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Updated: Feb 26, 2026

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
Sustainable All-Inorganic Double Perovskite Memristors Enabling Synaptic Learning and Cognitive Emulation
Jnanaprakash Dash1, Sandaap Sathyanarayana1, Muhammed Sahad E1
1eNDR Laboratory, School of Physics, IISER Thiruvananthapuram, Trivandrum, Kerala, India.
Abstract:
Lead-free halide perovskites are emerging as sustainable alternatives for next-generation electronic and neuromorphic technologies. In this work, we present a memristive device based on cooling-induced crystallization followed by thin-film deposition of Cs2AgBiBr6 (CABB), a stable, non-toxic all-inorganic double perovskite. Uniform CABB thin-film of roughness about 3.5 nm, integrated in ITO/CABB/Au structures, exhibits reproducible analog resistive switching with well-defined pinched hysteresis, validated by macroscopic I-V measurements and nanoscale c-AFM/STM-based spectroscopy results. Comprehensive structural and compositional analyses confirm phase-pure crystallization, while impedance spectroscopy and localized current mapping images with the scanning tunneling spectroscopy (STS) reveal a filamentary switching mechanism precisely driven by reversible Ag+ ion migration. Beyond non-volatile memory, the devices successfully emulate essential synaptic behaviors, including excitatory postsynaptic current, paired-pulse facilitation, and long-term potentiation/depression with tunable weight states, enabling robust plasticity. Notably, pattern recognition with trained spiking neural networks yields > 95% accuracy on the MNIST dataset, surpassing practical application benchmarks. Furthermore, sequenced pulse training enables adaptive learning and associative memory, evidencing Pavlovian conditioning at the device level. This multifunctionality highlights CABB as a promising and eco-friendly perovskite material that unites reliable resistive switching, neuromorphic learning, and cognitive adaptability, paving the way toward compact, energy-efficient, and sustainable brain-inspired hardware building blocks.
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