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

Two-Terminal Perovskite Optoelectronic Synapse for Rapid Trained Neuromorphic Computation with High Accuracy

Emerging neural morphological vision sensors inspired by biological systems that integrate image perception, memory, and information computing are expected to transform the landscape of machine vision and artificial intelligence. However, stable and reconfigurable light-induced synaptic behavior always relies on independent gateport modulation. Despite its potential, the limitations of uncontrollable defects and ionic characteristics have led to simpler, smaller, and more integration-friendly two-terminal devices being used as sidelines. In this work, the synergy between ion migration barriers and readout voltage is proven to be the key to realizing stable, reconfigurable, and precisely controllable postsynaptic current in two-terminal devices. Following the same mechanism, optical and electrical signal synchronous triggering is proposed to serve as a preprocessing method to achieve a recognition accuracy of 96.5%. Impressively, the gradual ion accumulation during the training process induces photocurrent evolution, serving as a reference for the dynamic learning rate and boosting accuracy to 97.8% in just 10 epochs. The PSC modulation potential under short optical pulse of 20 ns is also revealed. This optoelectronic device with perception, memory, and computation capabilities can promote the development of new devices for future photonic neural morphological circuits and artificial vision.

Two-dimensional material-based memristive devices for alternative computing

Two-dimensional (2D) materials have emerged as promising building blocks for next generation memristive devices, owing to their unique electronic, mechanical, and thermal properties, resulting in effective switching mechanisms for charge transport. Memristors are key components in a wide range of applications including neuromorphic computing, which is becoming increasingly important in artificial intelligence applications. Crossbar arrays are an important component in the development of hardware-based neural networks composed of 2D materials. In this paper, we summarize the current state of research on 2D material-based memristive devices utilizing different switching mechanisms, along with the application of these devices in neuromorphic crossbar arrays. Additionally, we discuss the challenges and future directions for the field.

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.

Nonvolatile Modulation of Bi2O2Se/Pb(Zr,Ti)O3 Heteroepitaxy

The pursuit of high-performance electronic devices has driven the research focus toward 2D semiconductors with high electron mobility and suitable band gaps. Previous studies have demonstrated that quasi-2D Bi2O2Se (BOSe) has remarkable physical properties and is a promising candidate for further exploration. Building upon this foundation, the present work introduces a novel concept for achieving nonvolatile and reversible control of BOSe's electronic properties. The approach involves the epitaxial integration of a ferroelectric PbZr0.2Ti0.8O3 (PZT) layer to modify BOSe's band alignment. Within the BOSe/PZT heteroepitaxy, through two opposite ferroelectric polarization states of the PZT layer, we can tune the Fermi level in the BOSe layer. Consequently, this controlled modulation of the electronic structure provides a pathway to manipulate the electrical properties of the BOSe layer and the corresponding devices.

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.

MoOx Synaptic Memristor with Programmable Multilevel Conductance for Reliable Neuromorphic Hardware

Memristor holds great potential for enabling next-generation neuromorphic computing hardware. Controlling the interfacial characteristics of the device is critical for seamlessly integrating and replicating the synaptic dynamic behaviors; however, it is commonly overlooked. Herein, we report the straightforward oxidation of a Mo electrode in air to design MoOx memristors that exhibit nonvolatile ultrafast switching (0.6-0.8 mV/decade, <1 mV/decade) with a high on/off ratio (>104), a long durability (>104 s), a low power consumption (17.9 μW), excellent device-to-device uniformity, ingeniously synaptic behavior, and finely programmable multilevel analog switching. The analyzed physical mechanism of the observed resistive switching behavior might be the conductive filaments formed by the oxygen vacancies. Intriguingly, upon organization into memristor-based crossbar arrays, in addition to simulated multipattern memorization, edge detection on random images can be implemented well by parallel processing of pixels using a 3 × 3 × 2 array of Prewitt filter groups. These are vital functions for neural system hardware in efficient in-memory computing neural systems with massive parallelism beyond a von Neumann architecture.

Optimizing Thermoelectric Performance of Hybrid Crystals Bi2O2Se1-xTex in the Bi2O2X System

In addressing the global need for sustainable energy conversion, this study presents a breakthrough in thermoelectric materials research by optimizing the Bi2O2Se1-xTex system in the Bi2O2Se/Bi2O2Te pseudobinary series. Leveraging the principles of innovative transport mechanisms and defect engineering, we introduce tellurium (Te) doping into Bi2O2Se to enhance its thermoelectric properties synergistically. With the help of various advanced characterization tools such as XRD, SEM, TEM, XPS, FTIR, TGA, LFA, and DSC, combined with relevant resistance and density measurement techniques, we conducted an in-depth exploration of the complex interactions between various factors within thermoelectric materials. We recognize that the balance and synergy of these factors in the thermoelectric conversion process are crucial to achieving efficient energy conversion. Through systematic research, we are committed to revealing the mechanisms of these interactions and providing a solid scientific foundation for the optimal design and performance enhancement of thermoelectric materials. Finally, the advantage coefficient (ZT) of the thermoelectric material has been significantly improved. The crystallographic analysis confirms the formation of a continuous series of mixed crystals with varying Te concentrations, adhering to Vegard's law and exhibiting significant improvements in electrical and thermal conductivities. The Bi2O2Se1-xTex crystals, particularly the Bi2O2Se0.6Te0.4 composition, demonstrate a peak ZT of 0.86 at 373 K. This achievement aligns with recent advancements in defect-enabled mechanisms and band convergence and sets a new standard for high-performance thermoelectrics. The study's findings contribute significantly to the ongoing quest for efficient thermal-to-electrical energy conversion, offering a promising avenue for future sustainable energy technologies.

