Artificial Synaptic Emulators Based on MoS2 Flash Memory Devices with Double Floating Gates

Photo-Assisted Ferroelectric Domain Control for α-In2Se3 Artificial Synapses Inspired by Spontaneous Internal Electric Fields

α-In2Se3 semiconductor crystals realize artificial synapses by tuning in-plane and out-of-plane ferroelectricity with diverse avenues of electrical and optical pulses. While the electrically induced ferroelectricity of α-In2Se3 shows synaptic memory operation, the optically assisted synaptic plasticity in α-In2Se3 has also been preferred for polarization flipping enhancement. Here, the synaptic memory behavior of α-In2Se3 is demonstrated by applying electrical gate voltages under white light. As a result, the induced internal electric field is identified at a polarization flipped conductance channel in α-In2Se3/hexagonal boron nitride (hBN) heterostructure ferroelectric field effect transistors (FeFETs) under white light and discuss the contribution of this built-in electric field on synapse characterization. The biased dipoles in α-In2Se3 toward potentiation polarization direction by an enhanced internal built-in electric field under illumination of white light lead to improvement of linearity for long-term depression curves with proper electric spikes. Consequently, upon applying appropriate electric spikes to α-In2Se3/hBN FeFETs with illuminating white light, the recognition accuracy values significantly through the artificial learning simulation is elevated for discriminating hand-written digit number images.

Polarized Tunneling Transistor for Ultralow-Energy-Consumption Artificial Synapse toward Neuromorphic Computing

Neural networks based on low-power artificial synapses can significantly reduce energy consumption, which is of great importance in today's era of artificial intelligence. Two-dimensional (2D) material-based floating-gate transistors (FGTs) have emerged as compelling candidates for simulating artificial synapses owing to their multilevel and nonvolatile data storage capabilities. However, the low erasing/programming speed of FGTs renders them unsuitable for low-energy-consumption artificial synapses, thereby limiting their potential in high-energy-efficient neuromorphic computing. Here, we introduce a FGT-inspired MoS2/Trap/PZT heterostructure-based polarized tunneling transistor (PTT) with a simple fabrication process and significantly enhanced erasing/programming speed. Distinct from the FGT, the PTT lacks a tunnel layer, leading to a marked improvement in its erasing/programming speed. The PTT's highest erasing/programming (operation) speed can reach ∼20 ns, which outperforms the performance of most FGTs based on 2D heterostructures. Furthermore, the PTT has been utilized as an artificial synapse, and its weight-update energy consumption can be as low as 0.0002 femtojoule (fJ), which benefits from the PTT's ultrahigh operation speed. Additionally, PTT-based artificial synapses have been employed in constructing artificial neural network simulations, achieving facial-recognition accuracy (95%). This groundbreaking work makes it possible for fabricating future high-energy-efficient neuromorphic transistors utilizing 2D materials.

Exploring the Cutting-Edge Frontiers of Electrochemical Random Access Memories (ECRAMs) for Neuromorphic Computing: Revolutionary Advances in Material-to-Device Engineering

Advanced materials and device engineering has played a crucial role in improving the performance of electrochemical random access memory (ECRAM) devices. ECRAM technology has been identified as a promising candidate for implementing artificial synapses in neuromorphic computing systems due to its ability to store analog values and its ease of programmability. ECRAM devices consist of an electrolyte and a channel material sandwiched between two electrodes, and the performance of these devices depends on the properties of the materials used. This review provides a comprehensive overview of material engineering strategies to optimize the electrolyte and channel materials' ionic conductivity, stability, and ionic diffusivity to improve the performance and reliability of ECRAM devices. Device engineering and scaling strategies are further discussed to enhance ECRAM performance. Last, perspectives on the current challenges and future directions in developing ECRAM-based artificial synapses in neuromorphic computing systems are provided.

Atomically Thin Synapse Networks on Van Der Waals Photo-Memtransistors

A new type of atomically thin synaptic network on van der Waals (vdW) heterostructures is reported, where each ultrasmall cell (≈2 nm thick) built with trilayer WS₂ semiconductor acts as a gate-tunable photoactive synapse, i.e., a photo-memtransistor. A train of UV pulses onto the WS₂ memristor generates dopants in atomic-level precision by direct light-lattice interactions, which, along with the gate tunability, leads to the accurate modulation of the channel conductance for potentiation and depression of the synaptic cells. Such synaptic dynamics can be explained by a parallel atomistic resistor network model. In addition, it is shown that such a device scheme can generally be realized in other 2D vdW semiconductors, such as MoS₂ , MoSe₂ , MoTe₂ , and WSe₂ . Demonstration of these atomically thin photo-memtransistor arrays, where the synaptic weights can be tuned for the atomistic defect density, provides implications for a new type of artificial neural networks for parallel matrix computations with an ultrahigh integration density.

