Mimicking Neurotransmitter Release in Chemical Synapses via Hysteresis Engineering in MoS2 Transistors

Neural mechanism of non-adaptive cognitive emotion regulation in patients with non-suicidal self-injury

BACKGROUND

The incidence of non-suicidal self-injury (NSSI) has been on the rise in recent years. Studies have shown that people with NSSI have difficulties in emotion regulation and cognitive control. In addition, some studies have investigated the cognitive emotion regulation of people with NSSI which found that they have difficulties in cognitive emotion regulation, but there was a lack of research on cognitive emotion regulation strategies and related neural mechanisms.

METHODS

This study included 117 people with NSSI (age = 19.47 ± 5.13, male = 17) and 84 non-NSSI participants (age = 19.86 ± 4.14, male = 16). People with NSSI met the DSM-5 diagnostic criteria, and non-NSSI participants had no mental or physical disorders. The study collected all participants' data of Cognitive Emotion Regulation Questionnaire (CERQ) and functional magnetic resonance imaging (fMRI) to explore the differences in psychological performance and brain between two groups. Afterwards, Machine learning was used to select the found differential brain regions to obtain the highest correlation regions with NSSI. Then, Allen's Human Brain Atlas database was used to compare with the information on the abnormal brain regions of people with NSSI to find the genetic information related to NSSI. In addition, gene enrichment analysis was carried out to find the related pathways and specific cells that may have differences.

RESULTS

The differences between NSSI participants and non-NSSI participants were as follows: positive refocusing (t = -4.74, p < 0.01); refocusing on plans (t = -4.11, p < 0.01); positive reappraisal (t = -9.22, p < 0.01); self-blame (t = 6.30, p < 0.01); rumination (t = 3.64, p < 0.01); catastrophizing (t = 9.10, p < 0.01), and blaming others (t = 2.52, p < 0.01), the precentral gyrus (t = 6.04, pFDR < 0.05) and the rolandic operculum (t = -4.57, pFDR < 0.05). Rolandic operculum activity was negatively correlated with blaming others (r = -0.20, p < 0.05). Epigenetic results showed that excitatory neurons (p < 0.01) and inhibitory neurons (p < 0.01) were significant differences in two pathways, "trans-synaptic signaling" (p < -log108) and "modulation of chemical synaptic transmission" (p < -log108) in both cells.

CONCLUSIONS

People with NSSI are more inclined to adopt non-adaptive cognitive emotion regulation strategies. Rolandic operculum is also abnormally active. Abnormal changes in the rolandic operculum of them are associated with non-adaptive cognitive emotion regulation strategies. Changes in the excitatory and inhibitory neurons provide hints to explore the abnormalities of the neurological mechanisms at the cellular level of them. Trial registration number NCT04094623.

Paraffin-Enabled Superlattice Customization for a Photostimulated Gradient-Responsive Artificial Reflex Arc

The development of photostimulated-motion artificial reflex arcs - a neural circuit inspired by light-driven motion reflexes - holds significant promises for advancements in robotic perception, navigation, and motion control. However, the fabrication of such systems, especially those that accommodate multiple actions and exhibit gradient responses, remains challenging. Here, a gradient-responsive photostimulated-motion artificial reflex arc is developed by integrating a programmable and tunable photoreceptor based on folded MoS2 at different twist angles. The twisted folded bilayer MoS2 used as photoreceptors can be customized via the transfer technique using patternable paraffin, where the twist angle and fold-line could be controlled. The photoluminescence (PL) intensity is 3.7 times higher at a twist angle of 29° compared to that at 0°, showing a monotonically decreasing indirect bandgap. Through tunable interlayer carrier transport, photoreceptors fabricated using folded bilayer MoS2 at different twist angles demonstrate gradient response time, enabling the photostimulated-motion artificial reflex arc for multiaction responses. They are transformed to digital command flow and studied via machine learning to control the gestures of a robotic hand, showing a prototype of photostimulated gradient-responsive artificial reflex arcs for the first time. This work provides a unique idea for developing intelligent soft robots and next-generation human-computer interfaces.

Schottky barrier reduction on optoelectronic responses in heavy ion irradiated WSe2 memtransistors

Two-dimensional transition metal dichalcogenide-based memtransistors provide simulation, sensing, and storage capabilities for applications in a remotely operated aerospace environment. Swift heavy ion (SHI) irradiation technology is a common method to simulate the influences of radiation ions on electronic devices in space environments. Here, SHI irradiation technology under different conditions was utilized to produce complex defects in WSe2-based memtransistors. Low-resistance state to low-resistance state (LRS-LRS) switching behaviors under light illumination were achieved and photocurrent responses with different spike trains were observed in SHI-irradiated memtransistors, which facilitated the design of devices with enriched analog functions. Reduction of the Schottky barrier height due to the introduced defects at the metal/WSe2 interface was confirmed to be the major factor responsible for the observed behaviors. 1T phase and concentric circle-type vacancies were also created in the SHI-irradiated 2H-WSe2 channel besides the amorphous structure; these complex defects could seriously affect the transport properties of the devices. We believe that this work serves as a foundation for aerospace radiation applications of all-in-one devices. It also opens a new application field of heavy ion irradiation technology for the development of multiterminal memtransistor-based optoelectronic artificial synapses for neuromorphic computing.

Vertically Integrated Monolithic Neuromorphic Nanowire Device for Physiological Information Processing

This study demonstrates the conceptual design and fabrication of a vertically integrated monolithic (VIM) neuromorphic device. The device comprises an n-type SnO2 nanowire bottom channel connected by a shared gate to a p-type P3HT nanowire top channel. This architecture establishes two distinct neural pathways with different response behaviors. The device generates excitatory and inhibitory postsynaptic currents, mimicking the corelease mechanism of bilingual synapses. To enhance the signal processing efficiency, we employed a bipolar spike encoding strategy to convert fluctuating sensory signals to spike trains containing positive and negative pulses. Utilizing the neuromorphic platform for synaptic processing, physiological signals featuring bidirectional fluctuations, including electrocardiogram and breathing signals, can be classified with an accuracy of over 90%. The VIM device holds considerable promise as a solution for developing highly integrated neuromorphic hardware for healthcare and edge intelligence applications.

