A biomimetic 2D transistor for audiomorphic computing

Enhance Carrier Diffusion of Monolayer MoSe2 by Interface Engineering

Two-dimensional materials hold great potentials for beyond-CMOS (complementary metal-oxide-semiconductor) electronical and optoelectrical applications, and the development of field effect transistors (FET) with excellent performance using such materials is of particular interest. How to improve the performance of devices thus becomes an urgent issue. The performance of FETs depends greatly on the intrinsic electrical properties of the channel materials, meanwhile the device interface quality, such as extrinsic scattering of charged impurities, charge traps, and substrate surface roughness have a great influence on the performance. In this paper, the impact of the interface quality on the carrier diffusion behaviors of monolayer (ML) MoSe2 has been investigated by using an in situ ultrafast laser technique to avoid the surface contamination during device fabrication process. Two types of self-assembled monolayers (SAMs) are introduced to modify the gate dielectric surface through an interface engineering approach to obtain chemical-stable interfaces. The results showed that the transport properties of ML MoSe2 were enhanced after interface engineering, for example, the carrier mobility of ML MoSe2 was improved from ∼59.4 to ∼166.5 cm2 V-1 s-1 after the SAM modification. Meanwhile, the photocarrier dynamics of ML MoSe2 before and after interfacial engineering were also carefully studied. Our studies provide a feasible method for improving the carrier diffusion behaviors of such materials, and making them suited for application in future integrated circuit.

Recent Progress in Wearable Near-Sensor and In-Sensor Intelligent Perception Systems

As the Internet of Things (IoT) becomes more widespread, wearable smart systems will begin to be used in a variety of applications in people's daily lives, not only requiring the devices to have excellent flexibility and biocompatibility, but also taking into account redundant data and communication delays due to the use of a large number of sensors. Fortunately, the emerging paradigms of near-sensor and in-sensor computing, together with the proposal of flexible neuromorphic devices, provides a viable solution for the application of intelligent low-power wearable devices. Therefore, wearable smart systems based on new computing paradigms are of great research value. This review discusses the research status of a flexible five-sense sensing system based on near-sensor and in-sensor architectures, considering material design, structural design and circuit design. Furthermore, we summarize challenging problems that need to be solved and provide an outlook on the potential applications of intelligent wearable devices.

Artificial Retina Based on Organic Heterojunction Transistors for Mobile Recognition

The flicker frequency of incident light constitutes a critical determinant in biology. Nevertheless, the exploration of methods to simulate external light stimuli with varying frequencies and develop artificial retinal neurons capable of responsive behavior remains an open question. This study presents an artificial neuron comprising organic phototransistors. The triggering properties of neurons are modulated by optical input, enabling them to execute rudimentary synaptic functions, emulating the biological characteristics of retinal neurons. The artificial retinal neuron exhibits varying responses to incoming light frequencies, allowing it to replicate the persistent visual behavior of the human eye and facilitating image discrimination. Additionally, through seamless integration with circuitry, it can execute motion recognition on a machine cart, preventing collisions with high-speed obstacles. The artificial retinal neuron offers a cost-effective and energy-efficient route for future mobile robot processors.

Model organisms and systems in neuroethology: one hundred years of history and a look into the future

The Journal of Comparative Physiology lived up to its name in the last 100 years by including more than 1500 different taxa in almost 10,000 publications. Seventeen phyla of the animal kingdom were represented. The honeybee (Apis mellifera) is the taxon with most publications, followed by locust (Locusta migratoria), crayfishes (Cambarus spp.), and fruitfly (Drosophila melanogaster). The representation of species in this journal in the past, thus, differs much from the 13 model systems as named by the National Institutes of Health (USA). We mention major accomplishments of research on species with specific adaptations, specialist animals, for example, the quantitative description of the processes underlying the axon potential in squid (Loligo forbesii) and the isolation of the first receptor channel in the electric eel (Electrophorus electricus) and electric ray (Torpedo spp.). Future neuroethological work should make the recent genetic and technological developments available for specialist animals. There are many research questions left that may be answered with high yield in specialists and some questions that can only be answered in specialists. Moreover, the adaptations of animals that occupy specific ecological niches often lend themselves to biomimetic applications. We go into some depth in explaining our thoughts in the research of motion vision in insects, sound localization in barn owls, and electroreception in weakly electric fish.

