Hauptverfasser: Yang, Zeou, Huang, Xiaozhong, Wang, Ze, Chen, Kai, Ma, Bingyang, Shang, Hailong, Hu, Hailong, Yue, Jianling
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Sprache:eng
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Zusammenfassung:Memristors are attracting widespread attention for their multilevel storage capacity and synaptic learning ability. Ferroelectric memristors, with resistance switching based on polarization reversal, offer uniform threshold voltage and analog resistance modulation. By preparing superlattice-like ferroelectric films, the ferroelectric properties can be significantly enhanced, thereby improving the memristive characteristics. In this study, the constructed BiFeO3/SrTiO3 (BFO/STO) superlattice-like structure increases the remanent polarization by approximately 200 % compared to a single-layer BFO structure, as well as the dramatically enhanced resistance switching performance. Notably, the Pt/(BFO/STO)2/NSTO device shows superior storage performance, with extended endurance (1000 cycles), reliable retention (10000 seconds) and fundamental synaptic behaviors being demonstrated. Finally, simulations for handwritten digit recognition classification, involving modulation of conductance to vary synaptic weights, achieve an impressive accuracy of 87.46 %, showing great promising for applications in information storage and neuromorphic computing. [Display omitted] •BiFeO3/SrTiO3 superlattice-like structure improves the remanent polarization and memristive performance significantly.•The Pt/(BiFeO3/SrTiO3)2/NSTO device shows superior storage performance and pronounced artificial synaptic plasticity.•An artificial neural network was constructed to simulate handwritten digit recognition with an accuracy of 87.46 %.
ISSN:0925-8388
DOI:10.1016/j.jallcom.2024.176364