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Rouven Lamprecht

Publications and source records attributed to Rouven Lamprecht.

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On the physical origins of switching diversity in Cu-embedded SiO$_x$ memristive devices

Resistive switching devices with sub-stoichiometric SiO$_x$ and pancake-like Cu nanoparticles (Cu-PCs) exhibit distinct macroscopic current-voltage characteristics classified as capacitive or gradual (interface-type switching) and abrupt or resistive (filamentary-type switching), motivating an analysis of the microscopic processes underlying this diversity. It is proposed that the device defect landscape is largely shaped by two charged defect types, mobile oxygen vacancies and immobile Cu-related defects, whose distributions jointly govern interfacial and bulk transport. An effective one-dimensional cloud-in-a-cell simulation framework is employed to reproduce the phenomenological picture of both interface-type and filamentary-type switching by incorporating the dominant coupled ionic and electronic processes underlying these mechanisms. The model includes oxygen-vacancy drift-diffusion, Schottky-limited injection at the metal/oxide interfaces, and bulk trap-assisted transport via Poole-Frenkel conduction, with Cu-PCs near the top interface treated effectively. A simulation-based parametric study varying voltage stress, sweep rate, and oxide thickness is used to examine how these factors rebalance voltage partitioning and the spatiotemporal electric field distribution, thereby altering vacancy redistribution and the relative contributions of interface- and bulk-limited conduction. Using representative, physically motivated parameter sets informed by prior device-level studies, the simulations accurately reproduce the characteristic $I$-$V$ signatures of seven different experimentally observed switching responses. Overall, the findings help to link microscopic defect landscapes and transport processes to experimentally measured macroscopic responses within a single, self-consistent modeling framework.

cond-mat.mtrl-sci

Wedge-type engineered analog SiO$_\mathrm{x}$/Cu/SiO$_\mathrm{x}$-Memristive Devices for Neuromorphic Applications

This study presents a comprehensive examination of the development of TiN/SiO$_\mathrm{x}$/Cu/SiO$_\mathrm{x}$/TiN memristive devices, engineered for neuromorphic applications using a wedge-type deposition technique and Monte Carlo simulations. Identifying critical parameters for the desired device characteristics can be challenging with conventional trial-and-error approaches, which often obscure the effects of varying layer compositions. By employing an \textit{off-center} thermal evaporation method, we created a thickness gradient of SiO$_\mathrm{x}$ and Cu on a 4-inch wafer, facilitating detailed resistance map analysis through semiautomatic measurements. This allows to investigate in detail the influence of layer composition and thickness on single wafers, thus keeping every other process condition constant. Combining experimental data with simulations provides a precise understanding of the layer thickness distribution and its impact on device performance. Optimizing the SiO$_\mathrm{x}$ layers to be below 12.5 nm, coupled with a discontinuous Cu layer with a nominal thickness lower than 0.6 nm, exhibits analog switching properties with an R$_\mathrm{on}$/R$_\mathrm{off}$ ratio of $>$100, suitable for neuromorphic applications, whereas R $\times$ A analysis shows no clear signs of filamentary switching. Our findings highlight the significant role of carefully choosing the SiO$_\mathrm{x}$ and Cu thickness in determining the switching behavior and provide insights that could lead to the more systematic development of high-performance analog switching components for bio-inspired computing systems.

cond-mat.mes-hall