Artificial Neuron Devices

Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.

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.

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.

A low-power vertical dual-gate neurotransistor with short-term memory for high energy-efficient neuromorphic computing

Neuromorphic computing aims to emulate the computing processes of the brain by replicating the functions of biological neural networks using electronic counterparts. One promising approach is dendritic computing, which takes inspiration from the multi-dendritic branch structure of neurons to enhance the processing capability of artificial neural networks. While there has been a recent surge of interest in implementing dendritic computing using emerging devices, achieving artificial dendrites with throughputs and energy efficiency comparable to those of the human brain has proven challenging. In this study, we report on the development of a compact and low-power neurotransistor based on a vertical dual-gate electrolyte-gated transistor (EGT) with short-term memory characteristics, a 30 nm channel length, a record-low read power of ~3.16 fW and a biology-comparable read energy of ~30 fJ. Leveraging this neurotransistor, we demonstrate dendrite integration as well as digital and analog dendritic computing for coincidence detection. We also showcase the potential of neurotransistors in realizing advanced brain-like functions by developing a hardware neural network and demonstrating bio-inspired sound localization. Our results suggest that the neurotransistor-based approach may pave the way for next-generation neuromorphic computing with energy efficiency on par with those of the brain.

Trainable Bilingual Synaptic Functions in Bio-enabled Synaptic Transistors

The signal transmission of the nervous system is regulated by neurotransmitters. Depending on the type of neurotransmitter released by presynaptic neurons, neuron cells can either be excited or inhibited. Maintaining a balance between excitatory and inhibitory synaptic responses is crucial for the nervous system's versatility, elasticity, and ability to perform parallel computing. On the way to mimic the brain's versatility and plasticity traits, creating a preprogrammed balance between excitatory and inhibitory responses is required. Despite substantial efforts to investigate the balancing of the nervous system, a complex circuit configuration has been suggested to simulate the interaction between excitatory and inhibitory synapses. As a meaningful approach, an optoelectronic synapse for balancing the excitatory and inhibitory responses assisted by light mediation is proposed here by deploying humidity-sensitive chiral nematic phases of known polysaccharide cellulose nanocrystals. The environment-induced pitch tuning changes the polarization of the helicoidal organization, affording different hysteresis effects with the subsequent excitatory and inhibitory nonvolatile behavior in the bio-electrolyte-gated transistors. By applying voltage pulses combined with stimulation of chiral light, the artificial optoelectronic synapse tunes not only synaptic functions but also learning pathways and color recognition. These multifunctional bio-based synaptic field-effect transistors exhibit potential for enhanced parallel neuromorphic computing and robot vision technology.

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.

Dielectrics for Two-Dimensional Transition-Metal Dichalcogenide Applications

Despite over a decade of intense research efforts, the full potential of two-dimensional transition-metal dichalcogenides continues to be limited by major challenges. The lack of compatible and scalable dielectric materials and integration techniques restrict device performances and their commercial applications. Conventional dielectric integration techniques for bulk semiconductors are difficult to adapt for atomically thin two-dimensional materials. This review provides a brief introduction into various common and emerging dielectric synthesis and integration techniques and discusses their applicability for 2D transition metal dichalcogenides. Dielectric integration for various applications is reviewed in subsequent sections including nanoelectronics, optoelectronics, flexible electronics, valleytronics, biosensing, quantum information processing, and quantum sensing. For each application, we introduce basic device working principles, discuss the specific dielectric requirements, review current progress, present key challenges, and offer insights into future prospects and opportunities.

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.

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.

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.