Ultrafast Operation of 2D Heterostructured Nonvolatile Memory Devices Provided by the Strong Short-Time Dielectric Breakdown Strength of h-BN

Recently, the ultrafast operation (∼20 ns) of a two-dimensional (2D) heterostructured nonvolatile memory (NVM) device was demonstrated, attracting considerable attention. However, there is no consensus on its physical origin. In this study, various 2D NVM device structures are compared. First, we reveal that the hole injection at the metal/MoS₂ interface is the speed-limiting path in the NVM device with the access region. Therefore, MoS₂ NVM devices with a direct tunneling path between source/drain electrodes and the floating gate are fabricated by removing the access region. Indeed, a 50 ns program/erase operation is successfully achieved for devices with metal source/drain electrodes as well as graphite source/drain electrodes. This controlled experiment proves that an atomically sharp interface is not necessary for ultrafast operation, which is contrary to the previous literature. Finally, the dielectric breakdown strength (E_BD) of h-BN under short voltage pulses is examined. Since a high dielectric breakdown strength allows a large tunneling current, ultrafast operations can be achieved. Surprisingly, an E_BD = 26.1 MV/cm for h-BN is realized under short voltage pulses, largely exceeding the E_BD = ∼12 MV/cm from the direct current (DC) measurement. This suggests that the high E_BD of h-BN can be the physical origin of the ultrafast operation.

Mimicking Neuroplasticity via Ion Migration in van der Waals Layered Copper Indium Thiophosphate

Artificial synaptic devices are the essential components of neuromorphic computing systems, which are capable of parallel information storage and processing with high area and energy efficiencies, showing high promise in future storage systems and in-memory computing. Analogous to the diffusion of neurotransmitter between neurons, ion-migration-based synaptic devices are becoming promising for mimicking synaptic plasticity, though the precise control of ion migration is still challenging. Due to the unique 2D nature and highly anisotropic ionic transport properties, van der Waals layered materials are attractive for synaptic device applications. Here, utilizing the high conductivity from Cu⁺ -ion migration, a two-terminal artificial synaptic device based on layered copper indium thiophosphate is studied. By controlling the migration of Cu⁺ ions with an electric field, the device mimics various neuroplasticity functions, such as short-term plasticity, long-term plasticity, and spike-time-dependent plasticity. The Pavlovian conditioning and activity-dependent synaptic plasticity involved neural functions are also successfully emulated. These results show a promising opportunity to modulate ion migration in 2D materials through field-driven ionic processes, making the demonstrated synaptic device an intriguing candidate for future low-power neuromorphic applications.

2D Heterostructures for Ubiquitous Electronics and Optoelectronics: Principles, Opportunities, and Challenges

A grand family of two-dimensional (2D) materials and their heterostructures have been discovered through the extensive experimental and theoretical efforts of chemists, material scientists, physicists, and technologists. These pioneering works contribute to realizing the fundamental platforms to explore and analyze new physical/chemical properties and technological phenomena at the micro-nano-pico scales. Engineering 2D van der Waals (vdW) materials and their heterostructures via chemical and physical methods with a suitable choice of stacking order, thickness, and interlayer interactions enable exotic carrier dynamics, showing potential in high-frequency electronics, broadband optoelectronics, low-power neuromorphic computing, and ubiquitous electronics. This comprehensive review addresses recent advances in terms of representative 2D materials, the general fabrication methods, and characterization techniques and the vital role of the physical parameters affecting the quality of 2D heterostructures. The main emphasis is on 2D heterostructures and 3D-bulk (3D) hybrid systems exhibiting intrinsic quantum mechanical responses in the optical, valley, and topological states. Finally, we discuss the universality of 2D heterostructures with representative applications and trends for future electronics and optoelectronics (FEO) under the challenges and opportunities from physical, nanotechnological, and material synthesis perspectives.

