arXiv subjects

Marko Mladenović

Publications and source records attributed to Marko Mladenović.

11 recordsLinked to original sources

arXiv2026

Elemental Germanium Phase-Change Memory

Phase-change memory (PCM) is a mature technology for fast, scalable, non-volatile data storage, with applications spanning embedded memory, as well as in-memory and neuromorphic computing. PCM predominantly relies on chalcogenide alloys, with $\mathrm{Ge_2Sb_2Te_5}$ (GST) as the industry standard. Yet in these alloys, the individual Ge, Sb, and Te atoms redistribute upon cycling, causing stochastic operation and ultimately device failure. To address this issue, elemental antimony was proposed as a PCM material, but it exhibits a metastable amorphous state that prevents reliable data retention. Moreover, tellurium and antimony can contaminate complementary metal-oxide-semiconductor (CMOS) production lines or act as unintended dopants, restricting manufacturing of PCM to dedicated fabs. Here we introduce elemental germanium (Ge) as a CMOS-native phase-change material that overcomes these fundamental limitations. In a vertical PCM cell architecture, Ge enables sub-nanosecond crystallization (240 ps, 40 times faster than GST), non-volatile data storage with excellent thermal stability ($>$110 °C for 10 years vs. $\sim$87 °C for GST), and a resistance drift coefficient approximately 60% lower than in GST. These results establish pure Ge, a standard semiconductor, as an alternative to chalcogenide phase-change materials, achieving superior performance in key metrics and enabling phase-change memory to be fabricated in standard semiconductor facilities.

cond-mat.mtrl-sci↗

arXiv2026

Conductance-dependent Photoresponse in a Dynamic SrTiO3 Memristor for Biorealistic Computing

Modern computers perform pre-defined operations using static memory components, whereas biological systems learn through inherently dynamic, time-dependent processes in synapses and neurons. The biological learning process also relies on global signals - neuromodulators - who influence many synapses at once depending on their dynamic, internal state. In this study, using optical radiation as a global neuromodulatory signal, we investigate nanoscale SrTiO3 (STO) memristors that can act as solid-state synapses. Via diverse sets of measurements, we demonstrate that the memristor's photoresponse depends on the electrical conductance state, following a well-defined square root relation. Additionally, we show that the conductance decays after photoexcitation with time constants in the range of 1 - 10 s and that this effect can be reliably controlled using an electrical bias. These properties in combination with our device's low power operation (< 1pJ per optical pulse) and small measurement variability may pave the way for space- and energy-efficient implementations of complex biological learning processes in electro-optical hardware.

cs.ET↗

arXiv2025

Relating the dynamics of photo de-mixing in mixed bromide-iodide perovskites to ionic and electronic transport

The observation of reversible de-mixing phenomena in mixed-halide perovskites under illumination is one of the most challenging as well as intriguing aspects of this class of materials. On the one hand, it poses critical constraints to the compositional space that allows reliable design of absorbers for perovskite photovoltaics. On the other hand, it holds potential for the development of novel optoionic devices where an ionic response is triggered via optical stimuli. Funda-mental questions about the origin of such photo de-mixing process remain unanswered, both in terms of its mechanism as well as thermodynamic description. Here, we relate in-situ measurements of ionic and electronic transport of mixed bromide-iodide perovskite thin films performed during photo de-mixing with the evolution of their optical and morpho-logical properties. The results point to the definition of different stages of the de-mixing process which, based on micros-copy and spectroscopic measurements, we assign to regimes of spinodal decomposition and nucleation of quasi-equilibrium iodide- and bromide-rich phases. Combined with density functional theory calculations, we explore the role of dimensionality in the mechanism and reversibility of photo de-mixing and dark re-mixing processes, referring to elec-tronic and ionic contributions to the de-mixing driving force. Additionally, our data emphasizes the role of the surface, as significantly different de-mixing dynamics, in terms of extent and reversibility, are observed for films with or without encapsulation. Our comprehensive analysis of transport, phase and optical properties of mixed-halide perovskites pro-vides guidelines for future materials design as well as for the more general fundamental understanding of light-induced ionic phenomena.

cond-mat.mtrl-sci↗

arXiv2025

Termination-Dependent Resistive Switching in SrTiO$_3$ Valence Change Memory Cells

Valence change memory (VCM) cells based on SrTiO$_3$ (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 and 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 at the origin of resistive switching in TiO$_2$-terminated devices by lowering the conduction band minimum of the STO layer, thus facilitating transmission through the Schottky barrier. Moreover, we investigate the possibility of filamentary switching in STO and reveal that it is most likely to occur at the Pt electrode of the SrO-terminated cells.

cond-mat.mtrl-sci↗

arXiv2025

Learning the Electronic Hamiltonian of Large Atomic Structures

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+ Angstroms) 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 $\leq$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.

cond-mat.mtrl-sci↗

arXiv2025

Electroforming Kinetics in HfOx/Ti RRAM: Mechanisms Behind Compositional and Thermal Engineering

