Related papers: A Ferroelectric Tunnel Junction-based Integrate-an…
Embedded systems acquire information about the real world from sensors and process it to make decisions and/or for transmission. In some situations, the relationship between the data and the decision is complex and/or the amount of data to…
Recent years have witnessed growing interest in the field of brain-inspired computing based on neural-network architectures. In order to translate the related algorithmic models into powerful, yet energy-efficient cognitive-computing…
We develop a fast Magnetic Josephson Junction (MJJ) - a superconducting ferromagnetic device for a scalable high-density cryogenic memory compatible in speed and fabrication with energy-efficient Single Flux Quantum (SFQ) circuits. We…
Antiferromagnetic (AFM) spintronics has emerged as a subfield of spintronics, where an AFM N\'eel vector is used as a state variable. Efficient electric control and detection of the N\'eel vector are critical for spintronic applications.…
The continuous development of superconducting electronics is encouraging several studies on hybrid Josephson junctions (JJs) based on superconductor/ferromagnet/superconductor (SFS) heterostructures, as either spintronic devices or…
The escalating demand for memory scaling requires switching mechanisms that remain reliable at atomic thickness while operating with minimal energy consumption. Sliding ferroelectricity provides a promising platform for this challenge: the…
Perpendicular magnetic tunnel junctions (p-MTJs) switched utilizing bipolar electric fields have extensive applications in energy-efficient memory and logic devices. Voltage-controlled magnetic anisotropy linearly lowers the energy barrier…
Stochastic spiking neural networks based on nanoelectronic spin devices can be a possible pathway to achieving "brainlike" compact and energy-effcient cognitive intelligence. The computational model attempt to exploit the intrinsic device…
Physical devices exhibiting stochastic functions with low energy consumption and high device density have the potential to enable complex probability-based computing algorithms, accelerate machine learning tasks, and enhance hardware…
Analog electronic non-volatile memories mimicking synaptic operations are being explored for the implementation of neuromorphic computing systems. Compound synapses consisting of ensembles of stochastic binary elements are alternatives to…
The straintronic magnetic tunnel junction (s-MTJ) is an MTJ whose resistance state can be changed continuously or gradually from high to low with a gate voltage that generates strain the magnetostrictive soft layer. This unusual feature,…
In this work, we present a clear evidence, based on numerical simulations and experiments, that the polarization compensation due to trapped charge strongly influences the ON/ OFF ratio in Hf 0.5 Zr 0.5 O 2 (HZO)-based ferroelectric tunnel…
Neuromorphic computing offers a pathway toward energy-efficient processing of data, yet hardware platforms combining nanoscale integration and multimodal functionality remain scarce. Here we demonstrate a gallium-phosphide…
The human brain comprises about a hundred billion neurons connected through quadrillion synapses. Spiking Neural Networks (SNNs) take inspiration from the brain to model complex cognitive and learning tasks. Neuromorphic engineering…
Neuromorphic chip refers to an unconventional computing architecture that is modelled on biological brains. It is ideally suited for processing sensory data for intelligence computing, decision-making or context cognition. Despite rapid…
Achieving brain-like density and performance in neuromorphic computers necessitates scaling down the size of nanodevices emulating neuro-synaptic functionalities. However, scaling nanodevices results in reduction of programming resolution…
Neuromorphic computing is an emerging computing paradigm that moves away from batched processing towards the online, event-driven, processing of streaming data. Neuromorphic chips, when coupled with spike-based sensors, can inherently adapt…
The ferroelectric (FE) control of electronic transport is one of the emerging technologies in oxide heterostructures. Many previous studies in FE tunnel junctions (FTJs) exploited solely the differences in the electrostatic potential across…
Neuromorphic computing and spiking neural networks (SNN) mimic the behavior of biological systems and have drawn interest for their potential to perform cognitive tasks with high energy efficiency. However, some factors such as temporal…
Although inspired by neuronal systems in the brain, artificial neural networks generally employ point-neurons, which offer far less computational complexity than their biological counterparts. Neurons have dendritic arbors that connect to…