Linear and Symmetric Li-Based Composite Memristors for Efficient Supervised Learning

Emerging energy-efficient neuromorphic circuits are based on hardware implementation of artificial neural networks (ANNs) that employ the biomimetic functions of memristors. Specifically, crossbar array memristive architectures are able to perform ANN vector-matrix multiplication more efficiently than conventional CMOS hardware. Memristors with specific characteristics, such as ohmic behavior in all resistance states in addition to symmetric and linear long-term potentiation/depression (LTP/LTD), are required in order to fully realize these benefits. Here, we demonstrate a Li-based composite memristor (LCM) that achieves these objectives. The LCM consists of three phases: Li-doped TiO₂ as a Li reservoir, Li₄Ti₅O₁₂ as the insulating phase, and Li₇Ti₅O₁₂ as the metallic phase, where resistive switching correlates with the change in the relative fraction of the metallic and insulating phases. The LCM exhibits a symmetric and gradual resistive switching behavior for both set and reset operations during a full bias sweep cycle. This symmetric and linear weight update is uniquely enabled by the symmetric bidirectional migration of Li ions, which leads to gradual changes in the relative fraction of the metallic phase in the film. The optimized LCM in ANN simulation showed that exceptionally high accuracy in image classification is realized in fewer training steps compared to the nonlinear behavior of conventional memristors.

Rectifying optoelectronic memory based on WSe2/graphene heterostructures

van der Waals heterostructures composed of two-dimensional materials vertically stacked have been extensively studied to develop various multifunctional devices. Here, we report WSe₂/graphene heterostructure devices with a top floating gate that can serve as multifunctional devices. They exhibit gate-controlled rectification inversion, rectified nonvolatile memory effects, and multilevel optoelectronic memory effects. Depending on the polarity of the gate voltage pulses (V _Gp), electrons or holes can be trapped in the floating gate, resulting in rectified nonvolatile memory properties. Furthermore, upon repeated illumination with laser pulses, positive or negative staircase photoconductivity is observed depending on the history of V _Gp, which is ascribed to the tunneling of electrons or holes between the WSe₂ channel and the floating gate. These multifunctional devices can be used to emulate excitatory and inhibitory synapses that have different neurotransmitters. Various synaptic functions, such as potentiation/depression curves and spike-timing-dependent plasticity, have been also implemented using these devices. In particular, 128 optoelectronic memory states with nonlinearity less than 1 can be achieved by controlling applied laser pulses and V _Gp, suggesting that the WSe₂/graphene heterostructure devices with a top floating gate can be applied to optoelectronic synapse devices.

Artificial Neuron and Synapse Devices Based on 2D Materials

Neuromorphic systems, which emulate neural functionalities of a human brain, are considered to be an attractive next-generation computing approach, with advantages of high energy efficiency and fast computing speed. After these neuromorphic systems are proposed, it is demonstrated that artificial synapses and neurons can mimic neural functions of biological synapses and neurons. However, since the neuromorphic functionalities are highly related to the surface properties of materials, bulk material-based neuromorphic devices suffer from uncontrollable defects at surfaces and strong scattering caused by dangling bonds. Therefore, 2D materials which have dangling-bond-free surfaces and excellent crystallinity have emerged as promising candidates for neuromorphic computing hardware. First, the fundamental synaptic behavior is reviewed, such as synaptic plasticity and learning rule, and requirements of artificial synapses to emulate biological synapses. In addition, an overview of recent advances on 2D materials-based synaptic devices is summarized by categorizing these into various working principles of artificial synapses. Second, the compulsory behavior and requirements of artificial neurons such as the all-or-nothing law and refractory periods to simulate a spike neural network are described, and the implementation of 2D materials-based artificial neurons to date is reviewed. Finally, future challenges and outlooks of 2D materials-based neuromorphic devices are discussed.

Material and Device Structure Designs for 2D Memory Devices Based on the Floating Gate Voltage Trajectory

Two-dimensional heterostructures have been extensively investigated as next-generation nonvolatile memory (NVM) devices. In the past decade, drastic performance improvements and further advanced functionalities have been demonstrated. However, this progress is not sufficiently supported by the understanding of their operations, obscuring the material and device structure design policy. Here, detailed operation mechanisms are elucidated by exploiting the floating gate (FG) voltage measurements. Systematic comparisons of MoTe₂, WSe₂, and MoS₂ channel devices revealed that the tunneling behavior between the channel and FG is controlled by three kinds of current-limiting paths, i.e., tunneling barrier, 2D/metal contact, and p-n junction in the channel. Furthermore, the control experiment indicated that the access region in the device structure is required to achieve 2D channel/FG tunneling by preventing electrode/FG tunneling. The present understanding suggests that the ambipolar 2D-based FG-type NVM device with the access region is suitable for further realizing potentially high electrical reliability.