Enhanced Memristive Performance via a Vertically Heterointerface in Nanocomposite Thin Films for Artificial Synapses

Memristors can be used to mimic synaptic behavior in artificial neural networks, which makes them a key component in neuromorphic computing and holds promise for advancing the field. In this study, a memory artificial synaptic device based on ZnO-BaTiO3 (ZnO-BTO) vertically aligned nanocomposite thin films was prepared. The vertical interface between the two phases can be used as a conduit for oxygen vacancy (OV) accumulation and a channel for OV movement, which greatly optimizes the resistive switching performance of the device and has the potential for multistage storage. By applying different pulse sequences to the device, the conductance of the device is adjusted from multiple angles, and a variety of synaptic functions are simulated, such as paired-pulse facilitation, spike-timing-dependent plasticity, short-term plasticity to long-term plasticity (STP-LTP), and long-term potentiation/depression (LTP/LTD). Finally, we construct a neural network for image recognition, and the recognition accuracy can reach 91%. Our study demonstrates the feasibility of using composite thin-film vertical interface to regulate the resistive performance of memristors and its great potential in artificial synaptic simulation and neuromorphic computing.

Recent Advances in Tactile Sensory Systems: Mechanisms, Fabrication, and Applications

Flexible electronics is a cutting-edge field that has paved the way for artificial tactile systems that mimic biological functions of sensing mechanical stimuli. These systems have an immense potential to enhance human-machine interactions (HMIs). However, tactile sensing still faces formidable challenges in delivering precise and nuanced feedback, such as achieving a high sensitivity to emulate human touch, coping with environmental variability, and devising algorithms that can effectively interpret tactile data for meaningful interactions in diverse contexts. In this review, we summarize the recent advances of tactile sensory systems, such as piezoresistive, capacitive, piezoelectric, and triboelectric tactile sensors. We also review the state-of-the-art fabrication techniques for artificial tactile sensors. Next, we focus on the potential applications of HMIs, such as intelligent robotics, wearable devices, prosthetics, and medical healthcare. Finally, we conclude with the challenges and future development trends of tactile sensors.

Silver Nanowire Networks with Moisture-Enhanced Learning Ability

The human brain possesses a remarkable ability to memorize information with the assistance of a specific external environment. Therefore, mimicking the human brain's environment-enhanced learning abilities in artificial electronic devices is essential but remains a considerable challenge. Here, a network of Ag nanowires with a moisture-enhanced learning ability, which can mimic long-term potentiation (LTP) synaptic plasticity at an ultralow operating voltage as low as 0.01 V, is presented. To realize a moisture-enhanced learning ability and to adjust the aggregations of Ag ions, we introduced a thin polyvinylpyrrolidone (PVP) coating layer with moisture-sensitive properties to the surfaces of the Ag nanowires of Ag ions. That Ag nanowire network was shown to exhibit, in response to the humidity of its operating environment, different learning speeds during the LTP process. In high-humidity environments, the synaptic plasticity was significantly strengthened with a higher learning speed compared with that in relatively low-humidity environments. Based on experimental and simulation results, we attribute this enhancement to the higher electric mobility of the Ag ions in the water-absorbed PVP layer. Finally, we demonstrated by simulation that the moisture-enhanced synaptic plasticity enabled the device to adjust connection weights and delivery modes based on various input patterns. The recognition rate of a handwritten data set reached 94.5% with fewer epochs in a high-humidity environment. This work shows the feasibility of building our electronic device to achieve artificial adaptive learning abilities.

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

Flexible/stretchable electronics, which are characterized by their ultrathin design, lightweight structure, and excellent mechanical robustness and conformability, have garnered significant attention due to their unprecedented potential in healthcare, advanced robotics, and human-machine interface technologies. An increasing number of low-dimensional nanostructures with exceptional mechanical, electronic, and/or optical properties are being developed for flexible/stretchable electronics to fulfill the functional and application requirements of information sensing, processing, and interactive loops. Compared to the traditional single-layer format, which has a restricted design space, a monolithic three-dimensional (M3D) integrated device architecture offers greater flexibility and stretchability for electronic devices, achieving a high-level of integration to accommodate the state-of-the-art design targets, such as skin-comfort, miniaturization, and multi-functionality. Low-dimensional nanostructures possess small size, unique characteristics, flexible/elastic adaptability, and effective vertical stacking capability, boosting the advancement of M3D-integrated flexible/stretchable systems. In this review, we provide a summary of the typical low-dimensional nanostructures found in semiconductor, interconnect, and substrate materials, and discuss the design rules of flexible/stretchable devices for intelligent sensing and data processing. Furthermore, artificial sensory systems in 3D integration have been reviewed, highlighting the advancements in flexible/stretchable electronics that are deployed with high-density, energy-efficiency, and multi-functionalities. Finally, we discuss the technical challenges and advanced methodologies involved in the design and optimization of low-dimensional nanostructures, to achieve monolithic 3D-integrated flexible/stretchable multi-sensory systems.