Resistive switching (RS) devices, often referred to as memristors, have exhibited interesting
electronic performance that could be useful to enhance the capabilities of multiple types of …

We provide an overview of the field of oxide-based filamentary resistive random access
memory (RRAM) for deep learning neural networks (DNNs). After introducing the electrical …

Artificial neural networks are notoriously power-and time-consuming when implemented on
conventional von Neumann computing systems. Consequently, recent years have seen an …

Write-variability and resistance instability are major reliability concerns impeding
implementation of oxide-based memristive devices in neuromorphic systems. The root …

Recently, ferroelectric tunnel junctions (FTJs) have gained extensive attention as possible
candidates for emerging memory and synaptic devices for neuromorphic computing …

Artificial synapse with good linearity is a critical issue in conductive bridging random access
memory (CBRAM) synaptic device to accomplish an efficient learning approach in the …

Computing-in-memory (CIM) architectures have paved the way for energy-efficient artificial
intelligence (AI) systems while outperforming von Neumann architectures. In particular …

Memristor devices can be utilized for various computing applications, and stochastic
computing is one of them. The intrinsic stochastic characteristics of the memristor cause …

In recent years, neuromorphic computing has been rapidly developed to overcome the
limitations of von Neumann architecture. In this regard, the demand for high‐performance …

In this work, a new operation scheme is developed to improve the state stability of multi-level
resistive random-access memory (RRAM) array. We found that the state instability after …