Flexible Artificial Sensory Systems Based on Neuromorphic Devices

Emerging flexible artificial sensory systems using neuromorphic electronics have been considered as a promising solution for processing massive data with low power consumption. The construction of artificial sensory systems with synaptic devices and sensing elements to mimic complicated sensing and processing in biological systems is a prerequisite for the realization. To realize high-efficiency neuromorphic sensory systems, the development of artificial flexible synapses with low power consumption and high-density integration is essential. Furthermore, the realization of efficient coupling between the sensing element and the synaptic device is crucial. This Review presents recent progress in the area of neuromorphic electronics for flexible artificial sensory systems. We focus on both the recent advances of artificial synapses, including device structures, mechanisms, and functions, and the design of intelligent, flexible perception systems based on synaptic devices. Additionally, key challenges and opportunities related to flexible artificial perception systems are examined, and potential solutions and suggestions are provided.

Controlling Native Oxidation of HfS2 for 2D Materials Based Flash Memory and Artificial Synapse

Two-dimensional (2D) materials based artificial synapses are important building blocks for the brain-inspired computing systems that are promising in handling large amounts of informational data with high energy-efficiency in the future. However, 2D devices usually rely on deposited or transferred insulators as the dielectric layer, resulting in various challenges in device compatibility and fabrication complexity. Here, we demonstrate a controllable and reliable oxidation process to turn 2D semiconductor HfS₂ into native oxide, HfO_x, which shows good insulating property and clean interface with HfS₂. We then incorporate the HfO_x/HfS₂ heterostructure into a flash memory device, achieving a high on/off current ratio of ∼10⁵, a large memory window over 60 V, good endurance, and a long retention time over 10³ seconds. In particular, the memory device can work as an artificial synapse to emulate basic synaptic functions and feature good linearity and symmetry in conductance change during long-term potentiation/depression processes. A simulated artificial neural network based on our synaptic device achieves a high accuracy of ∼88% in MNIST pattern recognition. Our work provides a simple and effective approach for integrating high-k dielectrics into 2D material-based memory and synaptic devices.

The capabilities of nanoelectronic 2-D materials for bio-inspired computing and drug delivery indicate their significance in modern drug design

Remarkable advancements in the computational techniques and nanoelectronics have attracted considerable interests for development of highly-sophisticated materials (Ms) including the theranostics with optimal characteristics and innovative delivery systems. Analyzing the huge amounts of multivariate data and solving the newly-emerged complicated problems including the healthcare-related ones have created increasing demands for improving the computational speed and minimizing the consumption of energy. Shifting towards the non-von Neumann approaches enables performing specific computational tasks and optimizing the processing of signals. Besides usefulness for neuromorphic computing and increasing the efficiency of computation energy, 2-D electronic Ms are capable of optical sensing with ultra-fast and ultra-sensitive responses, mimicking the neurons, detection of pathogens or biomolecules, and prediction of the progression of diseases, assessment of the pharmacokinetics/pharmacodynamics of therapeutic candidates, mimicking the dynamics of the release of neurotransmitters or fluxes of ions that might provide a deeper knowledge about the computations and information flow in the brain, and development of more effective treatment protocols with improved outcomes. 2-D Ms appear as the major components of the next-generation electronically-enabled devices for highly-advanced computations, bio-imaging, diagnostics, tissue engineering, and designing smart systems for site-specific delivery of therapeutics that might result in the reduced adverse effects of drugs and improved patient compliance. This manuscript highlights the significance of 2-D Ms in the neuromorphic computing, optimizing the energy efficiency of the multi-step computations, providing novel architectures or multi-functional systems, improved performance of a variety of devices and bio-inspired functionalities, and delivery of theranostics.

