Traditional von Neumann computing systems involve separate processing and memory
units. However, data movement is costly in terms of time and energy and this problem is …

Guided by brain-like 'spiking'computational frameworks, neuromorphic computing—brain-
inspired computing for machine intelligence—promises to realize artificial intelligence while …

Many in-memory computing frameworks demand electronic devices with specific switching
characteristics to achieve the desired level of computational complexity. Existing memristive …

Deep-learning models have become pervasive tools in science and engineering. However,
their energy requirements now increasingly limit their scalability. Deep-learning …

Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient
approach to training neural networks,,–. However, convolutional neural networks (CNNs) …

Hafnium oxide (HfO2) is one of the mature high‐k dielectrics that has been standing strong
in the memory arena over the last two decades. Its dielectric properties have been …

The rapid increase in information in the big-data era calls for changes to information-
processing paradigms, which, in turn, demand new circuit-building blocks to overcome the …

Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …

Ion migration as well as electron transfer and coupling in resistive switching materials
endow memristors with a physically tunable conductance to resemble synapses, neurons …

In this article, we review the recent progress of ferroelectric field-effect transistors (FeFETs)
based on ferroelectric hafnium oxide (HfO 2), ten years after the first report on such a device …