In-memory computing (IMC) has emerged as a new computing paradigm able to alleviate or
suppress the memory bottleneck, which is the major concern for energy efficiency and …

HfO 2-based resistive switching memory (RRAM) combines several outstanding properties,
such as high scalability, fast switching speed, low power, compatibility with complementary …

To reach the energy efficiency and the computing capability of biological neural networks,
novel hardware systems and paradigms are required where the information needs to be …

Owing to their unique features such as thresholding and self‐relaxation behavior diffusive
memristors built from volatile electrochemical metallization (v‐ECM) devices are drawing …

Bio-mimetic advanced electronic systems are emerging rapidly, engrossing their
applications in neuromorphic computing, humanoid robotics, tactile sensors, and so forth …

With the increasing demand for artificially intelligent hardware systems for brain-inspired in-
memory and neuromorphic computing, understanding the underlying mechanisms in the …

The shift towards a distributed computing paradigm, where multiple systems acquire and
elaborate data in real-time, leads to challenges that must be met. In particular, it is becoming …

The Boltzmann Tyranny, set by thermionic statistics, dictates the lower limit of switching
slope (SS) of a MOSFET to be 60 mV/dec, the fundamental barrier for low-dissipative …

Memristors are playing an increasingly important role in develo** in-memory computing.
Versatile memristors which offer both volatile and non-volatile performances can be …

Memristive neuromorphic computing has emerged as a promising computing paradigm for
the upcoming artificial intelligence era, offering low power consumption and high speed …