A 2D material-based floating gate device with linear synaptic weight update

Neuromorphic computing is of great interest among researchers interested in overcoming the von Neumann computing bottleneck. A synaptic device, one of the key components to realize a neuromorphic system, has a weight that indicates the strength of the connection between two neurons, and updating this weight must have linear and symmetric characteristics. Especially, a transistor-type device has a gate terminal, separating the processes of reading and updating the conductivity, used as a synaptic weight to prevent sneak path current issues during synaptic operations. In this study, we fabricate a top-gated flash memory device based on two-dimensional (2D) materials, MoS2 and graphene, as a channel and a floating gate, respectively, and Al2O3 and HfO2 to increase the tunneling efficiency. We demonstrate the linear weight updates and repeatable characteristics of applying negative/positive pulses, and also emulate spike timing-dependent plasticity (STDP), one of the learning rules in a spiking neural network (SNN).

Emerging Materials for Neuromorphic Devices and Systems

Neuromorphic devices and systems have attracted attention as next-generation computing due to their high efficiency in processing complex data. So far, they have been demonstrated using both machine-learning software and complementary metal-oxide-semiconductor-based hardware. However, these approaches have drawbacks in power consumption and learning speed. An energy-efficient neuromorphic computing system requires hardware that can mimic the functions of a brain. Therefore, various materials have been introduced for the development of neuromorphic devices. Here, recent advances in neuromorphic devices are reviewed. First, the functions of biological synapses and neurons are discussed. Also, deep neural networks and spiking neural networks are described. Then, the operation mechanism and the neuromorphic functions of emerging devices are reviewed. Finally, the challenges and prospects for developing neuromorphic devices that use emerging materials are discussed.

Understanding the Memory Window Overestimation of 2D Materials Based Floating Gate Type Memory Devices by Measuring Floating Gate Voltage

The memory window of floating gate (FG) type non-volatile memory (NVM) devices is a fundamental figure of merit used not only to evaluate the performance, such as retention and endurance, but also to discuss the feasibility of advanced functional memory devices. However, the memory window of 2D materials based NVM devices is historically determined from round sweep transfer curves, while that of conventional Si NVM devices is determined from high and low threshold voltages (V_th s), which are measured by single sweep transfer curves. Here, it is elucidated that the memory window of 2D NVM devices determined from round sweep transfer curves is overestimated compared with that determined from single sweep transfer curves. The floating gate voltage measurement proposed in this study clarifies that the V_th s in round sweep are controlled not only by the number of charges stored in floating gate but also by capacitive coupling between floating gate and back gate. The present finding on the overestimation of memory window enables to appropriately evaluate the potential of 2D NVM devices.

Engineering Complex Synaptic Behaviors in a Single Device: Emulating Consolidation of Short-term Memory to Long-term Memory in Artificial Synapses via Dielectric Band Engineering

As one of the key neuronal activities associated with memory in the human brain, memory consolidation is the process of the transition of short-term memory (STM) to long-term memory (LTM), which transforms an external stimulus to permanently stored information. Here, we report the emulation of this complex synaptic function, consolidation of STM to LTM, in a single-crystal indium phosphide (InP) field effect transistor (FET)-based artificial synapse. This behavior is achieved via the dielectric band and charge trap lifetime engineering in a dielectric gate heterostructure of aluminum oxide and titanium oxide. We analyze the behavior of these complex synaptic functions by engineering a variety of action potential parameters, and the devices exhibit good endurance, long retention time (>10⁵ s), and high uniformity. Uniquely, this approach utilizes growth and device fabrication techniques which are scalable and back-end CMOS compatible, making this InP synaptic device a potential building block for neuromorphic computing.

Two-Dimensional Near-Atom-Thickness Materials for Emerging Neuromorphic Devices and Applications

Two-dimensional (2D) layered materials and their heterostructures have recently been recognized as promising building blocks for futuristic brain-like neuromorphic computing devices. They exhibit unique properties such as near-atomic thickness, dangling-bond-free surfaces, high mechanical robustness, and electrical/optical tunability. Such attributes unattainable with traditional electronic materials are particularly promising for high-performance artificial neurons and synapses, enabling energy-efficient operation, high integration density, and excellent scalability. In this review, diverse 2D materials explored for neuromorphic applications, including graphene, transition metal dichalcogenides, hexagonal boron nitride, and black phosphorous, are comprehensively overviewed. Their promise for neuromorphic applications are fully discussed in terms of material property suitability and device operation principles. Furthermore, up-to-date demonstrations of neuromorphic devices based on 2D materials or their heterostructures are presented. Lastly, the challenges associated with the successful implementation of 2D materials into large-scale devices and their material quality control will be outlined along with the future prospect of these emergent materials.