Surfactant-Free Ultrasonication-Assisted Synthesis of 2d Tellurium Based on Metastable 1T'-MoTe2

Elemental 2D materials (E2DMs) have been attracting considerable attention owing to their chemical simplicity and excellent/exotic properties. However, the lack of robust chemical synthetic methods seriously limits their potential. Here, a surfactant-free liquid-phase synthesis of high-quality 2D tellurium is reported based on ultrasonication-assisted exfoliation of metastable 1T'-MoTe2. The as-grown 2D tellurium nanosheets exhibit excellent single crystallinity, ideal 2D morphology, surfactant-free surface, and negligible 1D by-products. Furthermore, a unique growth mechanism based on the atomic escape of Te atoms from metastable transition metal dichalcogenides and guided 2D growth in the liquid phase is proposed and verified. 2D tellurium-based field-effect transistors show ultrahigh hole mobility exceeding 1000 cm2 V-1 s-1 at room temperature attributing to the high crystallinity and surfactant-free surface, and exceptional chemical and operational stability using both solid-state dielectric and liquid-state electrical double layer. The facile ultrasonication-assisted synthesis of high-quality 2D tellurium paves the way for further exploration of E2DMs and expands the scope of liquid-phase exfoliation (LPE) methodology toward the controlled wet-chemical synthesis of functional nanomaterials.

Hydrogel-Gated FETs in Neuromorphic Computing to Mimic Biological Signal: A Review

Hydrogel-gated synaptic transistors offer unique advantages, including biocompatibility, tunable electrical properties, being biodegradable, and having an ability to mimic biological synaptic plasticity. For processing massive data with ultralow power consumption due to high parallelism and human brain-like processing abilities, synaptic transistors have been widely considered for replacing von Neumann architecture-based traditional computers due to the parting of memory and control units. The crucial components mimic the complex biological signal, synaptic, and sensing systems. Hydrogel, as a gate dielectric, is the key factor for ionotropic devices owing to the excellent stability, ultra-high linearity, and extremely low operating voltage of the biodegradable and biocompatible polymers. Moreover, hydrogel exhibits ionotronic functions through a hybrid circuit of mobile ions and mobile electrons that can easily interface between machines and humans. To determine the high-efficiency neuromorphic chips, the development of synaptic devices based on organic field effect transistors (OFETs) with ultra-low power dissipation and very large-scale integration, including bio-friendly devices, is needed. This review highlights the latest advancements in neuromorphic computing by exploring synaptic transistor developments. Here, we focus on hydrogel-based ionic-gated three-terminal (3T) synaptic devices, their essential components, and their working principle, and summarize the essential neurodegenerative applications published recently. In addition, because hydrogel-gated FETs are the crucial members of neuromorphic devices in terms of cutting-edge synaptic progress and performances, the review will also summarize the biodegradable and biocompatible polymers with which such devices can be implemented. It is expected that neuromorphic devices might provide potential solutions for the future generation of interactive sensation, memory, and computation to facilitate the development of multimodal, large-scale, ultralow-power intelligent systems.

Bipolar Resistive Switching in 2D MoSe2 Grown by Atmospheric Pressure Chemical Vapor Deposition

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) are highly promising nanomaterials for various electronic devices such as field-effect transistors, junction diodes, tunneling devices, and, more recently, memristors. 2D MoSe2 stands out for having high electrical conductivity, charge carrier mobility, and melting point. While these features make it particularly appropriate as a switching layer in memristive devices, reliable and scalable production of large-area 2D MoSe2 still represents a challenge. In this study, we manufacture 2D MoSe2 films by atmospheric-pressure chemical vapor deposition and investigate them on the atomic scale. We selected and transferred MoSe2 bilayer to serve as a switching layer between asymmetric Au-Cu electrodes in miniaturized crossbar vertical memristors. The electrochemical metallization devices showed forming-free, bipolar resistive switching at low voltages, with clearly identifiable nonvolatile states. Other than low-power neuromorphic computing, low switching voltages approaching the range of biological action potentials could unlock hybrid biological interfaces.

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.

A Butterfly-Inspired Multisensory Neuromorphic Platform for Integration of Visual and Chemical Cues

Unisensory cues are often insufficient for animals to effectively engage in foraging, mating, and predatory activities. In contrast, integration of cues collected from multiple sensory organs enhances the overall perceptual experience and thereby facilitates better decision-making. Despite the importance of multisensory integration in animals, the field of artificial intelligence (AI) and neuromorphic computing has primarily focused on processing unisensory information. This lack of emphasis on multisensory integration can be attributed to the absence of a miniaturized hardware platform capable of co-locating multiple sensing modalities and enabling in-sensor and near-sensor processing. In this study, this limitation is addressed by utilizing the chemo-sensing properties of graphene and the photo-sensing capability of monolayer molybdenum disulfide (MoS2 ) to create a multisensory platform for visuochemical integration. Additionally, the in-memory-compute capability of MoS2 memtransistors is leveraged to develop neural circuits that facilitate multisensory decision-making. The visuochemical integration platform is inspired by intricate courtship of Heliconius butterflies, where female species rely on the integration of visual cues (such as wing color) and chemical cues (such as pheromones) generated by the male butterflies for mate selection. The butterfly-inspired visuochemical integration platform has significant implications in both robotics and the advancement of neuromorphic computing, going beyond unisensory intelligence and information processing.

Biodegradable albumen dielectrics for high-mobility MoS2 phototransistors

This work demonstrates the fabrication and characterization of single-layer MoS2 field-effect transistors using biodegradable albumen (chicken eggwhite) as gate dielectric. By introducing albumen as an insulator for MoS2 transistors high carrier mobilities (up to ~90 cm2 V-1 s-1) are observed, which is remarkably superior to that obtained with commonly used SiO2 dielectric which we attribute to ionic gating due to the formation of an electric double layer in the albumen MoS2 interface. In addition, the investigated devices are characterized upon illumination, observing responsivities of 4.5 AW-1 (operated in photogating regime) and rise times as low as 52 ms (operated in photoconductivity regime). The presented study reveals the combination of albumen with van der Waals materials for prospective biodegradable and biocompatible optoelectronic device applications. Furthermore, the demonstrated universal fabrication process can be easily adopted to fabricate albumen-based devices with any other van der Waals material.

Toward High-Performance p-Type Two-Dimensional Field Effect Transistors: Contact Engineering, Scaling, and Doping

n-type field effect transistors (FETs) based on two-dimensional (2D) transition-metal dichalcogenides (TMDs) such as MoS2 and WS2 have come close to meeting the requirements set forth in the International Roadmap for Devices and Systems (IRDS). However, p-type 2D FETs are dramatically lagging behind in meeting performance standards. Here, we adopt a three-pronged approach that includes contact engineering, channel length (Lch) scaling, and monolayer doping to achieve high performance p-type FETs based on synthetic WSe2. Using electrical measurements backed by atomistic imaging and rigorous analysis, Pd was identified as the favorable contact metal for WSe2 owing to better epitaxy, larger grain size, and higher compressive strain, leading to a lower Schottky barrier height. While the ON-state performance of Pd-contacted WSe2 FETs was improved by ∼10× by aggressively scaling Lch from 1 μm down to ∼20 nm, ultrascaled FETs were found to be contact limited. To reduce the contact resistance, monolayer tungsten oxyselenide (WOxSey) obtained using self-limiting oxidation of bilayer WSe2 was used as a p-type dopant. This led to ∼5× improvement in the ON-state performance and ∼9× reduction in the contact resistance. We were able to achieve a median ON-state current as high as ∼10 μA/μm for ultrascaled and doped p-type WSe2 FETs with Pd contacts. We also show the applicability of our monolayer doping strategy to other 2D materials such as MoS2, MoTe2, and MoSe2.

