Resistance random access memories (RRAM) or memristors with an analog change of
conductance are widely explored as an artificial synapse, eg, Pr 0.7 Ca 0.3 MnO 3 (PCMO) …

To process data operations more efficiently in deep neural networks (DNNs), studies on
spiking neural networks (SNNs) have been conducted. In the reported literature, CMOS …

In the literature, various pulse-based programming schemes have been used to mimic
typical spike time-dependent plasticity (STDP)-based learning rule observed in biological …

This article is an overview of emerging memory materials and their role in advanced brain-
inspired computing technologies. It starts with the progress of memory technologies over the …

Resistance random access memories (RRAMs) are widely explored to show spike time
dependent plasticity (STDP) as a learning rule to show biological synaptic behavior, as …

The neural network enables efficient solutions for Nondeterministic Polynomial-time (NP)
hard problems, which are challenging for conventional von Neumann computing. The …

In filamentary RRAM, the role of self-heating in set/reset (by ion transport) is well
established. However, in nonfilamentary Pr 0.7 Ca 0.3 MnO 3 (PCMO) RRAM, self-heating …

Previous studies have mainly focused on the resistive switching (RS) of amorphous or
polycrystalline HfO 2-RRAM. The RS of single crystalline HfO 2 films has been rarely …

Resistive random-access memory (RRAM) devices that can execute logic are promising for
in-memory computing. In state-of-the-art, a complementary resistive switch (CRS) bit-cell of …

As one of the promising non-volatile memories (NVMs), resistive random access memory
(RRAM) has attracted extensive attention. Conventional RRAM is deeply dependent on …