arXiv subjects

Sahitya Yarragolla

Publications and source records attributed to Sahitya Yarragolla.

8 recordsLinked to original sources

arXiv2026

From Processing to Functionality: Engineering Accessible Material States in Cu-Embedded SiO$_x$ Memristive Devices

Resistive switching in oxide-based devices is widely governed by stochastic defect processes, yet a predictive link between fabrication conditions and functional behavior remains elusive. Here, we establish a multiscale framework connecting plasma-defined deposition conditions to macroscopic device functionality in sputtered SiO$_x$/Cu/SiO$_x$-based systems. By combining large-scale statistical analysis of more than 50,000 experimentally characterized devices with physics-based plasma and atomistic simulations, we show that device behavior does not emerge from deterministic process-to-performance mappings, but from a probabilistic cascade spanning defect formation, defect-state evolution, and functional-regime emergence. Data-driven clustering reveals a continuous functional state space composed of operational switching types, while inverse modeling identifies the reconstructed oxygen-vacancy density as an effective latent descriptor capturing the combined influence of structural disorder and defect topology. This latent descriptor is strongly coupled to both Cu redistribution and electrical response, linking otherwise hidden material properties to observable device characteristics. Furthermore, macroscopic switching behavior is argued to arise from ensemble integration across spatially heterogeneous subdomains, providing a physical explanation for the pronounced variability of large-area devices. These findings shift the perspective from deterministic defect engineering toward probabilistic defect-state design and establish a physically grounded framework for understanding and controlling functional variability in such oxide-based systems, such as memristive or resistive-switching devices.

cond-mat.mtrl-sci↗

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↗

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↗

arXiv2022

Physics inspired compact modelling of BiFeO$_3$ based memristors for hardware security applications

With the advent of the Internet of Things, nanoelectronic devices or memristors have been the subject of significant interest for use as new hardware security primitives. Among the several available memristors, BiFe$\rm O_{3}$ (BFO)-based electroforming-free memristors have attracted considerable attention due to their excellent properties, such as long retention time, self-rectification, intrinsic stochasticity, and fast switching. They have been actively investigated for use in physical unclonable function (PUF) key storage modules, artificial synapses in neural networks, nonvolatile resistive switches, and reconfigurable logic applications. In this work, we present a physics-inspired 1D compact model of a BFO memristor to understand its implementation for such applications (mainly PUFs) and perform circuit simulations. The resistive switching based on electric field-driven vacancy migration and intrinsic stochastic behaviour of the BFO memristor are modelled using the cloud-in-a-cell scheme. The experimental current-voltage characteristics of the BFO memristor are successfully reproduced. The response of the BFO memristor to changes in electrical properties, environmental properties (such as temperature) and stress are analyzed and consistent with experimental results.

cs.ET↗

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↗