An all 2D bio-inspired gustatory circuit for mimicking physiology and psychology of feeding behavior

Animal behavior involves complex interactions between physiology and psychology. However, most AI systems neglect psychological factors in decision-making due to a limited understanding of the physiological-psychological connection at the neuronal level. Recent advancements in brain imaging and genetics have uncovered specific neural circuits that regulate behaviors like feeding. By developing neuro-mimetic circuits that incorporate both physiology and psychology, a new emotional-AI paradigm can be established that bridges the gap between humans and machines. This study presents a bio-inspired gustatory circuit that mimics adaptive feeding behavior in humans, considering both physiological states (hunger) and psychological states (appetite). Graphene-based chemitransistors serve as artificial gustatory taste receptors, forming an electronic tongue, while 1L-MoS2 memtransistors construct an electronic-gustatory-cortex comprising a hunger neuron, appetite neuron, and feeding circuit. This work proposes a novel paradigm for emotional neuromorphic systems with broad implications for human health. The concept of gustatory emotional intelligence can extend to other sensory systems, benefiting future humanoid AI.

A bio-inspired visuotactile neuron for multisensory integration

Multisensory integration is a salient feature of the brain which enables better and faster responses in comparison to unisensory integration, especially when the unisensory cues are weak. Specialized neurons that receive convergent input from two or more sensory modalities are responsible for such multisensory integration. Solid-state devices that can emulate the response of these multisensory neurons can advance neuromorphic computing and bridge the gap between artificial and natural intelligence. Here, we introduce an artificial visuotactile neuron based on the integration of a photosensitive monolayer MoS2 memtransistor and a triboelectric tactile sensor which minutely captures the three essential features of multisensory integration, namely, super-additive response, inverse effectiveness effect, and temporal congruency. We have also realized a circuit which can encode visuotactile information into digital spiking events, with probability of spiking determined by the strength of the visual and tactile cues. We believe that our comprehensive demonstration of bio-inspired and multisensory visuotactile neuron and spike encoding circuitry will advance the field of neuromorphic computing, which has thus far primarily focused on unisensory intelligence and information processing.

Reservoir Computing with Charge-Trap Memory Based on a MoS2 Channel for Neuromorphic Engineering

Novel memory devices are essential for developing low power, fast, and accurate in-memory computing and neuromorphic engineering concepts that can compete with the conventional complementary metal-oxide-semiconductor (CMOS) digital processors. 2D semiconductors provide a novel platform for advanced semiconductors with atomic thickness, low-current operation, and capability of 3D integration. This work presents a charge-trap memory (CTM) device with a MoS2 channel where memory operation arises, thanks to electron trapping/detrapping at interface states. Transistor operation, memory characteristics, and synaptic potentiation/depression for neuromorphic applications are demonstrated. The CTM device shows outstanding linearity of the potentiation by applied drain pulses of equal amplitude. Finally, pattern recognition is demonstrated by reservoir computing where the input pattern is applied as a stimulation of the MoS2 -based CTMs, while the output current after stimulation is processed by a feedforward readout network. The good accuracy, the low current operation, and the robustness to input random bit flip makes the CTM device a promising technology for future high-density neuromorphic computing concepts.

Single-Gate In-Transistor Readout of Current Superposition and Collapse Utilizing Quantum Tunneling and Ferroelectric Switching

In nanostructure assemblies, the superposition of current paths forms microscopic electric circuits, and different circuit networks produce varying results, particularly when utilized as transistor channels for computing applications. However, the intricate nature of assembly networks and the winding paths of commensurate currents hinder standard circuit modeling. Inspired by the quantum collapse of superposition states for information decoding in quantum circuits, the implementation of analogous current path collapse to facilitate the detection of microscopic circuits by modifying their network topology is explored. Here, the superposition and collapse of current paths in gate-all-around polysilicon nanosheet arrays are demonstrated to enrich the computational resources within transistors by engineering the channel length and quantity. Switching the ferroelectric polarization of Hf0.5 Zr0.5 O2 gate dielectric, which drives these transistors out-of-equilibrium, decodes the output polymorphism through circuit topological modifications. Furthermore, a protocol for the single-electron readout of ferroelectric polarization is presented with tailoring the channel coherence. The introduction of lateral path superposition results into intriguing metal-to-insulator transitions due to transient behavior of ferroelectric switching. This ability to adjust the current networks within transistors and their interaction with ferroelectric polarization in polycrystalline nanostructures lays the groundwork for generating diverse current characteristics as potential physical databases for optimization-based computing.

Observation of Rich Defect Dynamics in Monolayer MoS2

Defects play a pivotal role in limiting the performance and reliability of nanoscale devices. Field-effect transistors (FETs) based on atomically thin two-dimensional (2D) semiconductors such as monolayer MoS₂ are no exception. Probing defect dynamics in 2D FETs is therefore of significant interest. Here, we present a comprehensive insight into various defect dynamics observed in monolayer MoS₂ FETs at varying gate biases and temperatures. The measured source-to-drain currents exhibit random telegraph signals (RTS) owing to the transfer of charges between the semiconducting channel and individual defects. Based on the modeled temperature and gate bias dependence, oxygen vacancies or aluminum interstitials are probable defect candidates. Several types of RTSs are observed including anomalous RTS and giant RTS indicating local current crowding effects and rich defect dynamics in monolayer MoS₂ FETs. This study explores defect dynamics in large area-grown monolayer MoS₂ with ALD-grown Al₂O₃ as the gate dielectric.

Hardware and Information Security Primitives Based on 2D Materials and Devices

Hardware security is a major concern for the entire semiconductor ecosystem that accounts for billions of dollars in annual losses. Similarly, information security is a critical need for the rapidly proliferating edge devices that continuously collect and communicate a massive volume of data. While silicon-based complementary metal-oxide-semiconductor technology offers security solutions, these are largely inadequate, inefficient, and often inconclusive, as well as resource intensive in time, energy, and cost, leading to tremendous room for innovation in this field. Furthermore, silicon-based security primitives have shown vulnerability to machine learning (ML) attacks. In recent years, 2D materials such as graphene and transition metal dichalcogenides have been intensely explored to mitigate these security challenges. In this review, 2D-materials-based hardware security solutions such as camouflaging, true random number generation, watermarking, anticounterfeiting, physically unclonable functions, and logic locking of integrated circuits (ICs) are summarized with accompanying discussion on their reliability and resilience to ML attacks. In addition, the role of native defects in 2D materials in developing high entropy hardware security primitives is also examined. Finally, the existing challenges for 2D materials, which must be overcome for large-scale deployment of 2D ICs to meet the security needs of the semiconductor industry, are discussed.

