This title appears in the Scientific Report :
2023
Please use the identifier:
http://dx.doi.org/10.34734/FZJ-2024-00604 in citations.
Unveiling the relaxation dynamics of Ag/HfO2 based diffusive memristors for use in neuromorphic computing
Unveiling the relaxation dynamics of Ag/HfO2 based diffusive memristors for use in neuromorphic computing
The rapid growth in volume and complexity of data and transfer, driven by advancements in information technologies such as artificial intelligence (AI), cloud computing, big data, and machine learning, is placing significant demands on computation power and speed. Traditional computing architectures...
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Personal Name(s): | Chekol, Solomon (Corresponding author) |
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Contributing Institute: |
Elektronische Materialien; PGI-7 |
Imprint: |
Jülich
Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag
2023
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Physical Description: |
x, 185 |
Dissertation Note: |
Dissertation, RWTH Aachen University, 2023 |
ISBN: |
978-3-95806-729-5 |
DOI: |
10.34734/FZJ-2024-00604 |
Document Type: |
Book Dissertation / PhD Thesis |
Research Program: |
Verbundprojekt: Neuro-inspirierte Technologien der künstlichen Intelligenz für die Elektronik der Zukunft - NEUROTEC II - Memristive Materials and Devices |
Series Title: |
Schriften des Forschungszentrums Jülich Reihe Information / Information
101 |
Link: |
OpenAccess |
Publikationsportal JuSER |
The rapid growth in volume and complexity of data and transfer, driven by advancements in information technologies such as artificial intelligence (AI), cloud computing, big data, and machine learning, is placing significant demands on computation power and speed. Traditional computing architectures are facing challenges in meeting these demands due to the Von Neumann bottleneck, which limits the data transfer rate between the memory and the central processing unit and causes high energy consumption.Today, neuromorphic computing (NC) concepts that mimic the structure and function of the biological brain are gaining popularity as they promise energy-efficient and scalable computing solutions. Currently, neuronal functionality is often performedusing a transistor-based neuron, which is area- and energy-inefficient. Therefore, research in the "beyond von Neumann" area is aimed at novel volatile switching components with adjustable switching times, low power consumption, and high scalability, which could potentially be used as artificial neurons in NC circuits. These include threshold-switching devices that switch abruptly from the high-resistance state (HRS) to the low-resistance state (LRS) at a defined voltage. As soon as the applied voltage falls below a certain value, the cell relaxes back to the initial HRS state. In particular, diffusive memristors built from volatile electrochemical metallization (ECM) cells are attracting attention in emerging NC areas such as temporal encoding. These diffusive memristors consist of switching layers made from oxides or chalcogenides sandwiched between an electrochemically active electrode (e.g., Ag or Cu) and an inert electrode (e.g., Pt metal). The cells can be miniaturized down to the sub-micrometer range and the switching itself relies on the formation and dissolution of a metallic filament. Since the temporal behavior of diffusive memristors is their main characteristic, it is of crucial importance to understand the relaxation dynamics of these devices from a physical perspective. This is a prerequisite for optimizing and modulating the performance of diffusive memristors, especially for applications requiring precise control of switching times. Previous approaches mainly describe the relaxation time as a function of the given filament diameter while the filament growth process is not considered. |