arXiv subjects

Atreya Majumdar

Publications and source records attributed to Atreya Majumdar.

8 recordsLinked to original sources

arXiv2026

Inferring Magnetic Material Parameters from Statistical Measures in Strongly Fluctuating Magnetization Dynamics

Magnetic material parameters such as the exchange stiffness and magnetic anisotropy govern the behavior and functionality of magnetic systems, yet their local inference from magnetization data remains challenging, particularly in strongly fluctuating regimes with polycrystalline or multiphase microstructure, where conventional texture-based methods become unreliable. We introduce a magnetization-only framework for inferring material parameters from thermally driven magnetization dynamics. Using micromagnetic simulations, we extract statistical quantities such as temporal mean and latent entropy from the magnetization dynamics, fit models to these descriptors, and invert the models to infer material parameters. We show that this framework enables material-parameter inference as well as grain-boundary detection in a heterogeneous sample. Among the descriptors considered, latent entropy yields more accurate parameter estimates than the temporal mean. Our results establish latent entropy as an efficient descriptor for inferring magnetic material parameters from dynamical magnetization data and point toward its use for experimental parameter extraction at high temperatures and, more broadly, under strongly fluctuating conditions.

cond-mat.mtrl-sci↗

arXiv2026

Photo-induced currents and short-term memory for reservoir computing in a ferroelectric semiconductor

Physical reservoir computing represents an energy efficient approach for processing temporal signals by exploiting the intrinsic nonlinear dynamics and fading memory of a physical system. Recently, ferroelectric semiconductors moved into focus as reservoir materials motivated by their versatile electronic responses to external stimuli. Here, we explore the fundamental possibility to recognize time-varying light pulses via photo-induced currents, using the small-band-gap p-type semiconductor ErMnO$_3$ as a model system. Under white light illumination, ErMnO$_3$ exhibits non-linearly evolving photo-induced currents and controllable relaxation dynamics that naturally realize the high-dimensional projection and fading memory capabilities required for reservoir computing. The reservoir capability of ErMnO$_3$ is reflected by the improved recognition accuracy of "Past" input pulses, which increases from ~33% to ~93% after applying reservoir transformation to the input signal. The results present ferroelectric hexagonal manganites as a promising platform for photo-induced current-based reservoir computing and highlight the potential of light-driven oxide semiconductors for temporal information processing.

cond-mat.mtrl-sci↗

arXiv2026

Defect Detection in Magnetic Systems Using U-Net and Statistical Measures

Local material inhomogeneities can strongly influence magnetization dynamics and macroscopic magnetic properties, yet detecting such defects from magnetic imaging data remains challenging when thermal fluctuations and experimental noise obscure static contrast. Here, we investigate defect detection in strongly fluctuating magnetization regimes where signatures of inhomogeneities largely average out in time-resolved measurements. Using finite-temperature micromagnetic simulations with randomly distributed defects and material parameters representative of \ce{Ni80Fe20}, we compute per-pixel temporal mean, temporal standard deviation, and latent entropy and use them as inputs for U-Net-based semantic segmentation models. We find that the most effective descriptor depends on the noise level and, importantly, that robust detection requires training data that reflect the expected noise statistics. These results provide practical guidance for designing noise-robust defect-detection workflows in magnetic imaging.

cond-mat.mtrl-sci↗

arXiv2026

Leveraging Interactions for Efficient Swarm-Based Brownian Computing

Drawing inspiration from swarm intelligence, we show that short-range attractive interactions between thermally driven Brownian quasiparticles enable energy-efficient optimization. As quasiparticles can be generated directly within a material, the swarm size can be adjusted with minimal energy overhead. Using an optimization task defined by a spatially varying temperature landscape, we quantitatively show that interacting swarms reliably identify global optima and significantly outperform non-interacting searchers within a well-defined regime of interaction strength and swarm size. This improvement arises from emergent cooperative behavior, where local interactions guide the swarm toward high-quality solutions without central coordination. To link our physical model to experimental realizations, we coarse-grain the quasiparticle dynamics onto a sensor lattice and generate trajectories emulating particle-tracking measurements. We further show that the interacting swarm adapts robustly to landscapes that evolve over time. These findings establish interacting Brownian quasiparticles as a physical platform for scalable and energy-efficient unconventional computing.