Nanopatterning Technologies of 2D Materials for Integrated Electronic and Optoelectronic Devices

With the reduction of feature size and increase of integration density, traditional 3D semiconductors are unable to meet the future requirements of chip integration. The current semiconductor fabrication technologies are approaching their physical limits based on Moore's law. 2D materials such as graphene, transitional metal dichalcogenides, etc., are of great promise for future memory, logic, and photonic devices due to their unique and excellent properties. To prompt 2D materials and devices from the laboratory research stage to the industrial integrated circuit-level, it is necessary to develop advanced nanopatterning methods to obtain high-quality, wafer-scale, and patterned 2D products. Herein, the recent development of nanopatterning technologies, particularly toward realizing large-scale practical application of 2D materials is reviewed. Based on the technological progress, the unique requirement and advances of the 2D integration process for logic, memory, and optoelectronic devices are further summarized. Finally, the opportunities and challenges of nanopatterning technologies of 2D materials for future integrated chip devices are prospected.

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.

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.

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.

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.

Neuromorphic object localization using resistive memories and ultrasonic transducers

Real-world sensory-processing applications require compact, low-latency, and low-power computing systems. Enabled by their in-memory event-driven computing abilities, hybrid memristive-Complementary Metal-Oxide Semiconductor neuromorphic architectures provide an ideal hardware substrate for such tasks. To demonstrate the full potential of such systems, we propose and experimentally demonstrate an end-to-end sensory processing solution for a real-world object localization application. Drawing inspiration from the barn owl's neuroanatomy, we developed a bio-inspired, event-driven object localization system that couples state-of-the-art piezoelectric micromachined ultrasound transducer sensors to a neuromorphic resistive memories-based computational map. We present measurement results from the fabricated system comprising resistive memories-based coincidence detectors, delay line circuits, and a full-custom ultrasound sensor. We use these experimental results to calibrate our system-level simulations. These simulations are then used to estimate the angular resolution and energy efficiency of the object localization model. The results reveal the potential of our approach, evaluated in orders of magnitude greater energy efficiency than a microcontroller performing the same task.

An Annealing Accelerator for Ising Spin Systems Based on In-Memory Complementary 2D FETs

Metaheuristic algorithms such as simulated annealing (SA) are often implemented for optimization in combinatorial problems, especially for discreet problems. SA employs a stochastic search, where high-energy transitions ("hill-climbing") are allowed with a temperature-dependent probability to escape local optima. Ising spin glass systems have properties such as spin disorder and "frustration" and provide a discreet combinatorial problem with a high number of metastable states and ground-state degeneracy. In this work, subthreshold Boltzmann transport is exploited in complementary 2D field-effect transistors (p-type WSe₂ and n-type MoS₂ ) integrated with an analog, nonvolatile, and programmable floating-gate memory stack to develop in-memory computing primitives necessary for energy- and area-efficient hardware acceleration of SA for Ising spin systems. Search acceleration of >800× is demonstrated for 4 × 4 ferromagnetic, antiferromagnetic, and spin glass systems using SA compared to an exhaustive search using a brute force trial at miniscule total energy expenditure of ≈120 nJ. The hardware-realistic numerical simulations further highlight the astounding benefits of SA in accelerating the search for larger spin lattices.

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.

Stimuli-Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing

Neuromorphic computing holds promise for building next-generation intelligent systems in a more energy-efficient way than the conventional von Neumann computing architecture. Memristive hardware, which mimics biological neurons and synapses, offers high-speed operation and low power consumption, enabling energy- and area-efficient, brain-inspired computing. Here, recent advances in memristive materials and strategies that emulate synaptic functions for neuromorphic computing are highlighted. The working principles and characteristics of biological neurons and synapses, which can be mimicked by memristive devices, are presented. Besides device structures and operation with different external stimuli such as electric, magnetic, and optical fields, how memristive materials with a rich variety of underlying physical mechanisms can allow fast, reliable, and low-power neuromorphic applications is also discussed. Finally, device requirements are examined and a perspective on challenges in developing memristive materials for device engineering and computing science is given.

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.