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 over the electroforming voltage magnitude, but the exact mechanisms remain unclear. Here, we develop an understanding of how these mechanisms work within a standard a-HfO$_x$/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 to 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 with the same material stack. We identify a transition from primarily vertical to lateral ion movement dominating the filamentary growth process in sub-stoichiometric 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 HfO$_x$/Ti-based stacks, and provide guidelines that can be leveraged when fabricating devices.

cond-mat.mtrl-sci↗

arXiv2024

Invariant subspaces and PCA in nearly matrix multiplication time

Approximating invariant subspaces of generalized eigenvalue problems (GEPs) is a fundamental computational problem at the core of machine learning and scientific computing. It is, for example, the root of Principal Component Analysis (PCA) for dimensionality reduction, data visualization, and noise filtering, and of Density Functional Theory (DFT), arguably the most popular method to calculate the electronic structure of materials. Given Hermitian $H,S\in\mathbb{C}^{n\times n}$, where $S$ is positive-definite, let $Π_k$ be the true spectral projector on the invariant subspace that is associated with the $k$ smallest (or largest) eigenvalues of the GEP $HC=SCΛ$, for some $k\in[n]$. We show that we can compute a matrix $\widetildeΠ_k$ such that $\lVertΠ_k-\widetildeΠ_k\rVert_2\leq ε$, in $O\left( n^{ω+η}\mathrm{polylog}(n,ε^{-1},κ(S),\mathrm{gap}_k^{-1}) \right)$ bit operations in the floating point model, for some $ε\in(0,1)$, with probability $1-1/n$. Here, $η>0$ is arbitrarily small, $ω\lesssim 2.372$ is the matrix multiplication exponent, $κ(S)=\lVert S\rVert_2\lVert S^{-1}\rVert_2$, and $\mathrm{gap}_k$ is the gap between eigenvalues $k$ and $k+1$. To achieve such provable "forward-error" guarantees, our methods rely on a new $O(n^{ω+η})$ stability analysis for the Cholesky factorization, and a smoothed analysis for computing spectral gaps, which can be of independent interest. Ultimately, we obtain new matrix multiplication-type bit complexity upper bounds for PCA problems, including classical PCA and (randomized) low-rank approximation.

cs.DS↗

arXiv2024

Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks

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.

cs.NE↗

arXiv2022

An Atomistic Model of Field-Induced Resistive Switching in Valence Change Memory

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. Accurate simulations of 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 modelling VCM cells. It combines a stochastic Kinetic Monte Carlo approach for atomic rearrangement with a quantum transport scheme, both parameterized at the ab-initio level by using inputs from Density Functional Theory (DFT). Each of these steps operates directly on the underlying 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 non-volatile switching between high- and low-resistance states in an 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.

cond-mat.dis-nn↗

arXiv2022

An Atomistic Modelling Framework for Valence Change Memory Cells

We present a framework dedicated to modelling the resistive switching operation of Valence Change Memory (VCM) cells. The method combines an atomistic description of the device structure, a Kinetic Monte Carlo (KMC) model for the creation and diffusion of oxygen vacancies in the central oxide under an external field, and an ab-initio quantum transport method to calculate electrical current and conductance. As such, it reproduces a realistically stochastic device operation and its impact on the resulting conductance. We demonstrate this framework by simulating a switching cycle for a TiN/HfO$_2$/TiN VCM cell, and see a clear current hysteresis between high/low resistance states, with a conductance ratio of one order of magnitude. Additionally, we observe that the changes in conductance originate from the creation and recombination of vacancies near the active electrode, effectively modulating a tunnelling gap for the current. This framework can be used to further investigate the mechanisms behind resistive switching at an atomistic scale and optimize VCM material stacks and geometries.

cond-mat.mtrl-sci↗

arXiv2021

Photo de-mixing in Dion-Jacobson two-dimensional mixed halide perovskites

Two-dimensional (2D) halide perovskites feature a versatile structure, which not only enables the fine-tuning of their optoelectronic properties but also makes them appealing as model systems to investigate the fundamental properties of hybrid perovskites. In this study, we analyzed the changes in the optical absorption of 2D Dion-Jacobson mixed halide perovskite thin films (encapsulated) based on (PDMA)Pb(I0.5Br0.5)4 (PDMA: 1,4-phenylenedimethanammonium spacer) exposed to a constant illumination. We demonstrate that these 2D mixed-halide perovskites undergo photo de-mixing with direct transformation from the pristine phase to the de-mixed phases. Almost complete re-mixing of these phases occurs when the sample is left in the dark, showing that the process is reversible in terms of optical properties. On the other hand, exposure to light appears to induce structural changes in the thin film that are not reversible in the dark. We have further investigated temperature-dependent absorption measurements under light to extract the photo de-mixed compositions and to map the photo-miscibility-gap. This work thereby reveals that photo de-mixing occurs in Dion-Jacobson two-dimensional hybrid perovskites and provides strategies to address the role of light in the thermodynamic properties of these materials.

physics.chem-ph↗