arXiv subjects

Jan Trieschmann

Publications and source records attributed to Jan Trieschmann.

At least 19 recordsLinked to original sources

arXiv2026

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↗

arXiv2026

A physics-informed neural network approach to solve the spatially inhomogeneous electron Boltzmann equation

The accurate determination of electron properties is fundamental to low-temperature plasma simulations, necessitating precise solutions to the spatially inhomogeneous electron Boltzmann equation (EBE). This work explores the use of physics-informed neural networks (PINNs) for obtaining solutions to the spatially one-dimensional (1D) EBE subject to a uniform electric field in atomic gases. Employing the two-term approximation, the resulting equation for the isotropic distribution is solved directly in kinetic energy space without the conventional transformation to total energy. This approach demonstrates the flexibility of the PINN framework in handling diverse equation formulations. To address the convergence difficulties associated with this class of kinetic equations, a new neural network architecture is introduced. It features a Fourier-feature input layer, adaptive activation functions, and a scaled multiplicative gating mechanism. It is demonstrated that this formulation preserves robust gradient flow throughout the network, which is critical for learning physically correct solutions. Benchmarking against reference data reveals that the present architecture achieves excellent agreement across both microscopic and macroscopic properties of the electrons. Furthermore, the architecture exhibits strong generalization across different gas types and a defined range of electric field strengths without requiring case-specific hyperparameter tuning. Ultimately, the excellent accuracy achieved here validates the applicability of the present PINN method.

physics.plasm-ph↗

arXiv2024

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↗

arXiv2024

Physics-based Modeling and Simulation of Nanoparticle Networks

This study presents the computational modeling and simulation of silver nanoparticle networks (NPNs), which, in the realm of neuromorphic computation, suggest to be a promising candidate for nontraditional computation methods. The modeling of the networks construction, its electrical properties and model parameters are derived from well-established physical principles.

cond-mat.mtrl-sci↗

arXiv2024

Nonlinear behavior of memristive devices for hardware security primitives and neuromorphic computing systems

Nonlinearity is a crucial characteristic for implementing hardware security primitives or neuromorphic computing systems. The main feature of all memristive devices is this nonlinear behavior observed in their current-voltage characteristics. To comprehend the nonlinear behavior, we have to understand the coexistence of resistive, capacitive, and inertia (virtual inductive) effects in these devices. These effects originate from corresponding physical and chemical processes in memristive devices. A physics-inspired compact model is employed to model and simulate interface-type RRAMs such as Au/BiFeO$_{3}$/Pt/Ti, Au/Nb$_{\rm x}$O$_{\rm y}$/Al$_{2}$O$_{3}$/Nb, while accounting for the modeling of capacitive and inertia effects. The simulated current-voltage characteristics align well with experimental data and accurately capture the non-zero crossing hysteresis generated by capacitive and inductive effects. This study examines the response of two devices to increasing frequencies, revealing a shift in their nonlinear behavior characterized by a reduced hysteresis range and increased chaotic behavior, as observed through internal state attractors. Fourier series analysis utilizing a sinusoidal input voltage of varying amplitudes and frequencies indicates harmonics or frequency components that considerably influence the functioning of RRAMs. Moreover, we propose and demonstrate the use of the frequency spectra as one of the fingerprints for memristive devices.

cs.ET↗

arXiv2024

Coexistence of resistive capacitive and virtual inductive effects in memristive devices

This paper examines the coexistence of resistive, capacitive, and inertia (virtual inductive) effects in memristive devices, focusing on ReRAM devices, specifically the interface-type or non-filamentary analog switching devices. A physics-inspired compact model is used to effectively capture the underlying mechanisms governing resistive switching in NbO$_{\rm x}$ and BiFeO$_{3}$ based on memristive devices. The model includes different capacitive components in metal-insulator-metal structures to simulate capacitive effects. Drift and diffusion of particles are modeled and correlated with particles' inertia within the system. Using the model, we obtain the I-V characteristics of both devices that show good agreement with experimental findings and the corresponding C-V characteristics. This model also replicates observed non-zero crossing hysteresis in perovskite-based devices. Additionally, the study examines how the reactance of the device changes in response to variations in the device area and length.

