Highly Reliable BiOSe Dendritic Neuron Enabling Spatial-Temporal Signal Processing for Real-World Image Classification.

Artificial intelligence (AI) has made significant strides by imitating biological neurons and synapses through simplified models, yet incomplete neuron functionalities can limit performance and energy efficiency in handling complex tasks. Biological neurons process input signals nonlinearly, utilizing dendrites to process spatial-temporal information. This study demonstrates the compact artificial dendrite device employing memristors based on bismuth oxyselenide (BiOSe).

Crystallinity-Controlled Hexagonal Boron Nitride-Based Memristors for Fully Integrated Reservoir Computing Processors.

Memristors based on 2D materials (2DMs) have attracted considerable research interest due to their excellent switching performance. Former synthesis methods for 2DMs aimed to synthesize 2DMs with a large grain size. However, these methods cause a stochastic distribution of defects in high-density memristor arrays, resulting in device nonuniformity.

A Highly Reliable Molybdenum Disulfide-Based Synaptic Memristor Using a Copper Migration-Controlled Structure.

Memristors are drawing attention as neuromorphic hardware components because of their non-volatility and analog programmability. In particular, electrochemical metallization (ECM) memristors are extensively researched because of their linear conductance controllability. Two-dimensional materials as switching medium of ECM memristors give advantages of fast speed, low power consumption, and high switching uniformity.

Conductive-bridging random-access memories for emerging neuromorphic computing.

With the increasing utilisation of artificial intelligence, there is a renewed demand for the development of novel neuromorphic computing owing to the drawbacks of the existing computing paradigm based on the von Neumann architecture. Extensive studies have been performed on memristors as their electrical nature is similar to those of biological synapses and neurons. However, most hardware-based artificial neural networks (ANNs) have been developed with oxide-based memristors owing to their high compatibility with mature complementary metal-oxide-semiconductor (CMOS) processes.