With the booming growth of artificial intelligence (AI), the traditional von Neumann
computing architecture based on complementary metal oxide semiconductor devices are …

Self-rectifying memristor with integrated excellent bio-synaptic behaviors are great potential
to realize high-density memristor neuromorphic networks with self-inhibition of stealth …

Neuromorphic computing systems based on high-temperature-resistant synaptic devices
have emerged as energy-efficient and intelligent strategies for harsh-environment …

Halide perovskites are promising for energy-efficient and reconfigurable artificial synapses
due to the unique charge transport characteristic, ie, ionic-electronic coupling. This work …

Photonic artificial synapses have become favorable candidates for the basic architecture of
neuromorphic computing systems due to their high bandwidth, low power consumption, and …

This work focused on plasma-induced nitrogen-doped indium gallium zinc oxide (InGaZnO:
N) based resistive switching devices. Nitrogen atoms in the InGaZnO: N can reduce the …

Memristor synapses based on green and pollution-free organic materials are expected to
facilitate biorealistic neuromorphic computing and to be an important step toward the next …

The human brain possesses a remarkable ability to memorize information with the
assistance of a specific external environment. Therefore, mimicking the human brain's …

We report on hybrid memristor devices consisting of germanium dioxide nanoparticles
(GeO2 NP) embedded within a poly (methyl methacrylate)(PMMA) thin film. Besides …

Integrating resistive memory or neuromorphic memristors into mainstream silicon technology
can be substantially facilitated if the memories are built in the back-end-of-line (BEOL) and …