cond-mat.mes-hall↗

arXiv2024

Non-zero crossing current-voltage characteristics of interface-type resistive switching devices

A number of memristive devices, mainly ReRAMs, have been reported to exhibit a unique non-zero crossing hysteresis attributed to the interplay of resistive and not yet fully understood `capacitive', and `inductive' effects. This work exploits a kinetic simulation model based on the stochastic cloud-in-a-cell method to capture these effects. The model, applied to Au/BiFeO$_{3}$/Pt/Ti interface-type devices, incorporates vacancy transport and capacitive contributions. The resulting nonlinear response, characterized by hysteresis, is analyzed in detail, providing an in-depth physical understanding of the virtual effects. Capacitive effects are modeled across different layers, revealing their significant role in shaping the non-zero crossing hysteresis behavior. Results from kinetic simulations demonstrate the impact of frequency-dependent impedance on the non-zero crossing phenomenon. This model provides insights into the effects of various device material properties, such as Schottky barrier height, device area and oxide layer on the non-zero crossing point.

cond-mat.mes-hall↗

arXiv2023

Machine learning for advancing low-temperature plasma modeling and simulation

Machine learning has had an enormous impact in many scientific disciplines. Also in the field of low-temperature plasma modeling and simulation it has attracted significant interest within the past years. Whereas its application should be carefully assessed in general, many aspects of plasma modeling and simulation have benefited substantially from recent developments within the field of machine learning and data-driven modeling. In this survey, we approach two main objectives: (a) We review the state-of-the-art focusing on approaches to low-temperature plasma modeling and simulation. By dividing our survey into plasma physics, plasma chemistry, plasma-surface interactions, and plasma process control, we aim to extensively discuss relevant examples from literature. (b) We provide a perspective of potential advances to plasma science and technology. We specifically elaborate on advances possibly enabled by adaptation from other scientific disciplines. We argue that not only the known unknowns, but also unknown unknowns may be discovered due to the inherent propensity of data-driven methods to spotlight hidden patterns in data.

physics.plasm-ph↗

arXiv2023

PECVD and PEALD on polymer substrates (Part II): Understanding and tuning of barrier and membrane properties of thin films

This feature article presents insights concerning the correlation of PECVD and PEALD thin film structures with their barrier or membrane properties. While in principle similar precursor gases and processes can be applied, the adjustment of deposition parameters for different polymer substrates can lead to either an effective diffusion barrier or selective permeabilities. In both cases the understanding of the film growth and the analysis of the pore size distribution and the pore surface chemistry is of utmost importance for the understanding of the related transport properties of small molecules. In this regard the article presents both concepts of thin film engineering and analytical as well as theoretical approaches leading to a comprehensive description of the state of the art in this field. Moreover, based on the presented correlation of film structure and molecular transport properties perspectives of future relevant research in this area is presented.

cond-mat.soft↗

arXiv2023

PECVD and PEALD on polymer substrates (part I): Fundamentals and analysis of plasma activation and thin film growth

This feature article considers the analysis of the initial states of film growth on polymer substrates. The assembled results are based on the cooperation between research groups in the field of plasma physics, chemistry, electric as well as mechanical engineering over the last years, mostly within the frame of the transregional project SFB-TR 87 ("Pulsed high power plasmas for the synthesis of nanostructured functional layers"). This feature article aims at bridging the gap between the understanding of plasma processes in the gas phase and the resulting surface and interface processes of the polymer. The results show that interfacial adhesion and initial film growth can be well controlled and even predicted based on the combination of analytical approaches.

cond-mat.soft↗

arXiv2023

Towards a Machine-Learned Poisson Solver for Low-Temperature Plasma Simulations in Complex Geometries