Layer-Structured Anisotropic Metal Chalcogenides: Recent Advances in Synthesis, Modulation, and Applications

The unique electronic and catalytic properties emerging from low symmetry anisotropic (1D and 2D) metal chalcogenides (MCs) have generated tremendous interest for use in next generation electronics, optoelectronics, electrochemical energy storage devices, and chemical sensing devices. Despite many proof-of-concept demonstrations so far, the full potential of anisotropic chalcogenides has yet to be investigated. This article provides a comprehensive overview of the recent progress made in the synthesis, mechanistic understanding, property modulation strategies, and applications of the anisotropic chalcogenides. It begins with an introduction to the basic crystal structures, and then the unique physical and chemical properties of 1D and 2D MCs. Controlled synthetic routes for anisotropic MC crystals are summarized with example advances in the solution-phase synthesis, vapor-phase synthesis, and exfoliation. Several important approaches to modulate dimensions, phases, compositions, defects, and heterostructures of anisotropic MCs are discussed. Recent significant advances in applications are highlighted for electronics, optoelectronic devices, catalysts, batteries, supercapacitors, sensing platforms, and thermoelectric devices. The article ends with prospects for future opportunities and challenges to be addressed in the academic research and practical engineering of anisotropic MCs.

A photonic artificial synapse with a reversible multifaceted photochromic compound

Modern computational technology based on the von Neumann architecture physically partitions memory and the central processing unit, resulting in fundamental speed limitations and high energy consumption. On the other hand, the human brain is an extraordinary multifunctional organ composed of more than a billion neurons capable of simultaneously thinking, processing, and storing information. Neurons are interconnected with synapses that control information flow from pre-synaptic-to-post-synaptic neurons. Therefore, emulating synaptic functionalities and developing neuromorphic computational architecture has recently attracted much interest. Due to their high-speed, large bandwidth, and no interconnect-related power loss, photonic (all-optical) synapses can overcome the existing hurdles with electronic synapses. Here, we show an artificial photonic synapse by utilizing the well-established reversible, high-contrast photochromic organic compound, spiropyran, stimulated by optical pulses. Optical transmission of spiropyran significantly changes during spiropyran-merocyanine isomerization driven by UV-visible optical pulses. Such changes are equivalent to the biological synapses' inhibitory and excitatory synaptic actions. The slow relaxation to the initial state is considered as synaptic plasticity responsible for learning and memory formation. Short-term memory (STM), long-term memory (LTM), and transition from the STM to the LTM are demonstrated in all-optical synapses by modulating the stimuli's strength. The solvatochromic properties of spiropyran are further utilized to augment memory in synapses. Our work shows that photochromic organic compounds are excellent hosts for artificial photonic synapses and can be implemented in neuromorphic applications.

Fully Optical in Operando Investigation of Ambient Condition Electrical Switching in MoS2 Nanodevices

MoS₂ nanoswitches have shown superb ultralow switching energies without excessive leakage currents. However, the debate about the origin and volatility of electrical switching is unresolved due to the lack of adequate nanoimaging of devices in operando. Here, three optical techniques are combined to perform the first noninvasive in situ characterization of nanosized MoS₂ devices. This study reveals volatile threshold resistive switching due to the intercalation of metallic atoms from electrodes directly between Mo and S atoms, without the assistance of sulfur vacancies. A "semi-memristive" effect driven by an organic adlayer adjacent to MoS₂ is observed, which suggests that nonvolatility can be achieved by careful interface engineering. These findings provide a crucial understanding of nanoprocess in vertically biased MoS₂ nanosheets, which opens new routes to conscious engineering and optimization of 2D electronics.

Recent progress in three-terminal artificial synapses based on 2D materials: from mechanisms to applications

Synapses are essential for the transmission of neural signals. Synaptic plasticity allows for changes in synaptic strength, enabling the brain to learn from experience. With the rapid development of neuromorphic electronics, tremendous efforts have been devoted to designing and fabricating electronic devices that can mimic synapse operating modes. This growing interest in the field will provide unprecedented opportunities for new hardware architectures for artificial intelligence. In this review, we focus on research of three-terminal artificial synapses based on two-dimensional (2D) materials regulated by electrical, optical and mechanical stimulation. In addition, we systematically summarize artificial synapse applications in various sensory systems, including bioplastic bionics, logical transformation, associative learning, image recognition, and multimodal pattern recognition. Finally, the current challenges and future perspectives involving integration, power consumption and functionality are outlined.

Two-Dimensional Transition Metal Dichalcogenide Based Biosensors: From Fundamentals to Healthcare Applications

There has been an exponential surge in reports on two-dimensional (2D) materials ever since the discovery of graphene in 2004. Transition metal dichalcogenides (TMDs) are a class of 2D materials where weak van der Waals force binds individual covalently bonded X-M-X layers (where M is the transition metal and X is the chalcogen), making layer-controlled synthesis possible. These individual building blocks (single-layer TMDs) transition from indirect to direct band gaps and have fascinating optical and electronic properties. Layer-dependent opto-electrical properties, along with the existence of finite band gaps, make single-layer TMDs superior to the well-known graphene that paves the way for their applications in many areas. Ultra-fast response, high on/off ratio, planar structure, low operational voltage, wafer scale synthesis capabilities, high surface-to-volume ratio, and compatibility with standard fabrication processes makes TMDs ideal candidates to replace conventional semiconductors, such as silicon, etc., in the new-age electrical, electronic, and opto-electronic devices. Besides, TMDs can be potentially utilized in single molecular sensing for early detection of different biomarkers, gas sensors, photodetector, and catalytic applications. The impact of COVID-19 has given rise to an upsurge in demand for biosensors with real-time detection capabilities. TMDs as active or supporting biosensing elements exhibit potential for real-time detection of single biomarkers and, hence, show promise in the development of point-of-care healthcare devices. In this review, we provide a historical survey of 2D TMD-based biosensors for the detection of bio analytes ranging from bacteria, viruses, and whole cells to molecular biomarkers via optical, electronic, and electrochemical sensing mechanisms. Current approaches and the latest developments in the study of healthcare devices using 2D TMDs are discussed. Additionally, this review presents an overview of the challenges in the area and discusses the future perspective of 2D TMDs in the field of biosensing for healthcare devices.

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.