Review and comparison of layer transfer methods for two-dimensional materials for emerging applications

Two-dimensional (2D) materials offer immense potential for scientific breakthroughs and technological innovations. While early demonstrations of 2D material-based electronics, optoelectronics, flextronics, straintronics, twistronics, and biomimetic devices exploited micromechanically-exfoliated single crystal flakes, recent years have witnessed steady progress in large-area growth techniques such as physical vapor deposition (PVD), chemical vapor deposition (CVD), and metal-organic CVD (MOCVD). However, use of high growth temperatures, chemically-active growth precursors and promoters, and the need for epitaxy often limit direct growth of 2D materials on the substrates of interest for commercial applications. This has led to the development of a large number of methods for the layer transfer of 2D materials from the growth substrate to the target application substrate with varying degrees of cleanliness, uniformity, and transfer-related damage. This review aims to catalog and discuss these layer transfer methods. In particular, the processes, advantages, and drawbacks of various transfer methods are discussed, as is their applicability to different technological platforms of interest for 2D material implementation.

A biomimetic neural encoder for spiking neural network

Spiking neural networks (SNNs) promise to bridge the gap between artificial neural networks (ANNs) and biological neural networks (BNNs) by exploiting biologically plausible neurons that offer faster inference, lower energy expenditure, and event-driven information processing capabilities. However, implementation of SNNs in future neuromorphic hardware requires hardware encoders analogous to the sensory neurons, which convert external/internal stimulus into spike trains based on specific neural algorithm along with inherent stochasticity. Unfortunately, conventional solid-state transducers are inadequate for this purpose necessitating the development of neural encoders to serve the growing need of neuromorphic computing. Here, we demonstrate a biomimetic device based on a dual gated MoS2 field effect transistor (FET) capable of encoding analog signals into stochastic spike trains following various neural encoding algorithms such as rate-based encoding, spike timing-based encoding, and spike count-based encoding. Two important aspects of neural encoding, namely, dynamic range and encoding precision are also captured in our demonstration. Furthermore, the encoding energy was found to be as frugal as ≈1-5 pJ/spike. Finally, we show fast (≈200 timesteps) encoding of the MNIST data set using our biomimetic device followed by more than 91% accurate inference using a trained SNN.

Satisfiability Attack-Resistant Camouflaged Two-Dimensional Heterostructure Devices

Reverse engineering (RE) is one of the major security threats to the semiconductor industry due to the involvement of untrustworthy parties in an increasingly globalized chip manufacturing supply chain. RE efforts have already been successful in extracting device level functionalities from an integrated circuit (IC) with very limited resources. Camouflaging is an obfuscation method that can thwart such RE. Existing work on IC camouflaging primarily involves transformable interconnects and/or covert gates where variation in doping and dummy contacts hide the circuit structure or build cells that look alike but have different functionalities. Emerging solutions, such as polymorphic gates based on a giant spin Hall effect and Si nanowire field effect transistors (FETs), are also promising but add significant area overhead and are successfully decamouflaged by the satisfiability solver (SAT)-based RE techniques. Here, we harness the properties of two-dimensional (2D) transition-metal dichalcogenides (TMDs) including MoS₂, MoSe₂, MoTe₂, WS₂, and WSe₂ and their optically transparent transition-metal oxides (TMOs) to demonstrate area efficient camouflaging solutions that are resilient to SAT attack and automatic test pattern generation attacks. We show that resistors with resistance values differing by 5 orders of magnitude, diodes with variable turn-on voltages and reverse saturation currents, and FETs with adjustable conduction type, threshold voltages, and switching characteristics can be optically camouflaged to look exactly similar by engineering TMO/TMD heterostructures, allowing hardware obfuscation of both digital and analog circuits. Since this 2D heterostructure devices family is intrinsically camouflaged, NAND/NOR/AND/OR gates in the circuit can be obfuscated with significantly less area overhead, allowing 100% logic obfuscation compared to only 5% for complementary metal oxide semiconductor (CMOS)-based camouflaging. Finally, we demonstrate that the largest benchmarking circuit from ISCAS'85, comprised of more than 4000 logic gates when obfuscated with the CMOS-based technique, is successfully decamouflaged by SAT attack in <40 min; whereas, it renders to be invulnerable even in more than 10 h when camouflaged with 2D heterostructure devices, thereby corroborating our hypothesis of high resilience against RE. Our approach of connecting material properties to innovative devices to secure circuits can be considered as a one of a kind demonstration, highlighting the benefits of cross-layer optimization.