Stimuli-Enabled Artificial Synapses for Neuromorphic Perception: Progress and Perspectives

Brain-inspired neuromorphic computing is intended to provide effective emulation of the functionality of the human brain via the integration of electronic components. Recent studies of synaptic plasticity, which represents one of the most significant neurochemical bases of learning and memory, have enhanced the general comprehension of how the brain functions and have thereby eased the development of artificial neuromorphic devices. An understanding of the synaptic plasticity induced by various types of stimuli is essential for neuromorphic system construction. The realization of multiple stimuli-enabled synapses will be important for future neuromorphic computing applications. In this Review, state-of-the-art synaptic devices with particular emphasis on their synaptic behaviors under excitation by a variety of external stimuli are summarized, including electric fields, light, magnetic fields, pressure, and temperature. The switching mechanisms of these synaptic devices are discussed in detail, including ion migration, electron/hole transfer, phase transition, redox-based resistive switching, and other mechanisms. This Review aims to provide a comprehensive understanding of the operating mechanisms of artificial synapses and thus provides the principles required for design of multifunctional neuromorphic systems with parallel processing capabilities.

Neuromorphic nanoelectronic materials

Memristive and nanoionic devices have recently emerged as leading candidates for neuromorphic computing architectures. While top-down fabrication based on conventional bulk materials has enabled many early neuromorphic devices and circuits, bottom-up approaches based on low-dimensional nanomaterials have shown novel device functionality that often better mimics a biological neuron. In addition, the chemical, structural and compositional tunability of low-dimensional nanomaterials coupled with the permutational flexibility enabled by van der Waals heterostructures offers significant opportunities for artificial neural networks. In this Review, we present a critical survey of emerging neuromorphic devices and architectures enabled by quantum dots, metal nanoparticles, polymers, nanotubes, nanowires, two-dimensional layered materials and van der Waals heterojunctions with a particular emphasis on bio-inspired device responses that are uniquely enabled by low-dimensional topology, quantum confinement and interfaces. We also provide a forward-looking perspective on the opportunities and challenges of neuromorphic nanoelectronic materials in comparison with more mature technologies based on traditional bulk electronic materials.

Electret-Based Organic Synaptic Transistor for Neuromorphic Computing

Neuromorphic computing inspired by the neural systems in human brain will overcome the issue of independent information processing and storage. An artificial synaptic device as a basic unit of a neuromorphic computing system can perform signal processing with low power consumption, and exploring different types of synaptic transistors is essential to provide suitable artificial synaptic devices for artificial intelligence. Hence, for the first time, an electret-based synaptic transistor (EST) is presented, which successfully shows synaptic behaviors including excitatory/inhibitory postsynaptic current, paired-pulse facilitation/depression, long-term memory, and high-pass filtering. Moreover, a neuromorphic computing simulation based on our EST is performed using the handwritten artificial neural network, which exhibits an excellent recognition accuracy (85.88%) after 120 learning epochs, higher than most reported organic synaptic transistors and close to the ideal accuracy (92.11%). Such a novel synaptic device enriches the diversity of synaptic transistors, laying the foundation for the diversified development of the next generation of neuromorphic computing systems.

Electric and Light Dual-Gate Tunable MoS2 Memtransistor

Memtransistor is a multiterminal device combining the concepts of memristor and field-effect transistor with two-dimensional (2D) materials. The gate tunability of resistive switching in 2D memtransistor enables the multifunctional modulation and promising applications in neuromorphic network. However, the tunability of switching ratio in 2D memtransistor remains small and seriously limits its practical application. Here, we investigate a memtransistor based on a 3-layer MoS₂ and realize the electric, light, and their combined modulations. In the electric gate mode, switching ratio is tunable in a large scale in the range 10⁰-10⁵. In the light gate mode, a maximum conductance change of 450% can be obtained by increasing the light power. Moreover, the switching ratio can be further improved to ∼10⁶ through a combination of electric and light dual gating. Such a gating effect can be ascribed to the modulation of carrier density in the MoS₂ channel. Our work provides a simple approach for achieving a high-performance multifunctional memtransistor.