Bioinspired and Low-Power 2D Machine Vision with Adaptive Machine Learning and Forgetting

Natural intelligence has many dimensions, with some of its most important manifestations being tied to learning about the environment and making behavioral changes. In primates, vision plays a critical role in learning. The underlying biological neural networks contain specialized neurons and synapses which not only sense and process visual stimuli but also learn and adapt with remarkable energy efficiency. Forgetting also plays an active role in learning. Mimicking the adaptive neurobiological mechanisms for seeing, learning, and forgetting can, therefore, accelerate the development of artificial intelligence (AI) and bridge the massive energy gap that exists between AI and biological intelligence. Here, we demonstrate a bioinspired machine vision system based on a 2D phototransistor array fabricated from large-area monolayer molybdenum disulfide (MoS₂) and integrated with an analog, nonvolatile, and programmable memory gate-stack; this architecture not only enables dynamic learning and relearning from visual stimuli but also offers learning adaptability under noisy illumination conditions at miniscule energy expenditure. In short, our demonstrated "all-in-one" hardware vision platform combines "sensing", "computing", and "storage" to not only overcome the von Neumann bottleneck of conventional complementary metal-oxide-semiconductor (CMOS) technology but also to eliminate the need for peripheral circuits and sensors.

An All-in-One Bioinspired Neural Network

In spite of recent advancements in artificial neural networks (ANNs), the energy efficiency, multifunctionality, adaptability, and integrated nature of biological neural networks remain largely unimitated by hardware neuromorphic computing systems. Here, we exploit optoelectronic, computing, and programmable memory devices based on emerging two-dimensional (2D) layered materials such as MoS₂ to demonstrate a monolithically integrated, multipixel, and "all-in-one" bioinspired neural network (BNN) capable of sensing, encoding, learning, forgetting, and inferring at minuscule energy expenditure. We also demonstrate learning adaptability and simulate learning challenges under specific synaptic conditions to mimic biological learning. Our findings highlight the potential of in-memory computing and sensing based on emerging 2D materials, devices, and integrated circuits to not only overcome the bottleneck of von Neumann computing in conventional CMOS designs but also to aid in eliminating the peripheral components necessary for competing technologies such as memristors.

Graphene and Beyond: Recent Advances in Two-Dimensional Materials Synthesis, Properties, and Devices

Since the isolation of graphene in 2004, two-dimensional (2D) materials research has rapidly evolved into an entire subdiscipline in the physical sciences with a wide range of emergent applications. The unique 2D structure offers an open canvas to tailor and functionalize 2D materials through layer number, defects, morphology, moiré pattern, strain, and other control knobs. Through this review, we aim to highlight the most recent discoveries in the following topics: theory-guided synthesis for enhanced control of 2D morphologies, quality, yield, as well as insights toward novel 2D materials; defect engineering to control and understand the role of various defects, including in situ and ex situ methods; and properties and applications that are related to moiré engineering, strain engineering, and artificial intelligence. Finally, we also provide our perspective on the challenges and opportunities in this fascinating field.

The Road for 2D Semiconductors in the Silicon Age

Continued reduction in transistor size can improve the performance of silicon integrated circuits (ICs). However, as Moore's law approaches physical limits, high-performance growth in silicon ICs becomes unsustainable, due to challenges of scaling, energy efficiency, and memory limitations. The ultrathin layers, diverse band structures, unique electronic properties, and silicon-compatible processes of 2D materials create the potential to consistently drive advanced performance in ICs. Here, the potential of fusing 2D materials with silicon ICs to minimize the challenges in silicon ICs, and to create technologies beyond the von Neumann architecture, is presented, and the killer applications for 2D materials in logic and memory devices to ease scaling, energy efficiency bottlenecks, and memory dilemmas encountered in silicon ICs are discussed. The fusion of 2D materials allows the creation of all-in-one perception, memory, and computation technologies beyond the von Neumann architecture to enhance system efficiency and remove computing power bottlenecks. Progress on the 2D ICs demonstration is summarized, as well as the technical hurdles it faces in terms of wafer-scale heterostructure growth, transfer, and compatible integration with silicon ICs. Finally, the promising pathways and obstacles to the technological advances in ICs due to the integration of 2D materials with silicon are presented.

Active pixel sensor matrix based on monolayer MoS2 phototransistor array

In-sensor processing, which can reduce the energy and hardware burden for many machine vision applications, is currently lacking in state-of-the-art active pixel sensor (APS) technology. Photosensitive and semiconducting two-dimensional (2D) materials can bridge this technology gap by integrating image capture (sense) and image processing (compute) capabilities in a single device. Here, we introduce a 2D APS technology based on a monolayer MoS₂ phototransistor array, where each pixel uses a single programmable phototransistor, leading to a substantial reduction in footprint (900 pixels in ∼0.09 cm²) and energy consumption (100s of fJ per pixel). By exploiting gate-tunable persistent photoconductivity, we achieve a responsivity of ∼3.6 × 10⁷ A W^-1, specific detectivity of ∼5.6 × 10¹³ Jones, spectral uniformity, a high dynamic range of ∼80 dB and in-sensor de-noising capabilities. Further, we demonstrate near-ideal yield and uniformity in photoresponse across the 2D APS array.

Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks

Artificial neural networks have demonstrated superiority over traditional computing architectures in tasks such as pattern classification and learning. However, they do not measure uncertainty in predictions, and hence they can make wrong predictions with high confidence, which can be detrimental for many mission-critical applications. In contrast, Bayesian neural networks (BNNs) naturally include such uncertainty in their model, as the weights are represented by probability distributions (e.g. Gaussian distribution). Here we introduce three-terminal memtransistors based on two-dimensional (2D) materials, which can emulate both probabilistic synapses as well as reconfigurable neurons. The cycle-to-cycle variation in the programming of the 2D memtransistor is exploited to achieve Gaussian random number generator-based synapses, whereas 2D memtransistor based integrated circuits are used to obtain neurons with hyperbolic tangent and sigmoid activation functions. Finally, memtransistor-based synapses and neurons are combined in a crossbar array architecture to realize a BNN accelerator for a data classification task.

Large Memory Window of van der Waals Heterostructure Devices Based on MOCVD-Grown 2D Layered Ge4 Se9

Van der Waals (vdW) heterostructures have drawn much interest over the last decade owing to their absence of dangling bonds and their intriguing low-dimensional properties. The emergence of 2D materials has enabled the achievement of significant progress in both the discovery of physical phenomena and the realization of superior devices. In this work, the group IV metal chalcogenide 2D-layered Ge₄ Se₉ is introduced as a new selection of insulating vdW material. 2D-layered Ge₄ Se₉ is synthesized with a rectangular shape using the metalcorganic chemical vapor deposition system using a liquid germanium precursor at 240 °C. By stacking the Ge₄ Se₉ and MoS₂ , vdW heterostructure devices are fabricated with a giant memory window of 129 V by sweeping back gate range of ±80 V. The gate-independent decay time reveals that the large hysteresis is induced by the interfacial charge transfer, which originates from the low band offset. Moreover, repeatable conductance changes are observed over the 2250 pulses with low non-linearity values of 0.26 and 0.95 for potentiation and depression curves, respectively. The energy consumption of the MoS₂ /Ge₄ Se₉ device is about 15 fJ for operating energy and the learning accuracy of image classification reaches 88.3%, which further proves the great potential of artificial synapses.