cond-mat.stat-mech↗

arXiv2024

Neuromorphic Spintronics

Neuromorphic spintronics combines two advanced fields in technology, neuromorphic computing and spintronics, to create brain-inspired, efficient computing systems that leverage the unique properties of the electron's spin. In this book chapter, we first introduce both fields - neuromorphic computing and spintronics and then make a case for neuromorphic spintronics. We discuss concrete examples of neuromorphic spintronics, including computing based on fluctuations, artificial neural networks, and reservoir computing, highlighting their potential to revolutionize computational efficiency and functionality.

cond-mat.mtrl-sci↗

arXiv2023

Novel implementations for reservoir computing -- from spin to charge

Topological textures in magnetic and electric materials are considered to be promising candidates for next-generation information technology and unconventional computing. Here, we discuss how the physical properties of topological nanoscale systems, such as skyrmions and domain walls, can be leveraged for reservoir computing, translating non-linear problems into linearly solvable ones. In addition to the necessary requirements of physical reservoirs, the topological textures give new opportunities for the downscaling of devices, enhanced complexity, and versatile input and readout options. Our perspective article presents topological magnetic and electric defects as an intriguing platform for non-linear signal conversion, giving a new dimension to reservoir computing and in-materio computing in general.

physics.app-ph↗

arXiv2023

Powering AI at the Edge: A Robust, Memristor-based Binarized Neural Network with Near-Memory Computing and Miniaturized Solar Cell

Memristor-based neural networks provide an exceptional energy-efficient platform for artificial intelligence (AI), presenting the possibility of self-powered operation when paired with energy harvesters. However, most memristor-based networks rely on analog in-memory computing, necessitating a stable and precise power supply, which is incompatible with the inherently unstable and unreliable energy harvesters. In this work, we fabricated a robust binarized neural network comprising 32,768 memristors, powered by a miniature wide-bandgap solar cell optimized for edge applications. Our circuit employs a resilient digital near-memory computing approach, featuring complementarily programmed memristors and logic-in-sense-amplifier. This design eliminates the need for compensation or calibration, operating effectively under diverse conditions. Under high illumination, the circuit achieves inference performance comparable to that of a lab bench power supply. In low illumination scenarios, it remains functional with slightly reduced accuracy, seamlessly transitioning to an approximate computing mode. Through image classification neural network simulations, we demonstrate that misclassified images under low illumination are primarily difficult-to-classify cases. Our approach lays the groundwork for self-powered AI and the creation of intelligent sensors for various applications in health, safety, and environment monitoring.

cs.ET↗

arXiv2021

Model of the Weak Reset Process in HfOx Resistive Memory for Deep Learning Frameworks

The implementation of current deep learning training algorithms is power-hungry, owing to data transfer between memory and logic units. Oxide-based RRAMs are outstanding candidates to implement in-memory computing, which is less power-intensive. Their weak RESET regime, is particularly attractive for learning, as it allows tuning the resistance of the devices with remarkable endurance. However, the resistive change behavior in this regime suffers many fluctuations and is particularly challenging to model, especially in a way compatible with tools used for simulating deep learning. In this work, we present a model of the weak RESET process in hafnium oxide RRAM and integrate this model within the PyTorch deep learning framework. Validated on experiments on a hybrid CMOS/RRAM technology, our model reproduces both the noisy progressive behavior and the device-to-device (D2D) variability. We use this tool to train Binarized Neural Networks for the MNIST handwritten digit recognition task and the CIFAR-10 object classification task. We simulate our model with and without various aspects of device imperfections to understand their impact on the training process and identify that the D2D variability is the most detrimental aspect. The framework can be used in the same manner for other types of memories to identify the device imperfections that cause the most degradation, which can, in turn, be used to optimize the devices to reduce the impact of these imperfections.

cs.LG↗