journal article·2026-06-23

Precursor-Directed Microstructure Engineering of CuS for Tunable Charge Transport and Memristive Performance

Dongheng Jiang, Huanxin Ren, Feng Yang, Yongfang Jia, Jianwei Sun, Keyu Liu, Lin Wang, Yaojie Yang, Hanyu Kong, Wenqin Zhang, Kunling Du, Haoran Jia, Yuxuan Jia, Bai Sun, Wensheng Song

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Abstract

The performance of memristive devices is fundamentally determined by the microstructure of the dielectric layer and the associated charge-transport mechanisms. Here, we report a precursor-directed microstructure engineering strategy to modulate copper sulfide (CuS) films through a room-temperature chemical bath deposition process. By simply switching the copper precursor from CuCl 2 to CuSO 4, the CuS morphology evolves from dense microspheres to a nanoparticle-assembled (NPA) porous architecture. This structural transformation induces a transition in the dominant transport mechanism from Schottky emission to Fowler−Nordheim (FN) tunneling. Systematic electrical analysis reveals a transition voltage of −0.52 V, corresponding to the onset of FN tunneling. The ratio between the transition voltage and the extracted barrier height (0.62 eV) falls within the theoretical range of 0.8−1.2, derived by equating direct tunneling and FN tunneling current densities under the Wentzel−Kramers−Brillouin (WKB) approximation. Fitting results yield a barrier width of ∼1.8 nm and trap energy depths of 0.15−0.18 eV, providing quantitative evidence for tunneling-dominated and trap-assisted transport in the NPA structure. Furthermore, modulation of the bottom electrode work function enables control over switching polarity and rectification direction. Neural network simulations based on experimentally measured synaptic characteristics achieve 93% accuracy on the Fashion-MNIST dataset within 30 training epochs. This work establishes a quantitative structure−mechanism−function correlation and provides a physically grounded framework for the rational design of customizable neuromorphic devices.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Copper-based nanomaterials and applications · Nanomaterials and Printing Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Physical Science and Technology

Southwest Jiaotong University

School of Integrated Circuit Science and Engineering

Sichuan Polytechnic University

School of Materials Science and Engineering, Southwest Jiaotong University

The Institute of Smart City and Intelligent Transportation

Frontier Institute of Science and Technology and Interdisciplinary Research Center of Frontier Science and Technology Xi’an Jiaotong University

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