Ferroelectric hafnium and zirconium oxides have undergone rapid scientific development
over the last decade, pushing them to the forefront of ultralow-power electronic systems …

HfO2-based ferroelectric memories have garnered significant attention for their potential to
serve as artificial synaptic devices owing to their scalability and CMOS compatibility. This …

Deep random forest (DRF), which combines deep learning and random forest, exhibits
comparable accuracy, interpretability, low memory and computational overhead to deep …

Non-volatile memories (NVMs) have the potential to reshape next-generation memory
systems because of their promising properties of near-zero leakage power consumption …

General-purpose multiply-accumulate (MAC) accelerators have become inevitable in the
internet-of-things (IoT) edge devices for performing computationally intensive tasks such as …

Field programmable gate array (FPGA) is widely used in the acceleration of deep learning
applications because of its reconfigurability, flexibility, and fast time-to-market. However …

Non-volatile ferroelectric field-effect transistor (FeFET) technology is a promising CMOS
process compatible solution for fast, energy efficient on-chip memories that are needed to …

Multi-level cell (MLC) ferroelectric FETs (FeFETs) face critical reliability challenges including
variation, endurance and write disturb. In this work, we proposed an innovative solution to …

Recently, with the development of the Internet of Things and Artificial Intelligence, higher
energy efficiency, density, and performance in on-chip memories and intelligent computing …

In this work, the impact of read scheme together with polarization state of the ferroelectric
(FE) domains on the interface trap behavior of FeFETs for NOR array applications were …