Compact Modeling of Volatile‐Switching Electrochemical Metallization Memory Cells by Means of the Electromotive Force

Volatile‐switching electrochemical metallization memory (v‐ECM) cells exhibit properties valuable for selector and neuromorphic computing applications, such as artificial hardware neuron circuits. Upon applying a threshold voltage, v‐ECM cells switch to a low resistive state due to filament growth bridging an insulator. Subsequent relaxation occurs at voltages below the hold voltage through filament destabilization, showing a characteristic time scale dependent on the switching voltage amplitude. Recent v‐ECM models attempt to describe thresholding and relaxation, but none can reproduce dynamic switching and relaxation behavior accurately and consistently with a single model and parameter set. These shortcomings hinder large‐scale circuitry studies for applications. This work presents a comprehensive v‐ECM model incorporating electrocrystallization, electron‐transfer reactions at interfaces, and ionic migration as rate‐limiting ionic processes. The experimentally observed electromotive force is included as a counteracting force into the model, providing a complete picture of electrochemical metallization switching and explaining volatile effects, that is, relaxation. The model resolves the identified shortcomings, aligns with previous SET kinetics results, and is implemented in Verilog‐A for use with circuit simulators. A comparison with existing v‐ECM models demonstrates the benchmark established by the proposed approach.

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Publication Details

Journal
Advanced Intelligent Systems
Published
2025-10-26
DOI
https://doi.org/10.1002/aisy.202501018
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

Compact Modeling of Volatile‐Switching Electrochemical Metallization Memory Cells by Means of the Electromotive Force

Solomon Amsalu Chekol, Rana Walied Ahmad, Susanne Hoffmann‐Eifert, Stephan Menzel et al.
Advanced Intelligent Systems
Advanced Memory and Neural Computing
article

Compact Modeling of Volatile‐Switching Electrochemical Metallization Memory Cells by Means of the Electromotive Force

Solomon Amsalu Chekol, Rana Walied Ahmad, Susanne Hoffmann‐Eifert, Stephan Menzel, Julius Rasbach
article en

Abstract

Volatile‐switching electrochemical metallization memory (v‐ECM) cells exhibit properties valuable for selector and neuromorphic computing applications, such as artificial hardware neuron circuits. Upon applying a threshold voltage, v‐ECM cells switch to a low resistive state due to filament growth bridging an insulator. Subsequent relaxation occurs at voltages below the hold voltage through filament destabilization, showing a characteristic time scale dependent on the switching voltage amplitude. Recent v‐ECM models attempt to describe thresholding and relaxation, but none can reproduce dynamic switching and relaxation behavior accurately and consistently with a single model and parameter set. These shortcomings hinder large‐scale circuitry studies for applications. This work presents a comprehensive v‐ECM model incorporating electrocrystallization, electron‐transfer reactions at interfaces, and ionic migration as rate‐limiting ionic processes. The experimentally observed electromotive force is included as a counteracting force into the model, providing a complete picture of electrochemical metallization switching and explaining volatile effects, that is, relaxation. The model resolves the identified shortcomings, aligns with previous SET kinetics results, and is implemented in Verilog‐A for use with circuit simulators. A comparison with existing v‐ECM models demonstrates the benchmark established by the proposed approach.

Advanced Intelligent Systems
Forschungszentrum Jülich (DE), Ernst Ruska Centre (DE), Jülich Aachen Research Alliance (DE)
Openalex Percentile: Top 69%
Advanced Memory and Neural Computing
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Compact Modeling of Volatile‐Switching Electrochemical Metallization Memory Cells by Means of the Electromotive Force — Solomon Amsalu Chekol, Rana Walied Ahmad, et al. · Advanced Intelligent Systems (2025) | TGRS Research Map | TGRS