Reconfigurable High-Performance Memristors Based on Few-Layer High-κ Dielectric Bi2SeO5 for Neuromorphic Computing

Reconfigurable High-Performance Memristors Based on Few-Layer High-κ Dielectric Bi2SeO5 for Neuromorphic Computing

Advanced Functional Materials

10.1002/adfm.202514338

https://doi.org/10.1002/adfm.202514338

Fang Yang, Yuwei Xiong, Zhaofu Chen, Shizheng Wang, Yinan Wang, Zhihao Qu, Weiwei Zhao, Jiayi Li, Kuibo Yin, Zhenhua Ni, Jing Wu, Diing Shenp Ang, DongZhi Chi, Xin Ju, Junpeng Lu, Hongwei Liu

memristor, high-κ, Bi2SeO5, neuromorphic device, reservoir computing

21 July 2025

Yang, F., Xiong, Y., Chen, Z., Wang, S., Wang, Y., Qu, Z., Zhao, W., Li, J., Yin, K., Ni, Z., Wu, J., Ang, D. shenp, Chi, D., Ju, X., Lu, J., & Liu, H. (2025). Reconfigurable High‐Performance Memristors Based on Few‐Layer High‐κ Dielectric Bi2SeO5 for Neuromorphic Computing. Advanced Functional Materials. Portico. https://doi.org/10.1002/adfm.202514338

AbstractMemristors are pivotal for energy‐efficient artificial intelligence (AI) hardware, potentially eliminating the von Neumann bottleneck by in‐memory realizations of synaptic operations. However, the dynamic requirements of neuromorphic computing on specific electronic devices pose reliability and universality challenges, limiting progress toward more widely applicable computing platforms. Here, a 2D high‐κ dielectric‐based memristor with the desired reconfigurable resistive switching behavior is successfully demonstrated. Utilizing a few layered Bi2SeO5 possessing excellent electrical insulation properties as the switching medium, the device features a low operating voltage (≈0.5 V), low operation current (10 pA), long memory retention (>103 s), large switching window (≈108), steep slope (<1 mV dec−1), fast switching speed (40 ns), and low energy dissipation (≈1 pJ). The switching characteristics between volatile and non‐volatile memory can be achieved on demand by regulating compliance currents, offering the possibility of implementing multiple neural computational primitives. A simulated convolutional neural network (CNN) based on long‐term potentiation/depression (LTP/D) achieves 85% accuracy in complex image recognition. Furthermore, MNIST and fashion‐MNIST recognition with built reservoir computing (RC) utilizing volatile behaviors reach 97% and 85% accuracy, respectively. This work opens new opportunities for 2D high‐κ dielectrics in next‐generation AI hardware with enhanced energy efficiency and computational versatility.

Publisher Copyright

This research / project is supported by the N.A. - National Key Research and Development Program
Grant Reference no. : 2024YFB3211701

This research / project is supported by the N.A. - National Natural Science Foundation of China
Grant Reference no. : 62404105, 12274234, T2222011

This research / project is supported by the National Research Foundation - Competitive Research Program
Grant Reference no. : NRF-CRP24-2020-0002

This research / project is supported by the Singapore Ministry of Education under research grants - NA
Grant Reference no. : RG67/23, RG70/24

This is the peer reviewed version of the following article: Yang, F., Xiong, Y., Chen, Z., Wang, S., Wang, Y., Qu, Z., Zhao, W., Li, J., Yin, K., Ni, Z., Wu, J., Ang, D. shenp, Chi, D., Ju, X., Lu, J., & Liu, H. (2025). Reconfigurable High‐Performance Memristors Based on Few‐Layer High‐κ Dielectric Bi2SeO5 for Neuromorphic Computing. Advanced Functional Materials. Portico. https://doi.org/10.1002/adfm.202514338 , which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.

https://oar.a-star.edu.sg/communities-collections/articles/22213

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Institute of Materials Research and Engineering