Ultrafast and Low-Power 2D Bi₂O₂Se Memristors for Neuromorphic Computing Applications

Abstract

Memristors that emulate synaptic plasticity are building blocks for opening a new era of energy-efficient neuromorphic computing architecture, which will overcome the limitation of the von Neumann bottleneck. Layered two-dimensional (2D) Bi2O2Se, as an emerging material for next-generation electronics, is of great significance in improving the efficiency and performance of memristive devices. Herein, high-quality Bi2O2Se nanosheets are grown by configuring mica substrates face-down on the Bi2O2Se powder. Then, bipolar Bi2O2Se memristors are fabricated with excellent performance including ultrafast switching speed (<5 ns) and low-power consumption (<3.02 pJ). Moreover, synaptic plasticity, such as long-term potentiation/depression (LTP/LTD), paired-pulse facilitation (PPF), and spike-timing-dependent plasticity (STDP), are demonstrated in the Bi2O2Se memristor. Furthermore, MNIST recognition with simulated artificial neural networks (ANN) based on conductance modification could reach a high accuracy of 91%. Notably, the 2D Bi2O2Se enables the memristor to possess ultrafast and low-power attributes, showing great potential in neuromorphic computing applications.

N-Doped Graphene/MXene Nanocomposite as a Temperature-Adaptive Neuromorphic Memristor

“…This change is reversible and can be repeatedly erased and written. Due to the reconfigurability and adaptivity of the memristor, it can achieve spike-timing-dependent plasticity (STDP), short-term potentiation/depression (STP/STD), long-term potentiation/depression (LTP/LTD), and paired-pulse facilitation (PPF), which can be used to develop AI and neural network systems, commonly known as electrical synapses. − Electrical synapses have been widely studied to explore their potential for simulation application in neural networks, pattern recognition, and intelligent control. , …”

Silver Nanowire Networks with Moisture-Enhanced Learning Ability

“…The human brain is capable of processing information in a parallel way, involving various functions, such as signal processing, perception, recognition, and learning. − The simulation of certain human brain functions is considered valuable and significant in the field of intelligent electronic devices. Different types of artificial electronic devices to mimic the functions of the human brain have been developed, including two-terminal memristor devices, , electrically excited transistors, , and optically excited transistors. , Most of the efforts have been focused on simulating the biological synaptic plasticity, including facilitation, − excitatory postsynaptic current, − long-term/short-term plasticity, − spike-timing-dependent plasticity, − and spike-rate-dependent plasticity. − Note that the biological nervous system is not a static system but rather a highly adaptive and dynamic system. It continuously responds to changes in the environment, adjusting its connectivity and neural activity accordingly.…”

Low-dimensional nanostructures for monolithic 3D-integrated flexible and stretchable electronics

“…Furthermore, nanomaterials, composed of nanostructures, exhibit distinct properties in comparison to bulk materials, including increased surface area and enhanced reactivity, attributable to their nanoscale dimensions. Representative nanostructures, including nanodots, nanowires, nanobelts, and nanofilms, find extensive use in advanced devices such as sensors, 5,22 transistors, 23 memory, 24,25 light-emitting diodes (LEDs), 26,27 laser diodes, 28,29 photodetectors, [30][31][32] and solar cells. 33,34 Due to their exceptional mechanical, electronic, and/or optical properties, numerous types of low-dimensional nanostructures have been progressively developed for flexible/stretchable electronics to meet the diverse needs and requirements of information sensing, processing, and interactive loops.…”