Related papers: Numerical study of synaptic behavior in amorphous …
The persistent and switchable polarization of ferroelectric materials based on HfO$_2$-based ferroelectric compounds, compatible with large-scale integration, are attractive synaptic elements for neuromorphic computing. To achieve a record…
Memristors can mimic the functions of biological synapse, where it can simultaneously store the synaptic weight and modulate the transmitted signal. Here, we report Nb/Nb2O5/Pt based memristors with bipolar resistive switching, exhibiting…
Spin-electronic devices are poised to become part of mainstream microelectronic technology .Downsizing them, however, faces the intrinsic difficulty that as ferromagnets become smaller, it becomes more difficult to stabilize their magnetic…
Biological neurons and their in-silico emulations for neuromorphic artificial intelligence (AI) use extraordinarily energy-efficient mechanisms, such as spike-based communication and local synaptic plasticity. It remains unclear whether…
This tutorial describes challenges and possible avenues for the implementation of the components of a solid-state system, which emulates a biological brain. The tutorial is devoted mostly to a charge-based (i.e. electric controlled)…
We consider a fully-connected network of leaky integrate-and-fire neurons with spike-timing-dependent plasticity. The plasticity is controlled by a parameter representing the expected weight of a synapse between neurons that are firing…
We present a low barrier magnet based compact hardware unit for analog stochastic neurons and demonstrate its use as a building-block for neuromorphic hardware. By coupling circular magnetic tunnel junctions (MTJs) with a CMOS based analog…
Hysteresis and memory effects in graphene field-effect transistors (GFETs) offer unique opportunities for neuromorphic computing, sensing, and memory applications, yet their physical origins remain debated due to competing volatile and…
Many organisms leverage an interplay between shape and activity to generate motion and adapt to their environment. Embedding such feedback into synthetic microrobots could eliminate the need for sensors, software, and actuators, yet current…
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…
Electric field-induced studies, including phase transition and polarization hysteresis, for ferroelectric HfO2 at the atomic scale are critical since they can largely affect its application in ferroelectric and dielectric devices. However,…
Neuromorphic computing based on spiking neural networks has the potential to significantly improve on-line learning capabilities and energy efficiency of artificial intelligence, specially for edge computing. Recent progress in…
We present a study based on numerical simulations and comparative analysis of recent experimental data concerning the operation and design of FeFETs. Our results show that a proper consideration of charge trapping in the…
Memristors are considered key building blocks for the development of neuromorphic computing hardware. For ferroelectric memristors with a capacitor-like structure, the polarization direction modulates the height of the Schottky barriers --…
Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of…
Electronic nematicity, a correlated state characterized by broken rotational symmetry, has been recognized as a ubiquitous feature intertwined with unconventional electron pairing in various iron-based superconductors. Here we employ…
Abstract: Bionic learning with fused sensing, memory and processing functions outperforms artificial neural networks running on silicon chips in terms of efficiency and footprint. However, digital hardware implementation of bionic learning…
While most neuromorphic systems are based on nanoscale electronic devices, nature relies on ions for energy-efficient information processing. Therefore, finding memristive nanofluidic devices is a milestone toward realizing electrolytic…
Ferroelectricity is intriguing for its spontaneous electric polarization, which is switchable by an external electric field. Expanding ferroelectric materials to two-dimensional limit will provide versatile applications for the development…
In the study of neurons, morphology influences function. The complexity in the structure of neurons poses a challenge in the identification and analysis of similar and dissimilar neuronal cells. Existing methodologies carry out structural…