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Thermally enhanced resistive switching in interface-engineered AgHfO3-x/Al2O3 memristors via trap depopulation for neuromorphic computing

AI Summary
  • HRS current decreases with temperature, producing a reversible ∼25-fold ON/OFF enhancement between 300 K and 400 K.
  • Thermal depopulation of shallow traps at a chemically sharp 5 nm Al2O3 interlayer induces trap-controlled SCLC, suppressing hopping leakage while preserving filamentary LRS.
  • Optimised heterostructure achieves ON/OFF ratio 349 ± 53, >10^3 cycles endurance, >25 000 s retention, ten analogue states, 97.74% MNIST accuracy.
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Mater Horiz. 2026 Sep 8. doi: 10.1039/d6mh00879h. Online ahead of print.

ABSTRACT

Thermal instability remains a fundamental bottleneck in oxide-based resistive random-access memory (RRAM), where elevated operating temperatures invariably increase the high-resistance-state (HRS) leakage current and collapse the ON/OFF switching window, severely limiting reliable deployment. Here, an Au/AgHfO3-x/Al2O3/FTO (fluorine-doped tin oxide) memristor is reported in which this paradigm is inverted. The HRS current anomalously decreases with increasing temperature, yielding a reversible ∼25-fold enhancement in the ON/OFF ratio from 300 K to 400 K. This counter-intuitive behavior originates from trap-controlled space charge-limited conduction (SCLC) at the chemically sharp 5 nm Al2O3 interlayer, where thermal energy depopulates shallow trap states, suppressing hopping-assisted leakage without disrupting filamentary conduction in the low-resistance state. The optimized heterostructure achieves a room-temperature ON/OFF ratio of 349 ± 53, endurance exceeding 103 bipolar switching cycles, retention over 25 000 s, and ten discrete analog conductance states spanning six orders of magnitude. Device-derived conductance states are directly used as synaptic weights in software-level simulations of handwritten-digit classification and image denoising on the MNIST benchmark. The handwritten-digit classification achieves 97.74% accuracy via transfer learning without implementation in a physical crossbar array or its associated peripheral circuitry. These results establish interface-engineered trap control as a design principle for thermally self-correcting memristors, a class where elevated temperature enhances rather than degrades performance, with direct relevance to neuromorphic hardware in thermally demanding environments.

PMID:42708446 | DOI:10.1039/d6mh00879h

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