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Updated: Oct 26, 2026

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
Defect and Ion-Migration Regulation in MAPbI3 Memristors via ZIF-67 Modification toward Stable Resistive Switching
Xiaochen Zhou1, Wei Qian1, Dawei Cao2
1School of Mechanical Engineering, Jiangsu University, Zhenjiang, Jiangsu212013, China.
Abstract:
High-performance resistive random-access memory (RRAM) based on organic-inorganic hybrid perovskites remains limited by intrinsic vacancy defects and uncontrolled ion migration, which usually induce random conductive filaments (CFs) formation and poor device stability. Herein, a defect-engineering strategy is proposed by using a cobalt-based zeolitic imidazolate framework (ZIF-67) as a multifunctional additive to synergistically regulate the MAPbI3 film through Co ion incorporation and ligand-assisted defect passivation. Structural, morphological, optical/electrical, and conduction analyses reveal that the introduction of ZIF-67 improves film quality by enlarging grains and reducing grain boundaries, while regulating Pb-vacancy-related defect states and introducing a stronger Co-I bond. These effects suppress disordered vacancy migration and promote stable CFs formation during resistive switching (RS). Consequently, the Cu/ZIF67-MAPbI3/p-Si device exhibits enhanced RS performance, including a high resistance ratio (>103), low operating power consumption (≈1.01 mW), endurance over 2300 cycles, and improved switching uniformity with reduced coefficients of variation for both high-resistance state and low-resistance state. Benefiting from its continuously tunable conductance, the device successfully emulates synaptic plasticity behaviors, including paired-pulse facilitation (PPF), spike-timing-dependent plasticity (STDP), and long-term potentiation/depression (LTP/LTD). Moreover, an experimentally calibrated multilayer perceptron achieves Modified National Institute of Standards and Technology (MNIST) handwritten digit recognition with an accuracy of 97.54% and a last loss of 3.35%, demonstrating the potential of ZIF67-MAPbI3-based RRAM device for artificial synapses and neuromorphic computing.
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