Precursor Ratio-Controlled Chloride Vacancies in Lead-Free KCuCl3-x Memristors for Forming-Free Gradual Resistive Switching and Neuromorphic Learning
Abstract
Defect engineering of memristive materials offers a powerful strategy for regulating ionic transport and analog resistive switching required for neuromorphic computing. Here, we report a wet chemical synthesis route for chloride-vacancy (VCl) engineering in lead-free all-inorganic KCuCl3-x thin films and establish a direct correlation between defect concentration, electronic structure, transport dynamics, and neuromorphic performance. Controlled precursor-ratio variation systematically tunes the VCl concentration in KCuCl3-x thin films, driving preferential vacancy formation at the terminal chloride sites, band-gap narrowing, and enhanced electronic disorder. W/KCuCl3-x/FTO memristors exhibit stable bipolar VCM-type resistive switching, with increasing VCl concentration promoting forming-free operation. An optimal VCl concentration (~ 1.3*10^21 cm^-3) provides a balance between ionic mobility and defect controllability, resulting in highly linear potentiation/depression characteristics and superior pulse endurance (>104 cycles). When the experimentally measured conductance characteristics of the optimized memristor are implemented as synaptic weights in artificial neural network simulations, recognition accuracies of 98.96% and 91.82% are achieved on the MNIST and Fashion-MNIST datasets, respectively. These findings establish precursor ratio-controlled VCl engineering as an effective strategy for simultaneously optimizing ionic transport, analog switching, and neuromorphic learning in lead-free halide perovskite memristors, providing a general materials design principle for energy-efficient neuromorphic electronics.
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BibTeX
@misc{mhaskar2026precursor,
title = {{Precursor Ratio-Controlled Chloride Vacancies in Lead-Free KCuCl3-x Memristors for Forming-Free Gradual Resistive Switching and Neuromorphic Learning}},
author = {Chinmayee Mandar Mhaskar and Sangeeta Roy Chaudhuri and Ayan Roy Chaudhuri},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.35436},
eprint = {2610.35436},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.35436},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Precursor Ratio-Controlled Chloride Vacancies in Lead-Free KCuCl3-x Memristors for Forming-Free Gradual Resistive Switching and Neuromorphic Learning
AU - Chinmayee Mandar Mhaskar
AU - Sangeeta Roy Chaudhuri
AU - Ayan Roy Chaudhuri
PY - 2026
DA - 2026/10/26
PB - arXiv
DO - 10.48550/arxiv.2610.35436
UR - https://arxiv.org/abs/2610.35436
AB - Defect engineering of memristive materials offers a powerful strategy for regulating ionic transport and analog resistive switching required for neuromorphic computing. Here, we report a wet chemical synthesis route for chloride-vacancy (VCl) engineering in lead-free all-inorganic KCuCl3-x thin films and establish a direct correlation between defect concentration, electronic structure, transport dynamics, and neuromorphic performance. Controlled precursor-ratio variation systematically tunes the VCl concentration in KCuCl3-x thin films, driving preferential vacancy formation at the terminal chloride sites, band-gap narrowing, and enhanced electronic disorder. W/KCuCl3-x/FTO memristors exhibit stable bipolar VCM-type resistive switching, with increasing VCl concentration promoting forming-free operation. An optimal VCl concentration (~ 1.3*10^21 cm^-3) provides a balance between ionic mobility and defect controllability, resulting in highly linear potentiation/depression characteristics and superior pulse endurance (>104 cycles). When the experimentally measured conductance characteristics of the optimized memristor are implemented as synaptic weights in artificial neural network simulations, recognition accuracies of 98.96% and 91.82% are achieved on the MNIST and Fashion-MNIST datasets, respectively. These findings establish precursor ratio-controlled VCl engineering as an effective strategy for simultaneously optimizing ionic transport, analog switching, and neuromorphic learning in lead-free halide perovskite memristors, providing a general materials design principle for energy-efficient neuromorphic electronics.
ER -