Benchmarking monolayer MoS2 and WS2 field-effect transistors

Here we benchmark device-to-device variation in field-effect transistors (FETs) based on monolayer MoS₂ and WS₂ films grown using metal-organic chemical vapor deposition process. Our study involves 230 MoS₂ FETs and 160 WS₂ FETs with channel lengths ranging from 5 μm down to 100 nm. We use statistical measures to evaluate key FET performance indicators for benchmarking these two-dimensional (2D) transition metal dichalcogenide (TMD) monolayers against existing literature as well as ultra-thin body Si FETs. Our results show consistent performance of 2D FETs across 1 × 1 cm² chips owing to high quality and uniform growth of these TMDs followed by clean transfer onto device substrates. We are able to demonstrate record high carrier mobility of 33 cm² V^-1 s^-1 in WS₂ FETs, which is a 1.5X improvement compared to the best reported in the literature. Our experimental demonstrations confirm the technological viability of 2D FETs in future integrated circuits.

Low-Power and Ultra-Thin MoS2 Photodetectors on Glass

Integration of low-power consumer electronics on glass can revolutionize the automotive and transport sectors, packaging industry, smart building and interior design, healthcare, life science engineering, display technologies, and many other applications. However, direct growth of high-performance, scalable, and reliable electronic materials on glass is difficult owing to low thermal budget. Similarly, development of energy-efficient electronic and optoelectronic devices on glass requires manufacturing innovations. Here, we accomplish both by relatively low-temperature (<600 °C) metal-organic chemical vapor deposition growth of atomically thin MoS₂ on multicomponent glass and fabrication of low-power phototransistors using atomic layer deposition (ALD)-grown, high-k, and ultra-thin (∼20 nm) Al₂O₃ as the top-gate dielectric, circumventing the challenges associated with the ALD nucleation of oxides on inert basal planes of van der Waals materials. The MoS₂ photodetectors demonstrate the ability to detect low-intensity visible light at high speed and low energy expenditure of ∼100 pico Joules. Furthermore, low device-to-device performance variation across the entire 1 cm² substrate and aggressive channel length scalability confirm the technology readiness level of ultra-thin MoS₂ photodetectors on glass.

Hardware implementation of photoelectrically modulated dendritic arithmetic and spike-timing-dependent plasticity enabled by an ion-coupling gate-tunable vertical 0D-perovskite/2D-MoS2 hybrid-dimensional van der Waals heterostructure

Brain-inspired nanodevices have been demonstrated to possess outstanding characteristics for implementing neuromorphic computing. Among these devices, photoelectrically modulated neuromorphic transistors are regarded as the basic building blocks for applications in emerging brain-like devices. However, to date, efficient optoelectronic-hybrid neuromorphic devices are still lacking. Because conventional transistors based on mono-semiconductor materials cannot absorb adequate light to ensure efficient light-matter interactions, they pose significant challenges to the synchronous processing of photoelectric information. Here, a novel photoelectrically modulated neuromorphic device based on an ion-coupling gate-tunable vertical 0D-CsPbBr3-quantum-dots/2D-MoS2 hybrid-dimensional van der Waals heterojunction is demonstrated by using a polymer ion gel electrolyte as the gate dielectric. A super-efficient heterojunction interface for photo-carrier transport is developed by integrating CsPbBr3 quantum dots with 2D-layered MoS2 semiconductors. We experimentally demonstrate that the drain-source current can be modulated by applying spikes to the drain and gate terminals, and the conductance can also be tuned by external light stimulus. Most importantly, photoelectrically modulated spiking Boolean logics, dendritic integrations in both temporal and spatial modes, and Hebbian learning rules can be successfully mimicked in our proposed hybrid-dimensional device using this intriguing optical and electrical synergy approach. These results suggest that the proposed device has great potential in intelligent cognitive systems and neuromorphic computing applications.

