Resistive switching in tunable Pt–C granular metals

The charge-transport properties of nanogranular metals, comprising arrays of metallic nanoparticles embedded in an insulating matrix, are governed by the average tunneling conductance g between neighboring particles. In the weak-coupling limit (g ≪ 1), a transition from a high-resistive state (HRS) to a low-resistive state (LRS) occurs for bias voltages exceeding the global Coulomb-blockade threshold Vt. In this work, we investigate the charge-transport properties of Pt–C granular metals fabricated by focused electron beam induced deposition, across a bias voltage range spanning from the low-voltage regime (V ≪ Vt) up to the global Coulomb-blockade threshold (V ∼ Vt). Current–voltage characteristics (I–V), measured between room temperature and 2 K, reveal ohmic behavior at low bias voltages and field-assisted ohmic conduction at intermediate biases. Approaching Vt, the I–V curve exhibits a steep, non-linear increase in current, indicative of an avalanche-like charge carrier multiplication process. Above Vt, ohmic behavior is again observed. The resistive switching mechanism is hysteretic, exhibiting distinct transition voltages for the HRS-to-LRS and LRS-to-HRS transitions. Temperature-dependent conductivity measurements for V ≪ Vt reveal transport characterized by correlated variable-range hopping (c-VRH), indicative of cotunneling. At higher bias voltages, a field-assisted c-VRH regime is observed at low temperatures, which culminates in an abrupt transition to a low-resistance state at Vt. Finally, as the coupling strength between particles in Pt–C can be precisely controlled via electron curing, the switching temperature between HRS and LRS is adjustable from a few Kelvin to 60 K.

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Publication Details

Journal
Journal of Applied Physics
Published
2026-05-11
DOI
https://doi.org/10.1063/5.0323104
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

Resistive switching in tunable Pt–C granular metals

Fabrizio Porrati, Michael Huth
Journal of Applied Physics
Advanced Memory and Neural Computing
article

Resistive switching in tunable Pt–C granular metals

Fabrizio Porrati, Michael Huth
article en

Abstract

The charge-transport properties of nanogranular metals, comprising arrays of metallic nanoparticles embedded in an insulating matrix, are governed by the average tunneling conductance g between neighboring particles. In the weak-coupling limit (g ≪ 1), a transition from a high-resistive state (HRS) to a low-resistive state (LRS) occurs for bias voltages exceeding the global Coulomb-blockade threshold Vt. In this work, we investigate the charge-transport properties of Pt–C granular metals fabricated by focused electron beam induced deposition, across a bias voltage range spanning from the low-voltage regime (V ≪ Vt) up to the global Coulomb-blockade threshold (V ∼ Vt). Current–voltage characteristics (I–V), measured between room temperature and 2 K, reveal ohmic behavior at low bias voltages and field-assisted ohmic conduction at intermediate biases. Approaching Vt, the I–V curve exhibits a steep, non-linear increase in current, indicative of an avalanche-like charge carrier multiplication process. Above Vt, ohmic behavior is again observed. The resistive switching mechanism is hysteretic, exhibiting distinct transition voltages for the HRS-to-LRS and LRS-to-HRS transitions. Temperature-dependent conductivity measurements for V ≪ Vt reveal transport characterized by correlated variable-range hopping (c-VRH), indicative of cotunneling. At higher bias voltages, a field-assisted c-VRH regime is observed at low temperatures, which culminates in an abrupt transition to a low-resistance state at Vt. Finally, as the coupling strength between particles in Pt–C can be precisely controlled via electron curing, the switching temperature between HRS and LRS is adjustable from a few Kelvin to 60 K.

Journal of Applied PhysicsVol. 139(18)
Goethe University Frankfurt (DE)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 9%
Advanced Memory and Neural Computing
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