Digital Keying Enabled by Reconfigurable 2D Modulators

Energy, area, and bandwidth efficient communication primitives are essential to sustain the rapid increase in connectivity among internet-of-things (IoT) edge devices. While IoT edge-sensing, edge-computing, and edge-storage have witnessed innovation in materials and devices, IoT edge communication is yet to experience such transformation. The aging silicon (Si)-based complementary metal-oxide-semiconductor (CMOS) technology continues to remain the mainstay of communication devices where they are used to implement amplitude, frequency, and phase shift keying (amplitude-shift keying [ASK]/frequency-shift keying [FSK]/phase-shift keying [PSK]). Keying allows digital information to be communicated over a radio channel. While CMOS-based keying devices have evolved over the years, their hardware footprint and energy consumption are major concerns for resource constrained IoT communication. Furthermore, separate circuit designs and hardware elements are needed for each keying scheme and achieving multibit modulation to improve bandwidth efficiency remains a challenge. Here, a reconfigurable modulator is introduced that exploits unique ambipolar transport and programmable Dirac voltage in ultrathin MoTe₂ field-effect transistors to achieve ASK, FSK, and PSK modulation. Furthermore, by integrating two programmed MoTe₂ field-effect transistors, multibit data modulation is demonstrated, which improves the bandwidth efficiency by 200%. Finally, a frequency quadrupler is also realized exploiting the unique "double-well" transfer characteristic.

Hardware implementation of Bayesian network based on two-dimensional memtransistors

Bayesian networks (BNs) find widespread application in many real-world probabilistic problems including diagnostics, forecasting, computer vision, etc. The basic computing primitive for BNs is a stochastic bit (s-bit) generator that can control the probability of obtaining '1' in a binary bit-stream. While silicon-based complementary metal-oxide-semiconductor (CMOS) technology can be used for hardware implementation of BNs, the lack of inherent stochasticity makes it area and energy inefficient. On the other hand, memristors and spintronic devices offer inherent stochasticity but lack computing ability beyond simple vector matrix multiplication due to their two-terminal nature and rely on extensive CMOS peripherals for BN implementation, which limits area and energy efficiency. Here, we circumvent these challenges by introducing a hardware platform based on 2D memtransistors. First, we experimentally demonstrate a low-power and compact s-bit generator circuit that exploits cycle-to-cycle fluctuation in the post-programmed conductance state of 2D memtransistors. Next, the s-bit generators are monolithically integrated with 2D memtransistor-based logic gates to implement BNs. Our findings highlight the potential for 2D memtransistor-based integrated circuits for non-von Neumann computing applications.

Emulating the Signal Transmission in a Neural System Using Polymer Membranes

Neurons are vital components of the brain. When stimulated by neurotransmitters at the dendrites, neurons deliver signals as changes in the membrane potential by ion movement. The signal transmission of a nervous system exhibits a high energy efficiency. These characteristics of neurons are being exploited to develop efficient neuromorphic computing systems. In this study, we develop chemical synapses for neuromorphic devices and emulate the signaling processes in a nervous system using a polymer membrane, in which the ionic permeability can be controlled. The polymer membrane comprises poly(diallyl-dimethylammonium chloride) and poly(3-sulfopropyl acrylate potassium salt), which have positive and negative charges, respectively. The ionic permeability of the polymer membrane is controlled by the injection of a neurotransmitter solution. This device emulates the signal transmission behavior of biological neurons depending on the concentration of the injected neurotransmitter solution. The proposed artificial neuronal signaling device can facilitate the development of bio-realistic neuromorphic devices.

All-in-one, bio-inspired, and low-power crypto engines for near-sensor security based on two-dimensional memtransistors

In the emerging era of the internet of things (IoT), ubiquitous sensors continuously collect, consume, store, and communicate a huge volume of information which is becoming increasingly vulnerable to theft and misuse. Modern software cryptosystems require extensive computational infrastructure for implementing ciphering algorithms, making them difficult to be adopted by IoT edge sensors that operate with limited hardware resources and at low energy budgets. Here we propose and experimentally demonstrate an “all-in-one” 8 × 8 array of robust, low-power, and bio-inspired crypto engines monolithically integrated with IoT edge sensors based on two-dimensional (2D) memtransistors. Each engine comprises five 2D memtransistors to accomplish sensing and encoding functionalities. The ciphered information is shown to be secure from an eavesdropper with finite resources and access to deep neural networks. Our hardware platform consists of a total of 320 fully integrated monolayer MoS₂-based memtransistors and consumes energy in the range of hundreds of picojoules and offers near-sensor security.

Internet of things (IoT) sensors can collect, store and communicate large volumes of information, which require effective security measures. Here, the authors report the realization of low-power edge sensors based on photosensitive and programmable 2D memtransistors, integrating sensing, storage and encryption functionalities.

Novel charm of 2D materials engineering in memristor: when electronics encounter layered morphology

The family of two-dimensional (2D) materials composed of atomically thin layers connected via van der Waals interactions has attracted much curiosity due to a variety of intriguing physical, optical, and electrical characteristics. The significance of analyzing statistics on electrical devices and circuits based on 2D materials is seldom underestimated. Certain requirements must be met to deliver scientific knowledge that is beneficial in the field of 2D electronics: synthesis and fabrication must occur at the wafer level, variations in morphology and lattice alterations must be visible and statistically verified, and device dimensions must be appropriate. The authors discussed the most recent significant concerns of 2D materials in the provided prose and attempted to highlight the prerequisites for synthesis, yield, and mechanism behind device-to-device variability, reliability, and durability benchmarking under memristors characteristics; they also indexed some useful approaches that have already been reported to be advantageous in large-scale production. Commercial applications, on the other hand, will necessitate further effort.

Covalent Coupling of Porphyrins with Monolayer Graphene for Low-Voltage Synaptic Transistors

Synaptic devices emulating biological synapses are a key building component of artificial neural networks. Porphyrins and graphene, as two kinds of emerging electronic materials, have attracted extensive attention in the research of photoelectric devices due to their excellent structural and functional properties. Herein, we present a photonic synaptic transistor based on porphyrin-graphene covalent hybrids utilizing 5,10,15,20-tetrakis (4-aminophenyl)-21H,23H-porphine and monolayer graphene linked through the diazo addition reaction. The photonic synaptic device successfully simulates several essential biological functions, and the synaptic plasticity can be regulated by adjusting the parameters of light spikes and gate voltages of the device. Moreover, learning and memory behaviors under different wavelengths are studied to imitate the learning efficiency of humans in diverse emotional states. It is worth noting that all the synaptic functions can be realized at a low operating voltage of -10 mV, which is much lower than that required by most reported photonic synaptic devices. These results indicate that covalent coupling products of porphyrins with graphene have broad prospects in the construction of synaptic transistors and may arouse new research advances in neuromorphic devices with ultralow operating voltage and low energy consumption.

