Multiscale analyses of structural, electronic and chemical aspects in dynamic memristive networks

• Applicants: Professor Dr. Lorenz Kienle; Dr.-Ing. Alexander Vahl
• Subject Area: Synthesis and Properties of Functional Materials ; Electronic Semiconductors, Components and Circuits, Integrated Systems, Sensor Technology, Theoretical Electrical Engineering
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 568550697

This project focuses on advancing the development and understanding of nanoparticle networks (NPNs) by intertwining advanced fabrication, electrical characterization and innovative analytics. NPNs at the percolation threshold exhibit collective, scale-free avalanche switching dynamics, which are linked to long-range interconnected nanoparticles and localized resistive switching at nanogaps in between adjacent nanoparticles. However, current NPNs are limited to planar 2D arrangements, restricting connectivity pathways. In contrast, biological neural networks excel in 3D connectivity, which solves routing problems and enhances information processing efficiency. This project hypothesizes that 3D connection schemes, sparseness and branching are essential for improving the application range of NPNs. The goal is to develop 2.5D and 3D NPNs with long-term stability in collective resistive switching and nonlinear properties for reservoir computing. Innovative nanoparticle beam deposition techniques will be combined with advanced electron microscopy to create complex networks with metallic and dielectric components. The dielectric component is expected to introduce sparseness and branching in self-ordered connection paths, while adding capacitive non-linearity due to the dielectric nanoparticles' charging behavior. The NPNs will be characterized using a range of electron microscopy techniques, including atomically resolved X-ray (EDX) and electron energy loss spectroscopy (EELS), as well as multiscale analytics such as in situ scanning electron microscopy (SEM) and X-ray diffraction (XRD). This high-precision methodology will allow for the study of the structure-function relationships of NPNs with different dimensionalities, helping to identify the microscopic origins of resistance changes and current path modifications. By integrating electron microscopy with nanoparticle synthesis, the project aims to design NPNs optimized for memristive switching, exploring the limits of their collective switching capabilities, stability and nonlinear properties. Key research questions include the impact of connection schemes on resistive switching, how nanoparticle properties affect the network topology, and how these NPNs can be optimized for use as physical reservoirs for in-materia reservoir computing.

• DFG Programme: Research Grants
• International Connection: Czech Republic, Italy, New Zealand
• Cooperation Partners: Francesca Borghi, Ph.D.; Professor Simon Brown, Ph.D.; Daniil Nikitin, Ph.D.