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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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Bifacially Engineered Perovskite-Based Synaptic Memristors Achieve High Linearity and Symmetricity for Accurate and
Jang Woo Lee1, Liang Cai1, Jeong-Seok Nam1,2
1Department of Nano Engineering, Department of Nano Science and Technology, SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University (SKKU), Suwon, 16419, Republic of Korea.
Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|August 20, 2025
Summary
Bifacially engineered metal halide perovskite (MHP) memristors achieve high linearity and symmetricity. This breakthrough enhances reliability for neuromorphic computing applications.
Area of Science:
- Materials Science
- Electronics
- Nanotechnology
Background:
- Metal halide perovskite (MHP)-based memristors face challenges in linearity and symmetricity due to abrupt switching and irregular conductive filament (CF) pathways.
- Existing MHP memristors struggle to achieve simultaneous high linearity, symmetricity, and reliability.
Purpose of the Study:
- To report bifacially engineered MHP memristors with simultaneous high linearity, symmetricity, and reliability.
- To investigate the impact of top-surface and bottom-side passivation on MHP memristor performance.
Main Methods:
- Top-surface passivation using phenylethylammonium iodide (PEAI) to form an ultrathin 2D perovskite layer (PEA2PbI4).
- Bottom-side PEAI treatment to alleviate tensile strain and enhance perovskite grain uniformity.
- Characterization of memristor performance, including I_on/I_off ratio, endurance, data retention, and neuromorphic computing simulations.
Main Results:
- Bifacially engineered MHP memristors exhibit simultaneous high linearity, symmetricity, and reliability.
- Top-surface passivation promotes gradual switching and suppresses ion migration, enhancing linearity.
- Bottom-side treatment leads to stable CF rupture and improved linearity and symmetricity.
- Achieved an I_on/I_off ratio of 3.67 × 10^5, endurance >11,000 cycles, and data retention >10^5 s.
- Demonstrated high classification accuracies (92.60% on CIFAR-10, 94.53% on MNIST) in simulations.
Conclusions:
- Bifacial engineering is an effective strategy to overcome challenges in MHP-based memristors.
- The developed memristors show significant potential for next-generation hardware-based neuromorphic computing applications.
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