Memristive Devices Based on Two-Dimensional Transition Metal Chalcogenides for Neuromorphic Computing

Two-dimensional (2D) transition metal chalcogenides (TMC) and their heterostructures are appealing as building blocks in a wide range of electronic and optoelectronic devices, particularly futuristic memristive and synaptic devices for brain-inspired neuromorphic computing systems. The distinct properties such as high durability, electrical and optical tunability, clean surface, flexibility, and LEGO-staking capability enable simple fabrication with high integration density, energy-efficient operation, and high scalability. This review provides a thorough examination of high-performance memristors based on 2D TMCs for neuromorphic computing applications, including the promise of 2D TMC materials and heterostructures, as well as the state-of-the-art demonstration of memristive devices. The challenges and future prospects for the development of these emerging materials and devices are also discussed. The purpose of this review is to provide an outlook on the fabrication and characterization of neuromorphic memristors based on 2D TMCs.

Secure Electronics Enabled by Atomically Thin and Photosensitive Two-Dimensional Memtransistors

The rapid proliferation of security compromised hardware in today's integrated circuit (IC) supply chain poses a global threat to the reliability of communication, computing, and control systems. While there have been significant advancements in detection and avoidance of security breaches, current top-down approaches are mostly inadequate, inefficient, often inconclusive, and resource extensive in time, energy, and cost, offering tremendous scope for innovation in this field. Here, we introduce an energy and area efficient non-von Neumann hardware platform providing comprehensive and bottom-up security solutions by exploiting inherent device-to-device variation, electrical programmability, and persistent photoconductivity demonstrated by atomically thin two-dimensional memtransistors. We realize diverse security primitives including physically unclonable function, anticounterfeit measures, intellectual property (IP) watermarking, and IC camouflaging to prevent false authentication, detect recycled and remarked ICs, protect IP theft, and stop reverse engineering of ICs.

Demonstration of Stochastic Resonance, Population Coding, and Population Voting Using Artificial MoS2 Based Synapses

Fast detection of weak signals at low energy expenditure is a challenging but inescapable task for the evolutionary success of animals that survive in resource constrained environments. This task is accomplished by the sensory nervous system by exploiting the synergy between three astounding neural phenomena, namely, stochastic resonance (SR), population coding (PC), and population voting (PV). In SR, the constructive role of synaptic noise is exploited for the detection of otherwise invisible signals. In PC, the redundancy in neural population is exploited to reduce the detection latency. Finally, PV ensures unambiguous signal detection even in the presence of excessive noise. Here we adopt a similar strategies and experimentally demonstrate how a population of stochastic artificial neurons based on monolayer MoS₂ field effect transistors (FETs) can use an optimum amount of white Gaussian noise and population voting to detect invisible signals at a frugal energy expenditure (∼10s of nano-Joules). Our findings can aid remote sensing in the emerging era of the Internet of things (IoT) that thrive on energy efficiency.

Reconfigurable 2D WSe2 -Based Memtransistor for Mimicking Homosynaptic and Heterosynaptic Plasticity

The mimicking of both homosynaptic and heterosynaptic plasticity using a high-performance synaptic device is important for developing human-brain-like neuromorphic computing systems to overcome the ever-increasing challenges caused by the conventional von Neumann architecture. However, the commonly used synaptic devices (e.g., memristors and transistors) require an extra modulate terminal to mimic heterosynaptic plasticity, and their capability of synaptic plasticity simulation is limited by the low weight adjustability. In this study, a WSe₂ -based memtransistor for mimicking both homosynaptic and heterosynaptic plasticity is fabricated. By applying spikes on either the drain or gate terminal, the memtransistor can mimic common homosynaptic plasticity, including spiking rate dependent plasticity, paired pulse facilitation/depression, synaptic potentiation/depression, and filtering. Benefitting from the multi-terminal input and high adjustability, the resistance state number and linearity of the memtransistor can be improved by optimizing the conditions of the two inputs. Moreover, the device can successfully mimic heterosynaptic plasticity without introducing an extra terminal and can simultaneously offer versatile reconfigurability of excitatory and inhibitory plasticity. These highly adjustable and reconfigurable characteristics offer memtransistors more freedom of choice for tuning synaptic weight, optimizing circuit design, and building artificial neuromorphic computing systems.

Nanomaterials and their applications on bio-inspired wearable electronics

Wearable electronics featuring conformal attachment, sensitive perception and intellectual signal processing have made significant progress in recent years. However, when compared with living organisms, artificial sensory devices showed undeniable bulky shape, poor adaptability, and large energy consumption. To make up for the deficiencies, biological examples provide inspirations of novel designs and practical applications. In the field of biomimetics, nanomaterials from nanoparticles to layered two-dimensional materials are actively involved due to their outstanding physicochemical properties and nanoscale configurability. This review focuses on nanomaterials related to wearable electronics through bioinspired approaches on three different levels, interfacial packaging, sensory structure, and signal processing, which comprehensively guided recent progress of wearable devices in leveraging both nanomaterial superiorities and biorealistic functionalities. In addition, opinions on potential development trend are proposed aiming at implementing bioinspired electronics in multifunctional portable sensors, health monitoring, and intelligent prosthetics.

A Roadmap for Disruptive Applications and Heterogeneous Integration Using Two-Dimensional Materials: State-of-the-Art and Technological Challenges

This Mini Review attempts to establish a roadmap for two-dimensional (2D) material-based microelectronic technologies for future/disruptive applications with a vision for the semiconductor industry to enable a universal technology platform for heterogeneous integration. The heterogeneous integration would involve integrating orthogonal capabilities, such as different forms of computing (classical, neuromorphic, and quantum), all forms of sensing, digital and analog memories, energy harvesting, and so forth, all in a single chip using a universal technology platform. We have reviewed the state-of-the-art 2D materials such as graphene, transition metal dichalcogenides, phosphorene and hexagonal boron nitride, and so forth, and how they offer unique possibilities for a range of futuristic/disruptive applications. Besides, we have discussed the technological and fundamental challenges in enabling such a universal technology platform, where the world stands today, and what gaps are required to be filled.

Biomedical Implants with Charge-Transfer Monitoring and Regulating Abilities

Transmembrane charge (ion/electron) transfer is essential for maintaining cellular homeostasis and is involved in many biological processes, from protein synthesis to embryonic development in organisms. Designing implant devices that can detect or regulate cellular transmembrane charge transfer is expected to sense and modulate the behaviors of host cells and tissues. Thus, charge transfer can be regarded as a bridge connecting living systems and human-made implantable devices. This review describes the mode and mechanism of charge transfer between organisms and nonliving materials, and summarizes the strategies to endow implants with charge-transfer regulating or monitoring abilities. Furthermore, three major charge-transfer controlling systems, including wired, self-activated, and stimuli-responsive biomedical implants, as well as the design principles and pivotal materials are systematically elaborated. The clinical challenges and the prospects for future development of these implant devices are also discussed.

