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

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …

Brain-inspired electronics require artificial synapses that have ultra-low energy
consumption, high operating speed, and stable flexibility. Here, we demonstrate a flexible …

Memristors showcasing progressive conductance modulation are now recognized as one of
the most promising candidates to act like electronic synapses inside computer circuits …

Organic–inorganic hybrid perovskites (OHPs) with a rich reservoir of distinct physical
attributes have been considered as promising resistive switching materials for next …

High‐performance organic neuromorphic devices with miniaturized device size and
computing capability are essential elements for developing brain‐inspired humanoid …

Neuromorphic circuits emulating the bio‐brain functionality via artificial devices have
achieved a substantial scientific leap in the past decade. However, even with the advent of …

In neuromorphic computing networks, a flexible synaptic memristor with high recognition
accuracy is highly desired. In this study, ZnO nanosheets (ZnO NS) embedded within a …

Photonic in-memory computing exhibits promising potential to address the inherent
limitations of traditional von Neumann architecture. In this study, we demonstrate a tantalum …

Systems and methods are disclosed for communicating data in an integrated sensor network
having one or more nodes coupled to an optical sensor network and a radio frequency …