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Enhanced Giant Ferroelectric Tunneling Electroresistance in 2D Ruddlesden-Popper Oxides
Hui Zeng1, Yao Wen1, Yangyuan Tu1
1Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, and School of Physics and Technology, Wuhan University, Wuhan 430072, China.
ACS Nano
|September 8, 2025
Summary
Ferroelectric tunnel junctions (FTJs) show a massive 4-order enhancement in tunneling electrosistance (TER) ratio using bismuth ferrite and 2D Ruddlesden-Popper oxide Bi2FeO4. This breakthrough enables advanced in-memory computing and artificial neural networks.
Area of Science:
- Materials Science
- Condensed Matter Physics
- Nanotechnology
Background:
- Ferroelectric tunnel junctions (FTJs) are crucial for 2D electronic devices in data storage and neural networks.
- Enhanced tunneling electrosistance (TER) ratio is vital for precise synaptic weight modulation, improving neural network stability and accuracy.
Purpose of the Study:
- To significantly enhance the TER ratio in FTJs.
- To explore the potential of 2D Ruddlesden-Popper oxides for advanced electronic applications.
- To demonstrate the efficacy of these FTJs in artificial neural synapses for image recognition and dehaze processing.
Main Methods:
- Fabrication of large-scale heterostructures combining bismuth ferrite with 2D Ruddlesden-Popper oxide Bi2FeO4.
- Characterization of the tunneling electrosistance (TER) ratio.
- Demonstration of image recognition and dehaze processing using artificial neural synapses.
Main Results:
- Observed a pronounced enhancement in the TER ratio by over 4 orders of magnitude.
- Achieved a remarkable TER value of 7.8 × 106 due to significant Schottky barrier height differences.
- Demonstrated successful image recognition and dehaze processing, showcasing the enhanced conductance contrast.
Conclusions:
- Giant barrier height modulation is achievable through 2D Ruddlesden-Popper oxides.
- These FTJs offer a facile technique for high-density in-memory computing applications.
- The enhanced TER ratio significantly improves the performance of artificial neural synapses.
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