A continuous exponential rise has been observed in the storage and processing of the data
that may not curtail in the foreseeable future. The required data processing speed and …

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 …

With the recently increasing prevalence of deep learning, both academia and industry
exhibit substantial interest in neuromorphic computing, which mimics the functional and …

Using “capacitive” crossbar arrays for compute-in-memory (CIM) offers higher energy
efficiency compared to “resistive” crossbar arrays. The non-volatile capacitive (nvCap) …

To analyze the effect of the intrinsic variations of the memristor device on the neuromorphic
system, we fabricated 32× 32 Al 2 O 3/TiO x-based memristor crossbar array and …

On-chip learning in neuromorphic systems, wherein both training and inference are
performed on memristive synaptic devices, has been actively studied recently. However, on …

In this work, two types of InGaZnO (IGZO) memristors were fabricated to confirm the
conduction mechanism and degradation characteristics of memristors with different …

Recently, transistor scaling is approaching its physical limit, hindering the further
development of the computing capability. In the post-Moore era, emerging logic and storage …

A ferroelectric field-effect-transistor (FeFET) with hafnium zirconium oxide ferroelectric layer
and hafnium silicate (HfSiOx) interlayer (IL) is demonstrated. Compared to a FeFET with …

Ferroelectric field effect transistors (FeFETs) as artificial synapses have attracted great
interests in neuromorphic computing. To enable the applications, the electrical response of …