Deterministic Memristive Polarization Switching in Relaxor Ferroelectrics

The burgeoning field of neuromorphic computing demands ferroelectric materials exhibiting memristive multilevel polarization to enable high-density memory storage and brain-inspired devices. Here, it is demonstrated deterministic multilevel polarization in a relaxor ferroelectric-rhombohedral PMN-PT through domain engineering, achieving precise control over stepwise domain switching. Using reciprocal space mapping (RSM) and in situ second-harmonic generation (SHG), it is shown that optimized electric pulses guide multi-domain switching to a 4R engineered domain state, stabilizing memristive behavior. In situ characterization techniques, including transmission electron microscopy and piezoelectric force microscopy, reveal that the memristive behavior arises from local domain rearrangements rather than crystal structure transitions. The resulting multilevel polarization states with up to 20 levels exhibit robust retention (>10<sup>4</sup> s) and fatigue resistance (>10<sup>5</sup> cycles) while maintaining high polarizability. Phase-field simulations corroborate these findings, offering microscopic insights into the repeatability of multilevel polarization. This approach overcomes the bistable limitations of conventional ferroelectrics, enabling stable multilevel polarization states that modulate mechanical, electrical, and optical properties. This results provide a versatile strategy for next-generation ferroelectric memristors, advancing neuromorphic and reconfigurable computing with minimal compromise in ferroelectric performance.

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Publication Details

Journal
Advanced Materials
Published
2025-07-24
DOI
https://doi.org/10.1002/adma.202504143
Citations
2
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
1.46

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article

Deterministic Memristive Polarization Switching in Relaxor Ferroelectrics

Chongjun He, Chenguang Deng, Linfeng Fei, Xiaoming Shi et al.
2 citations
Advanced Materials
Advanced Memory and Neural Computing
1.46
article

Deterministic Memristive Polarization Switching in Relaxor Ferroelectrics

Chongjun He, Chenguang Deng, Linfeng Fei, Xiaoming Shi, Houbing Huang, Yiyang Wen, Guangren Wang, Zhenping Wu, Yang Zhang, Weiwei Liu, Fan Zhang, Hongda Ren, Yilin Cao, Xiaona Du
article en
2 citations

Abstract

The burgeoning field of neuromorphic computing demands ferroelectric materials exhibiting memristive multilevel polarization to enable high-density memory storage and brain-inspired devices. Here, it is demonstrated deterministic multilevel polarization in a relaxor ferroelectric-rhombohedral PMN-PT through domain engineering, achieving precise control over stepwise domain switching. Using reciprocal space mapping (RSM) and in situ second-harmonic generation (SHG), it is shown that optimized electric pulses guide multi-domain switching to a 4R engineered domain state, stabilizing memristive behavior. In situ characterization techniques, including transmission electron microscopy and piezoelectric force microscopy, reveal that the memristive behavior arises from local domain rearrangements rather than crystal structure transitions. The resulting multilevel polarization states with up to 20 levels exhibit robust retention (>10<sup>4</sup> s) and fatigue resistance (>10<sup>5</sup> cycles) while maintaining high polarizability. Phase-field simulations corroborate these findings, offering microscopic insights into the repeatability of multilevel polarization. This approach overcomes the bistable limitations of conventional ferroelectrics, enabling stable multilevel polarization states that modulate mechanical, electrical, and optical properties. This results provide a versatile strategy for next-generation ferroelectric memristors, advancing neuromorphic and reconfigurable computing with minimal compromise in ferroelectric performance.

Advanced MaterialsVol. 37(41)
Beijing University of Posts and Telecommunications (CN), Nanchang University (CN), Nankai University (CN), Ministry of Industry and Information Technology (CN), University of Science and Technology Beijing (CN), Nanjing University of Aeronautics and Astronautics (CN), Tsinghua University (CN)
National Natural Science Foundation of China, State Key Laboratory of Information Photonics and Optical Communications, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 16%
Advanced Memory and Neural Computing
1.46
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