Printed electronics including large‐area sensing, wearables, and bioelectronic systems are
often limited to simple circuits and hence it remains a major challenge to efficiently store …

Recently, interest in programmable photonics integrated circuits has grown as a potential
hardware framework for deep neural networks, quantum computing, and field programmable …

Resistive switching (RS) behavior can serve as a building block in the development of non-
volatile memory and neuromorphic computing applications. Thus far, various device …

With the arrival of the era of big data, the conventional von Neumann architecture is now
insufficient owing to its high latency and energy consumption that originate from its …

Organic memristors are promising candidates for the flexible synaptic components of
wearable intelligent systems. With heightened concerns for the environment, considerable …

Emerging energy-efficient neuromorphic circuits are based on hardware implementation of
artificial neural networks (ANNs) that employ the biomimetic functions of memristors …

We present a new methodology to quantify the variability of resistive switching memories.
Instead of statistically analyzing few data points extracted from current versus voltage (I–V) …

Resistive-switching-based memory devices meet most of the requirements for use in next-
generation information and communication technology applications, including standalone …

Memristors are promising candidates for synapse emulation in brain-inspired neuromorphic
computing systems. The main obstacle to their usage in such systems is high variability of …

Memristive devices, known for their nonvolatile resistive switching, are promising
components for next-generation neuromorphic computing systems, which mimic the brain's …