Manasa Kaniselvan

Last Name

Kaniselvan

First Name

Manasa

ORCID

0000-0002-5331-8878

Organisational unit

03925 - Luisier, Mathieu / Luisier, Mathieu

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Publications1 - 10 of 14

Termination-Dependent Resistive Switching in SrTiO3 Valence Change Memory Cells
Mladenović, Marko; Kaniselvan, Manasa; Weilenmann, Christoph; et al. (2025)
ACS Applied Electronic Materials
Valence change memory (VCM) cells based on SrTiO3 (STO), a perovskite oxide, are a promising type of emerging memory device. While the operational principle of most VCM cells relies on the growth and dissolution of one or multiple conductive filaments, those based on STO are known to exhibit a distinctive “interface-type” switching, which is associated with the modulation of the Schottky barrier at their active electrode. Still, a detailed picture of the processes that lead to interface-type switching is not available. In this work, we use a fully atomistic ab initio model to study the resistive switching of a Pt-STO-Ti stack. We identify that the termination of the crystalline STO plays a decisive role in the switching mechanism, depending on the relative band alignment between the material and the Pt electrode. In particular, we show that the accumulation of oxygen vacancies at the Pt side can be the origin of resistive switching in TiO2-terminated devices by lowering the conduction band minimum of the STO layer, thus facilitating transmission through the Schottky barrier. Moreover, we investigated the possibility of filamentary switching in STO and revealed that it is most likely to occur at the Pt electrode of the SrO-terminated cells.
Single neuromorphic memristor closely emulates multiple synaptic mechanisms for energy efficient neural networks
Weilenmann, Christoph; Ziogas, Alexandros Nikolaos; Zellweger, Till; et al. (2024)
Nature Communications
Biological neural networks do not only include long-term memory and weight multiplication capabilities, as commonly assumed in artificial neural networks, but also more complex functions such as short-term memory, short-term plasticity, and meta-plasticity - all collocated within each synapse. Here, we demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation. They can act as multi-functional hardware synapses in a class of bio-inspired deep neural networks (DNN) that make use of both long- and short-term synaptic dynamics and are capable of meta-learning or learning-to-learn. The resulting bio-inspired DNN is then trained to play the video game Atari Pong, a complex reinforcement learning task in a dynamic environment. Our analysis shows that the energy consumption of the DNN with multi-functional memristive synapses decreases by about two orders of magnitude as compared to a pure GPU implementation. Based on this finding, we infer that memristive devices with a better emulation of the synaptic functionalities do not only broaden the applicability of neuromorphic computing, but could also improve the performance and energy costs of certain artificial intelligence applications.
Nanoscale Device Modeling Beyond the Ballistic Limit of Transport and Fixed Geometries
Luisier, Mathieu; Backman, Jonathan; Cao, Jiang; et al. (2024)
2024 IEEE International Electron Devices Meeting (IEDM)
To design the multi-functional, multi-materials nano-devices equipping today's electronic products, a dedicated simulation environment that fulfills very specific requirements is needed. In this paper, we present such a framework that relies on density functional theory, molecular dynamics, kinetic Monte Carlo, and quantum transport. We then demonstrate its key features (inclusion of scattering and treatment of evolving geometry) using four representative device examples.
An Atomistic Model of Field-Induced Resistive Switching in Valence Change Memory
Kaniselvan, Manasa; Luisier, Mathieu; Mladenović, Marko (2023)
ACS Nano
In valence change memory (VCM) cells, the conductance of an insulating switching layer is reversibly modulated by creating and redistributing point defects under an external field. Accurately simulating the switching dynamics of these devices can be difficult due to their typically disordered atomic structures and inhomogeneous arrangements of defects. To address this, we introduce an atomistic framework for modeling VCM cells. It combines a stochastic kinetic Monte Carlo approach for atomic rearrangement with a quantum transport scheme, both parametrized at the ab initio level by using inputs from density functional theory. Each of these steps operates directly on the underly i n g atomic structure. The model thus directly relates the energy landscape and electronic structure of the device to its switching characteristics. We apply this model to simulate field-induced nonvolatile switching between high-and low-resistance states in a TiN/HfO2/Ti/TiN stack and analyze both the kinetics and stochasticity of the conductance transitions. We also resolve the atomic nature of current flow resulting from the valence change mechanism, finding that conductive paths are formed between the undercoordinated Hf atoms neighboring oxygen vacancies. The model developed here can be applied to different material systems to evaluate their resistive switching potential, both for use as conventional memory cells and as neuromorphic computing primitives.
Electroforming Kinetics in HfOₓ/Ti RRAM: Mechanisms behind Compositional and Thermal Engineering
Kaniselvan, Manasa; Portner, Kevin; Falcone, Donato Francesco; et al. (2025)
ACS Nano
A critical issue affecting filamentary resistive random access memory (RRAM) cells is the requirement of high voltages during electroforming. Reducing the magnitude of these voltages is of significant interest, as it ensures compatibility with complementary metal-oxide-semiconductor (CMOS) technologies. Previous studies have identified that changing the initial stoichiometry of the switching layer and/or implementing thermal engineering approaches has an influence on the electroforming voltage magnitude, but the exact mechanisms remain unclear. Here, we develop an understanding of how these mechanisms work within a standard a-HfOₓ/Ti RRAM stack through combining atomistic driven kinetic Monte Carlo (d-KMC) simulations with experimental data. By performing device-scale simulations at atomistic resolution, we can precisely model the movements of point defects under applied biases in structurally inhomogeneous materials, which allows us to not only capture finite-size effects but also understand how conductive filaments grow under different electroforming conditions. Doing atomistic simulations at the device level also enables us to link simulations of the mechanisms behind conductive filament formation with trends in experimental data for the same material stack. We identify a transition from primarily vertical to lateral ion movement dominating the filamentary growth process in substoichiometric oxides and differentiate the influence of global and local heating on the morphology of the formed filaments. These different filamentary structures have implications for the dynamic range exhibited by formed devices in subsequent SET/RESET operations. Overall, our results unify the complex ion dynamics in technologically relevant HfOₓ/Ti-based stacks and provide guidelines that can be leveraged when fabricating devices.
