Reconfigurable Low-Voltage Hexagonal Boron Nitride Nonvolatile Switches for Millimeter-Wave Wireless Communications

Programmable Retention Characteristics in MoS2-Based Atomristors for Neuromorphic and Reservoir Computing Systems

In this study, we investigate the coexistence of short- and long-term memory effects owing to the programmable retention characteristics of a two-dimensional Au/MoS2/Au atomristor device and determine the impact of these effects on synaptic properties. This device is constructed using bilayer MoS2 in a crossbar structure. The presence of both short- and long-term memory characteristics is proposed by using a filament model within the bilayer transition-metal dichalcogenide. Short- and long-term properties are validated based on programmable multilevel retention tests. Moreover, we confirm various synaptic characteristics of the device, demonstrating its potential use as a synaptic device in a neuromorphic system. Excitatory postsynaptic current, paired-pulse facilitation, spike-rate-dependent plasticity, and spike-number-dependent plasticity synaptic applications are implemented by operating the device at a low-conductance level. Furthermore, long-term potentiation and depression exhibit symmetrical properties at high-conductance levels. Synaptic learning and forgetting characteristics are emulated using programmable retention properties and composite synaptic plasticity. The learning process of artificial neural networks is used to achieve high pattern recognition accuracy, thereby demonstrating the suitability of the use of the device in a neuromorphic system. Finally, the device is used as a physical reservoir with time-dependent inputs to realize reservoir computing by using short-term memory properties. Our study reveals that the proposed device can be applied in artificial intelligence-based computing applications by utilizing its programmable retention properties.

Low Power Volatile and Nonvolatile Memristive Devices from 1D MoO2-MoS2 Core-Shell Heterostructures for Future Bio-Inspired Computing

Memristors-based integrated circuits for emerging bio-inspired computing paradigms require an integrated approach utilizing both volatile and nonvolatile memristive devices. Here, an innovative architecture comprising of 1D CVD-grown core-shell heterostructures (CSHSs) of MoO2-MoS2 is employed as memristors manifesting both volatile switching (with high selectivity of 107 and steep slope of 0.6 mV decade-1) and nonvolatile switching phenomena (with Ion/Ioff ≈103 and switching speed of 60 ns). In these CSHSs, the metallic core MoO2 with high current carrying capacity provides a conformal and immaculate interface with semiconducting MoS2 shells and therefore it acts as a bottom electrode for the memristors. The power consumption in volatile devices is as low as 50 pW per set transition and 0.1 fW in standby mode. Voltage-driven current spikes are observed for volatile devices while with nonvolatile memristors, key features of a biological synapse such as short/long-term plasticity and paired pulse facilitation are emulated suggesting their potential for the development of neuromorphic circuits. These CSHSs offer an unprecedented solution for the interfacial issues between metallic electrodes and the layered materials-based switching element with the prospects of developing smaller footprint memristive devices for future integrated circuits.

Resistive Memory Devices at the Thinnest Limit: Progress and Challenges

The Si-based integrated circuits industry has been developing for more than half a century, by focusing on the scaling-down of transistor. However, the miniaturization of transistors will soon reach its physical limits, thereby requiring novel material and device technologies. Resistive memory is a promising candidate for in-memory computing and energy-efficient synaptic devices that can satisfy the computational demands of the future applications. However, poor cycle-to-cycle and device-to-device uniformities hinder its mass production. 2D materials, as a new type of semiconductor, is successfully employed in various micro/nanoelectronic devices and have the potential to drive future innovation in resistive memory technology. This review evaluates the potential of using the thinnest advanced materials, that is, monolayer 2D materials, for memristor or memtransistor applications, including resistive switching behavior and atomic mechanism, high-frequency device performances, and in-memory computing/neuromorphic computing applications. The scaling-down advantages of promising monolayer 2D materials including graphene, transition metal dichalcogenides, and hexagonal boron nitride are presented. Finally, the technical challenges of these atomic devices for practical applications are elaborately discussed. The study of monolayer-2D-material-based resistive memory is expected to play a positive role in the exploration of beyond-Si electronic technologies.

First-Principles Prediction of High and Low Resistance States in Ta/h-BN/Ta Atomristor

Two-dimensional (2D) materials have received significant attention for their potential use in next-generation electronics, particularly in nonvolatile memory and neuromorphic computing. This is due to their simple metal-insulator-metal (MIM) sandwiched structure, excellent switching performance, high-density capability, and low power consumption. In this work, using comprehensive material simulations and device modeling, the thinnest monolayer hexagonal boron nitride (h-BN) atomristor is studied by using a MIM configuration with Ta electrodes. Our first-principles calculations predicted both a high resistance state (HRS) and a low resistance state (LRS) in this device. We observed that the presence of van der Waals (vdW) gaps between the Ta electrodes and monolayer h-BN with a boron vacancy (VB) contributes to the HRS. The combination of metal electrode contact and the adsorption of Ta atoms onto a single VB defect (TaB) can alter the interface barrier between the electrode and dielectric layer, as well as create band gap states within the band gap of monolayer h-BN. These band gap states can shorten the effective tunneling path for electron transport from the left electrode to the right electrode, resulting in an increase in the current transmission coefficient of the LRS. This resistive switching mechanism in monolayer h-BN atomristors can serve as a theoretical reference for device design and optimization, making them promising for the development of atomristor technology with ultra-high integration density and ultra-low power consumption.

Volatile and Nonvolatile Resistive Switching Coexistence in Conductive Point Hexagonal Boron Nitride Monolayer

Recently, we demonstrated the nonvolatile resistive switching effects of metal-insulator-metal (MIM) atomristor structures based on two-dimensional (2D) monolayers. However, there are many remaining combinations between 2D monolayers and metal electrodes; hence, there is a need to further explore 2D resistance switching devices from material selections to future perspectives. This study investigated the volatile and nonvolatile switching coexistence of monolayer hexagonal boron nitride (h-BN) atomristors using top and bottom silver (Ag) metal electrodes. Utilizing an h-BN monolayer and Ag electrodes, we found that the transition between volatile and nonvolatile switching is attributed to the thickness/stiffness of chain-like conductive bridges between h-BN and Ag surfaces based on the current compliance and atomristor area. Computations indicate a "weak" bridge is responsible for volatile switching, while a "strong" bridge is formed for nonvolatile switching. The current compliance determines the number of Ag atoms that undergo dissociation at the electrode, while the atomristor area determines the degree of electric field localization that forms more stable conductive bridges. The findings of this study suggest that the h-BN atomristor using Ag electrodes shows promise as a potential solution to integrate both volatile neurons and nonvolatile synapses in a single neuromorphic crossbar array structure through electrical and dimensional designs.

Memristor-based neural networks: a bridge from device to artificial intelligence

Since the beginning of the 21st century, there is no doubt that the importance of artificial intelligence has been highlighted in many fields, among which the memristor-based artificial neural network technology is expected to break through the limitation of von Neumann so as to realize the replication of the human brain by enabling strong parallel computing ability and efficient data processing and become an important way towards the next generation of artificial intelligence. A new type of nanodevice, namely memristor, which is based on the variability of its resistance value, not only has very important applications in nonvolatile information storage, but also presents obsessive progressiveness in highly integrated circuits, making it one of the most promising circuit components in the post-Moore era. In particular, memristors can effectively simulate neural synapses and build neural networks; thus, they can be applied for the preparation of various artificial intelligence systems. This study reviews the research progress of memristors in artificial neural networks in detail and highlights the structural advantages and frontier applications of neural networks based on memristors. Finally, some urgent problems and challenges in current research are summarized and corresponding solutions and future development trends are put forward.