Emerging 2D Memory Devices for In-Memory Computing

It is predicted that the conventional von Neumann computing architecture cannot meet the demands of future data-intensive computing applications due to the bottleneck between the processing and memory units. To try to solve this problem, in-memory computing technology, where calculations are carried out in situ within each nonvolatile memory unit, has been intensively studied. Among various candidate materials, 2D layered materials have recently demonstrated many new features that have been uniquely exploited to build next-generation electronics. Here, the recent progress of 2D memory devices is reviewed for in-memory computing. For each memory configuration, their operation mechanisms and memory characteristics are described, and their pros and cons are weighed. Subsequently, their versatile applications for in-memory computing technology, including logic operations, electronic synapses, and random number generation are presented. Finally, the current challenges and potential strategies for future 2D in-memory computing systems are also discussed at the material, device, circuit, and architecture levels. It is hoped that this manuscript could give a comprehensive review of 2D memory devices and their applications in in-memory computing, and be helpful for this exciting research area.

Doping Concentration Modulation in Vanadium-Doped Monolayer Molybdenum Disulfide for Synaptic Transistors

Doping is an effective way to modify the electronic property of two-dimensional (2D) materials and endow them with additional functionalities. However, wide-range control of the doping concentrations in monolayer 2D materials with large-scale uniformity remains challenging. Here, we report in situ chemical vapor deposition growth of vanadium-doped monolayer molybdenum disulfide (MoS₂) with widely tunable doping concentrations ranging from 0.3 to 13.1 atom %. The key to regulate the doping concentration lies in the use of appropriate vanadium precursors with different doping abilities, which also generate large-scale uniform doping to MoS₂. Artificial synaptic transistors were fabricated using the heavily doped MoS₂ as the channel material. Synaptic potentiation, depression, and repetitive learning processes were mimicked by the gate-tunable changes of channel conductance in such transistors with abundant vanadium atoms to trap/detrap electrons. This work develops a feasible method to dope monolayer 2D semiconductors and demonstrates their applications in artificial synaptic transistors.

Broadband and Tunable Light Harvesting in Nanorippled MoS2 Ultrathin Films

Nanofabrication of flat optic silica gratings conformally layered with two-dimensional (2D) MoS₂ is demonstrated over large area (cm²), achieving a strong amplification of the photon absorption in the active 2D layer. The anisotropic subwavelength silica gratings induce a highly ordered periodic modulation of the MoS₂ layer, promoting the excitation of Guided Mode Anomalies (GMA) at the interfaces of the 2D layer. We show the capability to achieve a broadband tuning of these lattice modes from the visible (VIS) to the near-infrared (NIR) by simply tailoring the illumination conditions and/or the period of the lattice. Remarkably, we demonstrate the possibility to strongly confine resonant and nonresonant light into the 2D MoS₂ layers via GMA excitation, leading to a strong absorption enhancement as high as 240% relative to a flat continuous MoS₂ film. Due to their broadband and tunable photon harvesting capabilities, these large area 2D MoS₂ metastructures represent an ideal scalable platform for new generation devices in nanophotonics, photo- detection and -conversion, and quantum technologies.

Recent Process of Flexible Transistor-Structured Memory

Flexible transistor-structured memory (FTSM) has attracted great attention for its important role in flexible electronics. For nonvolatile information storage, FTSMs with floating-gate, charge-trap, and ferroelectric mechanisms have been developed. By introducing an optical sensory module, FTSM can be operated by optical inputs to function as an optical memory transistor. As a special type of FTSM, transistor-structured artificial synapse emulates important functions of a biological synapse to mimic brain-inspired memory behaviors and nervous signal transmissions. This work reviews the recent development of the above mentioned FTSMs, with a focus on working mechanism and materials, and flexibility.

High-Performance Organic Synaptic Transistors with an Ultrathin Active Layer for Neuromorphic Computing

In recent years, much attention has been focused on two-dimensional (2D) material-based synaptic transistor devices because of their inherent advantages of low dimension, simultaneous read-write operation and high efficiency. However, process compatibility and repeatability of these materials are still a big challenge, as well as other issues such as complex transfer process and material selectivity. In this work, synaptic transistors with an ultrathin organic semiconductor layer (down to 7 nm) were obtained by the simple dip-coating process, which exhibited a high current switch ratio up to 10⁶, well off state as low as nearly 10^-12 A, and low operation voltage of -3 V. Moreover, various synaptic behaviors were successfully simulated including excitatory postsynaptic current, paired pulse facilitation, long-term potentiation, and long-term depression. More importantly, under ultrathin conditions, excellent memory preservation, and linearity of weight update were obtained because of the enhanced effect of defects and improved controllability of the gate voltage on the ultrathin active layer, which led to a pattern recognition rate up to 85%. This is the first work to demonstrate that the pattern recognition rate, a crucial parameter for neuromorphic computing can be significantly improved by reducing the thickness of the channel layer. Hence, these results not only reveal a simple and effective way to improve plasticity and memory retention of the artificial synapse via thickness modulation but also expand the material selection for the 2D artificial 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.

Mimicking Neurotransmitter Activity and Realizing Algebraic Arithmetic on Flexible Protein-Gated Oxide Neuromorphic Transistors

Recently, flexible neuromorphic devices have attracted extensive attention for the construction of perception cognitive systems with the ultimate objective to achieve robust computation, efficient learning, and adaptability to evolutionary changes. In particular, the design of flexible neuromorphic devices with data processing and arithmetic capabilities is highly desirable for wearable cognitive platforms. Here, an albumen-based protein-gated flexible indium tin oxide (ITO) ionotronic neuromorphic transistor was proposed. First, the transistor demonstrates excellent mechanical robustness against bending stress. Moreover, spike-duration-dependent synaptic plasticity and spike-amplitude-dependent synaptic plasticity behaviors are not affected by bending stress. With the unique protonic gating behaviors, neurotransmission processes in biological synapses are emulated, exhibiting three characteristics in neurotransmitter release, including quantal release, stochastic release, and excitatory or inhibitory release. In addition, three types of spike-timing-dependent plasticity learning rules are mimicked on the ITO ionotronic neuromorphic transistor. Most interestingly, algebraic arithmetic operations, including addition, subtraction, multiplication, and division, are implemented on the protein gated neuromorphic transistor for the first time. The present work would open a promising biorealistic avenue to the scientific community to control and design wearable "green" cognitive platforms, with potential applications including but not limited to intelligent humanoid robots and replacement neuroprosthetics.