Poisson's equation plays an important role in modeling many physical systems. In electrostatic self-consistent low-temperature plasma (LTP) simulations, Poisson's equation is solved at each simulation time step, which can amount to a significant computational cost for the entire simulation. In this paper, we describe the development of a generic machine-learned Poisson solver specifically designed for the requirements of LTP simulations in complex 2D reactor geometries on structured Cartesian grids. Here, the reactor geometries can consist of inner electrodes and dielectric materials as often found in LTP simulations. The approach leverages a hybrid CNN-transformer network architecture in combination with a weighted multiterm loss function. We train the network using highly-randomized synthetic data to ensure the generalizability of the learned solver to unseen reactor geometries. The results demonstrate that the learned solver is able to produce quantitatively and qualitatively accurate solutions. Furthermore, it generalizes well on new reactor geometries such as reference geometries found in the literature. To increase the numerical accuracy of the solutions required in LTP simulations, we employ a conventional iterative solver to refine the raw predictions, especially to recover the high-frequency features not resolved by the initial prediction. With this, the proposed learned Poisson solver provides the required accuracy and is potentially faster than a pure GPU-based conventional iterative solver. This opens up new possibilities for developing a generic and high-performing learned Poisson solver for LTP systems in complex geometries.

physics.comp-ph↗

arXiv2023

Physics-separating artificial neural networks for predicting sputtering and thin film deposition of AlN in Ar/N$_2$ discharges on experimental timescales

Understanding and modeling plasma-surface interactions frame a multi-scale as well as multi-physics problem. Scale-bridging machine learning surface surrogate models have been demonstrated to perceive the fundamental atomic fidelity for the physical vapor deposition of pure metals. However, the immense computational cost of the data-generating simulations render a practical application with predictions on relevant timescales impracticable. This issue is resolved in this work for the sputter deposition of AlN in Ar/N$_2$ discharges by developing a scheme that populates the parameter spaces effectively. Hybrid reactive molecular dynamics / time-stamped force-bias Monte Carlo simulations of randomized plasma-surface interactions / diffusion processes are used to setup a physics-separating artificial neural network. The application of this generic machine learning model to a specific experimental reference case study enables the systematic analysis of the particle flux emission as well as underlying system state (e.g., composition, mass density, stress, point defect structure) evolution within process times of up to 45 minutes.

cond-mat.mtrl-sci↗

arXiv2022

Physics-separating artificial neural networks for predicting initial stages of Al sputtering and thin film deposition in Ar plasma discharges

Simulations of Al thin film sputter depositions rely on accurate plasma and surface interaction models. Establishing the latter commonly requires a higher level of abstraction and means to dismiss the fundamental atomic fidelity. Previous works on sputtering processes addressed this issue by establishing machine learning surrogate models, which include a basic surface state (i.e., stoichiometry) as static input. In this work, an evolving surface state and defect structure are introduced to jointly describe sputtering and growth with physics-separating artificial neural networks. The data describing the plasma-surface interactions stem from hybrid reactive molecular dynamics/time-stamped force bias Monte Carlo simulations of Al neutrals and Ar$^+$ ions impinging onto Al(001) surfaces. It is demonstrated that the fundamental processes are comprehensively described by taking the surface state as well as defect structure into account. Hence, a machine learning plasma-surface interaction surrogate model is established that resolves the inherent kinetics with high physical fidelity. The resulting model is not restricted to input from modeling and simulation, but may similarly be applied to experimental input data.

cond-mat.mtrl-sci↗

arXiv2022

Charge-optimized many-body interaction potential for AlN revisited to explore plasma-surface interactions

Plasma-surface interactions during AlN thin film sputter deposition could be studied by means of reactive molecular dynamics (RMD) methods. This requires an interaction potential that describes all species as well as wall interactions (e.g., particle emission, damage formation) appropriately. However, previous works focused on the establishment of AlN bulk potentials. Although for the third-generation charge-optimized many-body (COMB3) potential at least a single reference surface was taken into account, surface interactions are subject to limited reliability only. The demand for a revised COMB3 AlN potential is met in two steps: First, the Ziegler-Biersack-Littmark potential is tapered and the variable charge model QTE$^+$ is implemented to account for high-energy collisions and distant charge transport, respectively. Second, the underlying parameterization is reworked by applying a self-adaptive evolution strategy implemented in the GARFfield software. Four wurtzite, three zinc blende and three rock salt surfaces are considered. An example study on the ion bombardment induced particle emission and point defect formation reveals that the revised COMB3 AlN potential is appropriate for the accurate investigation of plasma-surface interactions by means of RMD simulations.

physics.plasm-ph↗

arXiv2022

2022 Review of Data-Driven Plasma Science

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future.

