Artificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications

Abstract

Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed.

Memristor-Based Artificial Chips

“…Breakthroughs in powerful hardware and algorithms would bring a revolution of brain-like chips. , For brain-like chips, some automatic design toold, simulator platforms, and integration technology should be developed to support the system-level design of memristor-based computing chips. − The hardware–software codesign flow and platforms from device to algorithm is a foundation to design efficient computing chips. For the nanodevices and nanotechnology, the memristor-based nanodevice primitives should be optimized to meet the AI application requirements of computing chips, and developing integration technology is beneficial to the design of future large-scale computing chips.…”

Charge‐Mediated Copper‐Iodide‐Based Artificial Synaptic Device with Ultrahigh Neuromorphic Efficacy

“…Overcoming the drawbacks of the huge energy consumption in silicon-based neuromorphic computing devices, [2,14] our developed artificial synaptic device based on the CuI active layer exhibits ultralow-powered synaptic switching characteristics. Energy consumption for the synaptic operations are calculated from the relation Equation ( 4)…”

“…[13] The point-topoint architecture of the memristor, where two electrodes are layered between active film, is a perfect structural substitute for the biological synapse, where two neighboring neurons are connected through synapses. [14] Thus, the modulation of the synaptic weight and transmission characteristics of the biological synapse is well DOI: 10.1002/pssr.202300191…”

Homochiral Chemistry Strategy to Trigger Second-Harmonic Generation and Dual Dielectric Switches

“…Switchable materials are substances that can switch between two different states under external stimuli such as heat, light, external electric field, and magnetic fields; their physical properties change with the change of states. − These materials can be widely used in various fields such as communication transmission, information storage, and photonic carriers. − Dielectric switches are mostly appearing in structural phase transition materials, where the dielectric constant changes abruptly near the phase transition temperature ( T h ) and reversible cycling occurs. − For example, (DMTBA) 3 Bi 2 Br 9 (DMTBA = N , N -dimethyl- tert -butylaminiu) was reported to have a pair of reversible phase transitions at 188 and 425 K and exhibited excellent dielectric switching properties − However, a single dielectric switch also limits the application scenarios for the material.…”