Learning the Electronic Hamiltonian of Large Atomic Structures
Xia, Chen Hao; Kaniselvan, Manasa; Ziogas, Alexandros Nikolaos; et al. (2025)
Proceedings of Machine Learning Research ~ Proceedings of the 42nd International Conference on Machine Learning
Graph neural networks (GNNs) have shown promise in learning the ground-state electronic properties of materials, subverting ab initio density functional theory (DFT) calculations when the underlying lattices can be represented as small and/or repeatable unit cells (i.e., molecules and periodic crystals). Realistic systems are, however, non-ideal and generally characterized by higher structural complexity. As such, they require large (10+ {Å}) unit cells and thousands of atoms to be accurately described. At these scales, DFT becomes computationally prohibitive, making GNNs especially attractive. In this work, we present a strictly local equivariant GNN capable of learning the electronic Hamiltonian (H) of realistically extended materials. It incorporates an augmented partitioning approach that enables training on arbitrarily large structures while preserving local atomic environments beyond boundaries. We demonstrate its capabilities by predicting the electronic Hamiltonian of various systems with up to 3,000 nodes (atoms), 500,000+ edges, 28 million orbital interactions (nonzero entries of H), and ≤0.53% error in the eigenvalue spectra. Our work expands the applicability of current electronic property prediction methods to some of the most challenging cases encountered in computational materials science, namely systems with disorder, interfaces, and defects.
Accelerated Atomistic Kinetic Monte Carlo Simulations of Resistive Memory Arrays
Kaniselvan, Manasa; Maeder, Alexander; Mladenović, Marko; et al. (2024)
2024 SC24: International Conference for High Performance Computing, Networking, Storage and Analysis SC
Simulating emerging resistive switching memory devices, such as memristors, requires modeling frameworks that can treat the motion of point defects across nanoscale domains. Field-driven Kinetic Monte Carlo (d-KMC) methods that simulate the discrete structural evolution of atomic coordinates in the presence of external potential and heat fields can be used for this purpose. While physically similar to conventional KMC methods, field-driven approaches present different computational motifs and introduce global communication. Here, we develop the first scalable d-KMC code for resistive memory arrays at atomistic resolution. We accelerate this latency-sensitive simulation on the GPU partition of the LUMI Supercomputer, exploiting the highspeed interconnects between GPUs on the same node. Applied to the technologically relevant HfOx material stack, our code enables the first atomistic simulation of 33 arrays of resistive switching memory cells with more than 1 million atoms, matching the dimensions of fabricated structures.
Machine-Learned Hamiltonians for Quantum Transport Simulation of Valence Change Memories
Xia, Chen Hao; Kaniselvan, Manasa; Mladenović, Marko; et al. (2025)
2025 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD)
The construction of the Hamiltonian matrix H is an essential, yet computationally expensive step in ab-initio device simulations based on density-functional theory (DFT). In homogeneous structures, the fact that a unit cell repeats itself along at least one direction can be leveraged to minimize the number of atoms considered and the calculation time. However, such an approach does not lend itself to amorphous or defective materials for which no periodicity exists. In these cases, (much) larger domains containing thousands of atoms might be needed to accurately describe the physics at play, pushing DFT tools to their limit. Here we address this issue by learning and directly predicting the Hamiltonian matrix of large structures through equivariant graph neural networks and so-called augmented partitioning training. We demonstrate the strength of our approach by modeling valence change memory (VCM) cells, achieving a Mean Absolute Error (MAE) of 3.39 to 3.58 meV, as compared to DFT, when predicting the Hamiltonian matrix entries of systems made of ∼5,000 atoms. We then replace the DFT-computed Hamiltonian of these VCMs with the predicted one to compute their energy-resolved transmission function with a quantum transport tool. A qualitatively good agreement between both sets of curves is obtained. Our work provides a path forward to overcome the memory and computational limits of DFT, thus enabling the study of large-scale devices beyond current ab-initio capabilities.
Mechanisms of resistive switching in two-dimensional monolayer and multilayer materials
Kaniselvan, Manasa; Jeon, Y.-R.; Mladenović, Marko; et al. (2025)
Nature Materials
The power and energy consumption of resistive switching devices can be lowered by reducing the dimensions of their active layers. Efforts to push this low-energy switching property to its limits have led to the investigation of active regions made with two-dimensional (2D) layered materials. Despite their small dimensions, 2D layered materials exhibit a rich variety of switching mechanisms, each involving different types of atomic structure reconfiguration. In this Review, we highlight and classify the mechanisms of resistive switching in monolayer and bulk 2D layered materials, with a subsequent focus on those occurring in a monolayer and/or localized to point defects in the crystalline sheet. We discuss the complex energetics involved in these fundamentally defect-assisted processes, including the coexistence of multiple mechanisms and the effects of the contacts used. Examining the highly localized ‘atomristor’-type switching, we provide insights into atomic motions and electronic transport across the metal–2D interfaces underlying their operation. Finally, we discuss progress and our perspective on the challenges associated with the development of 2D resistive switching devices. Promising application areas and material systems are identified and suggested for further research.
Insights behind multi-level conductance transitions in HfO𝑥 memristors
Kaniselvan, Manasa; Mladenović, Marko; Clarysse, Jente; et al. (2024)
2024 Device Research Conference (DRC)

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Manasa Kaniselvan

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