The conventional computing architecture faces substantial challenges, including high
latency and energy consumption between memory and processing units. In response, in …

Resistive switching (RS) phenomena are reversible changes in the metastable resistance
state induced by external electric fields. After discovery∼ 50 years ago, RS phenomena …

Memristor crossbar with programmable conductance could overcome the energy
consumption and speed limitations of neural networks when executing core computing tasks …

Although several types of architecture combining memory cells and transistors have been
used to demonstrate artificial synaptic arrays, they usually present limited scalability and …

With the advent of the era of big data, resistive random access memory (RRAM) has become
one of the most promising nanoscale memristor devices (MDs) for storing huge amounts of …

With its comprehensive coverage, this reference introduces readers to the wide topic of
resistance switching, providing the knowledge, tools, and methods needed to understand …

Brain‐inspired neuromorphic computing is a new paradigm that holds great potential to
overcome the intrinsic energy and speed issues of traditional von Neumann based …

To implement artificial neural networks (ANNs) based on memristor devices, it is essential to
secure the linearity and symmetry in weight update characteristics of the memristor, and …

Inspired by the human brain, nonvolatile memories (NVMs)–based neuromorphic computing
emerges as a promising paradigm to build power-efficient computing hardware for artificial …

Organic‐inorganic halide perovskites (OHPs) have been intensively studied for application
in solar cells with high conversion efficiency exceeding 22%. The unique electrical and …