Conventional artificial intelligence (AI) systems are facing bottlenecks due to the
fundamental mismatches between AI models, which rely on parallel, in-memory, and …

The slowing down of transistor scaling and explosive growth for intelligence computing
power emerge as the two driving factors for the study of novel devices and materials to …

Neuromorphic computing targets the hardware embodiment of neural network, and device
implementation of individual neuron and synapse has attracted considerable attention. The …

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

Translating the surging interest in neuromorphic electronic components, such as those
based on nonlinearities near Mott transitions, into large‐scale commercial deployment faces …

Memristors are drawing attention as neuromorphic hardware components because of their
non‐volatility and analog programmability. In particular, electrochemical metallization (ECM) …

Ferroelectric field-effect transistor (FeFET) can be operated as a nonvolatile memory device
with low programming voltage based on polarization. In particular, it can be used as a …

Since the early 2000s, the impending end of Moore's scaling, as the physical limits to
shrinking transistors have been approached, has fueled interest in improving the …

Diffusive memristors based on volatile electrochemical metallization (v‐ECM) devices are of
broad interest for applications in emerging memory technologies and neuromorphic …

MLP-Mixer based on multilayer perceptrons (MLPs) is a novel architecture of a
neuromorphic computing system (NCS) introduced for image classification tasks without …