Ferroelectric field-effect transistors (FeFETs) have been considered as promising electrically switchable nonvolatile data storage elements due to their fast switching speed, programmable conductance, and high dynamic range for neuromorphic applications. Meanwhile, FeFETs can be aggressively shrunk to the atomic scale for a high density device integration, ideally, without comprising the performance by introducing two-dimensional (2D) materials. So far, the demonstrated 2D material-based FeFETs mainly rely on mechanically exfoliated flakes, which are not favorable for large-scale industrial applications, and FeFETs based on organic ferroelectrics typically show a large writing voltage (e.g., >±20 V), making these types of memory devices imp...
In-memory computing featuring a radical departure from the von Neumann architecture is promising to ...
Online training of deep neural networks (DNN) can be significantly accelerated by performing in situ...
Online training of deep neural networks (DNN) can be significantly accelerated by performing in situ...
Development of unconventional computing architectures, including neuromorphic computing, relies heav...
An artificial synaptic element consisting of a three terminal Ferroelectric Field-Effect Transistor ...
We introduce a concept of programmable ferroelectric devices composed of two-dimensional (2D) and fe...
Development of unconventional computing architectures, including neuromorphic computing, relies heav...
Development of unconventional computing architectures, including neuromorphic computing, relies heav...
Due to the voltage driven switching at low voltages combined with nonvolatility of the achieved pola...
Ferroelecticity, one of the keys to realize nonvolatile memories owing to the remanent electric pola...
In ferroelectric materials, spontaneous symmetry breaking leads to a switchable electric polarizatio...
Throughout the 22 nm technology node HfO2 is established as a reliable gate dielectric in contempora...
The discovery of interfacial ferroelectricity in two-dimensional rhombohedral (3R)-stacked semicondu...
Recently, considerable attention has been paid to the development of advanced technologies such as a...
Neuromorphic computing architectures demand the development of analog, non-volatile memory component...
In-memory computing featuring a radical departure from the von Neumann architecture is promising to ...
Online training of deep neural networks (DNN) can be significantly accelerated by performing in situ...
Online training of deep neural networks (DNN) can be significantly accelerated by performing in situ...
Development of unconventional computing architectures, including neuromorphic computing, relies heav...
An artificial synaptic element consisting of a three terminal Ferroelectric Field-Effect Transistor ...
We introduce a concept of programmable ferroelectric devices composed of two-dimensional (2D) and fe...
Development of unconventional computing architectures, including neuromorphic computing, relies heav...
Development of unconventional computing architectures, including neuromorphic computing, relies heav...
Due to the voltage driven switching at low voltages combined with nonvolatility of the achieved pola...
Ferroelecticity, one of the keys to realize nonvolatile memories owing to the remanent electric pola...
In ferroelectric materials, spontaneous symmetry breaking leads to a switchable electric polarizatio...
Throughout the 22 nm technology node HfO2 is established as a reliable gate dielectric in contempora...
The discovery of interfacial ferroelectricity in two-dimensional rhombohedral (3R)-stacked semicondu...
Recently, considerable attention has been paid to the development of advanced technologies such as a...
Neuromorphic computing architectures demand the development of analog, non-volatile memory component...
In-memory computing featuring a radical departure from the von Neumann architecture is promising to ...
Online training of deep neural networks (DNN) can be significantly accelerated by performing in situ...
Online training of deep neural networks (DNN) can be significantly accelerated by performing in situ...