Infrared Detectable MoS2 Phototransistor and Its Application to Artificial Multilevel Optic-Neural Synapse

Layered two-dimensional (2D) materials have entered the spotlight as promising channel materials for future optoelectronic devices owing to their excellent electrical and optoelectronic properties. However, their limited photodetection range caused by their wide bandgap remains a principal challenge in 2D layered materials-based phototransistors. Here, we developed a germanium (Ge)-gated MoS₂ phototransistor that can detect light in the region from visible to infrared (λ = 520-1550 nm) using a detection mechanism based on band bending modulation. In addition, the Ge-gated MoS₂ phototransistor is proposed as a multilevel optic-neural synaptic device, which performs both optical-sensing and synaptic functions on one device and is operated in different current ranges according to the light conditions: dark, visible, and infrared. This study is expected to contribute to the development of 2D material-based phototransistors and synaptic devices in next-generation optoelectronics.

Multilevel MoS2 Optical Memory with Photoresponsive Top Floating Gates

Optoelectronic memory devices, whose states can be controlled using electrical optical signals, are receiving much attention for their potential applications in image sensing and parallel data transmission and processes. Here, we report MoS₂-based devices with top floating gates of Au, graphene, and MoS₂. Unlike conventional floating gate memory devices, our devices have the photoresponsive floating gate at the top, acting as a charge trapping layer. Stable and reliable switching with an on/off ratio of ∼10⁶ and a retention time of >10⁴ s is achieved by illumination with 405 nm light pulses as well as application of gate voltage pulses. However, upon illumination with 532 or 635 nm light pulses, multilevel optical memory effects are observed, which are dependent on the wavelength and the optical exposure dosage. In addition, compared to the device employing a graphene floating gate, the device with an MoS₂ floating gate is more sensitive to light, suggesting that the multilevel optical memory properties originate from photoexcited carriers in the top floating gate and can be modulated by adjusting the top floating gate materials. The structure of the top floating gate may open up a new way to novel optoelectronic memory devices.

Reduction of Threshold Voltage Hysteresis of MoS2 Transistors with 3-Aminopropyltriethoxysilane Passivation and Its Application for Improved Synaptic Behavior

Although molybdenum disulfide (MoS₂) is highlighted as a promising channel material, MoS₂-based field-effect transistors (FETs) have a large threshold voltage hysteresis (Δ V_TH) from interface traps at their gate interfaces. In this work, the Δ V_TH of MoS₂ FETs is significantly reduced by inserting a 3-aminopropyltriethoxysilane (APTES) passivation layer at the MoS₂/SiO₂ gate interface owing to passivation of the interface traps. The Δ V_TH is reduced from 23 to 10.8 V by inserting the 1%-APTES passivation layers because APTES passivation prevents trapping and detrapping of electrons, which are the major source of the Δ V_TH. The reduction in the density of interface traps ( D_it) is confirmed by the improvement of the subthreshold swing (SS) after inserting the APTES layer. Furthermore, the improvement in the synaptic characteristics of the MoS₂ FET through the APTES passivation is investigated. Both inhibitory and excitatory postsynaptic currents (PSC) are increased by 33% owing to the reduction in the Δ V_TH and the n-type doping effect of the APTES layer; moreover, the linearity of PSC characteristics is significantly improved because the reduction in Δ V_TH enables the synaptic operation to be over the threshold region, which is linear. The application of the APTES gate passivation technique to MoS₂ FETs is promising for reliable and accurate synaptic applications in neuromorphic computing technology as well as for the next-generation complementary logic applications.

A Thought-Operated Digital Random-Access Memory

The capacity and reliability of biological memory could be exceeded by a constantly growing flux of information to remember and operate by. Yet, our memory is fragile and could be easily impaired, and the prevalence of memory disorders is increasing in correlation with the population's mean age. As expected, auxiliary memory devices (such as writing pads and computers) are abundant but are operated indirectly using significant effort compared with biological memory. We report a working prototype of a simplified, 4 KB random-access memory (RAM) that can be written to or read from using thought and could be embedded more seamlessly than other artificial memory aids. The system analyses EEG signals to extract attention levels, which trained subjects can use to write messages into an RFID sticker, or read from it on a display. We describe basic modes of using memory by a single subject, emulate common forms of social communication using this system, and highlight new forms of social usage and allocation of memories that are linked to specific persons. This preliminary prototype highlights the technical feasibility and the possibilities of implantable thought-operated memory devices and could be developed further to provide seamless aid to people suffering from memory disorders in the near future.