physics.plasm-ph↗

arXiv2021

Stochastic behaviour of an interface-based memristive device

A large number of simulation models have been proposed over the years to mimic the electrical behaviour of memristive devices. The models are based either on sophisticated mathematical formulations that do not account for physical and chemical processes responsible for the actual switching dynamics or on multi-physical spatially resolved approaches that include the inherent stochastic behaviour of real-world memristive devices but are computationally very expensive. In contrast to the available models, we present a computationally inexpensive and robust spatially 1D model for simulating interface-type memristive devices. The model efficiently incorporates the stochastic behaviour observed in experiments and can be easily transferred to circuit simulation frameworks. The ion transport, responsible for the resistive switching behaviour, is modelled using the kinetic Cloud-In-a-Cell scheme. The calculated current-voltage characteristics obtained using the proposed model show excellent agreement with the experimental findings.

cs.ET↗

arXiv2021

Molecular Dynamics Study on the Role of Ar Ions in the Sputter Deposition of Al Thin Films

Molecular dynamics simulations are often used to study sputtering and thin film growth. Compressive stresses in these thin films are generally assumed to be caused by a combination of forward sputtered (peened) built-in particles and entrapped working gas atoms. While the former are assumed to hold a predominant role, the effect of the latter on the interaction dynamics as well as thin film properties are scarcely clarified (concurrent or causative). The inherent overlay of the ion bombardment induced processes render an isolation of their contribution impracticable. In this work, this issue is addressed by comparing the results of two case studies on the sputter deposition of Al thin films in Ar working gas. In the first run Ar atoms are fully retained. In the second run they are artificially neglected, as implanted Ar atoms are assumed to outgas anyhow and not alter the ongoing dynamics significantly. Both case studies have in common that the consecutive impingement of 100 particles (i.e., Ar$^+$ ions, Al atoms) onto Al(001) surfaces for ion energies in the range of 3 eV to 300 eV as well as Al/Ar$^+$ flux ratios from 0 to 1 are considered. The surface interactions are simulated by means of hybrid reactive molecular dynamics/force-biased Monte Carlo simulations and characterized in terms of mass density, Ar concentration, biaxial stress, shear stress, ring statistical connectivity profile, Ar gas porosity, Al vacancy density, and root-mean-squared roughness. Ultimately, implanted Ar atoms are found to form subnanometer sized eventually outgassing clusters for ion energies exceeding 100 eV. They fundamentally govern a variety of surface processes (e.g., forward sputtering/peening) and surface properties (e.g., compressive stresses) in the considered operating regime.

cond-mat.mtrl-sci↗

arXiv2021

An efficient plasma-surface interaction surrogate model for sputtering processes based on autoencoder neural networks

Simulations of thin film sputter deposition require the separation of the plasma and material transport in the gas-phase from the growth/sputtering processes at the bounding surfaces. Interface models based on analytic expressions or look-up tables inherently restrict this complex interaction to a bare minimum. A machine learning model has recently been shown to overcome this remedy for Ar ions bombarding a Ti-Al composite target. However, the chosen network structure (i.e., a multilayer perceptron) provides approximately 4 million degrees of freedom, which bears the risk of overfitting the relevant dynamics and complicating the model to an unreliable extend. This work proposes a conceptually more sophisticated but parameterwise simplified regression artificial neural network for an extended scenario, considering a variable instead of a single fixed Ti-Al stoichiometry. A convolutional $\beta$-variational autoencoder is trained to reduce the high-dimensional energy-angular distribution of sputtered particles to a latent space representation of only two components. In addition to a primary decoder which is trained to reconstruct the input energy-angular distribution, a secondary decoder is employed to reconstruct the mean energy of incident Ar ions as well as the present Ti-Al composition. The mutual latent space is hence conditioned on these quantities. The trained primary decoder of the variational autoencoder network is subsequently transferred to a regression network, for which only the mapping to the particular latent space has to be learned. While obtaining a competitive performance, the number of degrees of freedom is drastically reduced to 15,111 and 486 parameters for the primary decoder and the remaining regression network, respectively. The underlying methodology is general and can easily be extended to more complex physical descriptions with a minimal amount of data required.

physics.comp-ph↗