Ferroelectric materials, the charge equivalent of magnets, have been the subject of
continued research interest since their discovery more than 100 years ago. The …

Ferroelectric transistors (FeFETs) based on doped hafnium oxide (HfO2) have received
much attention due to their technological potential in terms of scalability, high-speed, and …

The discovery of ferroelectric doped HfO2 enabled the emergence of scalable and CMOS-
compatible ferroelectric field-effect transistor (FeFET) technology which has the potential to …

Advances in graph neural network (GNN)-based algorithms enable machine learning on
relational data. GNNs are computationally demanding since they rely upon backpropagation …

Data intensive applications such as AI (Artificial Intelligence) and IoT (Internet of Things)
demand high performance and highly reliable non-volatile memories (NVM). FeFET offers …

Recent discovery of ferroelectricity in doped HfO2 has reignited research interest in the
ferroelectric field-effect transistor (FeFET) as emerging embedded nonvolatile memory with …

Ferroelectric-doped HfO 2-based ferroelectric field-effect transistors (FeFETs) are being
actively explored as emerging nonvolatile memory devices with the potential for in-memory …

Ferroelectric Field-Effect Transistor (FeFET) is a promising future technology for non-volatile
on-chip memories. It is rapidly attracting an ever-increasing attention from industry. The key …

Brain-inspired hyperdimensional computing (HDC) is continuously gaining remarkable
attention. It is a promising alternative to traditional machine-learning approaches due to its …

In our work we describe and demonstrate an alternative approach of integrating 1T-1C
FeFET having separated transistor (1T) without modifying frontend CMOS technology and …