Vertical 0D-Perovskite/2D-MoS2 van der Waals Heterojunction Phototransistor for Emulating Photoelectric-Synergistically Classical Pavlovian Conditioning and Neural Coding Dynamics

Optoelectronic-neuromorphic transistors are vital for next-generation nanoscale brain-like computational systems. However, the hardware implementation of optoelectronic-neuromorphic devices, which are based on conventional transistor architecture, faces serious challenges with respect to the synchronous processing of photoelectric information. This is because mono-semiconductor material cannot absorb adequate light to ensure efficient light-matter interactions. In this work, a novel neuromorphic-photoelectric device of vertical van der Waals heterojunction phototransistors based on a colloidal 0D-CsPbBr₃ -quantum-dots/2D-MoS₂ heterojunction channel is proposed using a polymer ion gel electrolyte as the gate dielectric. A highly efficient photocarrier transport interface is established by introducing colloidal perovskite quantum dots with excellent light absorption capabilities on the 2D-layered MoS₂ semiconductor with strong carrier transport abilities. The device exhibits not only high photoresponsivity but also fundamental synaptic characteristics, such as excitatory postsynaptic current, paired-pulse facilitation, dynamic temporal filter, and light-tunable synaptic plasticity. More importantly, efficiency-adjustable photoelectronic Pavlovian conditioning and photoelectronic hybrid neuronal coding behaviors can be successfully implemented using the optical and electrical synergy approach. The results suggest that the proposed device has potential for applications associated with next-generation brain-like photoelectronic human-computer interactions and cognitive systems.

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.

Nonvolatile and Neuromorphic Memory Devices Using Interfacial Traps in Two-Dimensional WSe2/MoTe2 Stack Channel

Very recently, stacked two-dimensional materials have been studied, focusing on the van der Waals interaction at their stack junction interface. Here, we report field effect transistors (FETs) with stacked transition metal dichalcogenide (TMD) channels, where the heterojunction interface between two TMDs appears useful for nonvolatile or neuromorphic memory FETs. A few nanometer-thin WSe₂ and MoTe₂ flakes are vertically stacked on the gate dielectric, and bottom p-MoTe₂ performs as a channel for hole transport. Interestingly, the WSe₂/MoTe₂ stack interface functions as a hole trapping site where traps behave in a nonvolatile manner, although trapping/detrapping can be controlled by gate voltage (V_GS). Memory retention after high V_GS pulse appears longer than 10000 s, and the Program/Erase ratio in a drain current is higher than 200. Moreover, the traps are delicately controllable even with small V_GS, which indicates that a neuromorphic memory is also possible with our heterojunction stack FETs. Our stack channel FET demonstrates neuromorphic memory behavior of ∼94% recognition accuracy.

Stochastic resonance in MoS2 photodetector

In this article, we adopt a radical approach for next generation ultra-low-power sensor design by embracing the evolutionary success of animals with extraordinary sensory information processing capabilities that allow them to survive in extreme and resource constrained environments. Stochastic resonance (SR) is one of those astounding phenomena, where noise, which is considered detrimental for electronic circuits and communication systems, plays a constructive role in the detection of weak signals. Here, we show SR in a photodetector based on monolayer MoS₂ for detecting ultra-low-intensity subthreshold optical signals from a distant light emitting diode (LED). We demonstrate that weak periodic LED signals, which are otherwise undetectable, can be detected by a MoS₂ photodetector in the presence of a finite and optimum amount of white Gaussian noise at a frugal energy expenditure of few tens of nano-Joules. The concept of SR is generic in nature and can be extended beyond photodetector to any other sensors.

Here, the authors take advantage of stochastic resonance in a photodetector based on monolayer MoS₂ for measuring otherwise undetectable, ultra-low-intensity, subthreshold optical signals from a distant light emitting diode in the presence of a finite and optimum amount of white Gaussian noise.

Multi-terminal Transistor-like Devices Based on Strongly Correlated Metallic Oxides for Neuromorphic Applications

Memristive devices are attracting a great attention for memory, logic, neural networks, and sensing applications due to their simple structure, high density integration, low-power consumption, and fast operation. In particular, multi-terminal structures controlled by active gates, able to process and manipulate information in parallel, would certainly provide novel concepts for neuromorphic systems. In this way, transistor-based synaptic devices may be designed, where the synaptic weight in the postsynaptic membrane is encoded in a source-drain channel and modified by presynaptic terminals (gates). In this work, we show the potential of reversible field-induced metal-insulator transition (MIT) in strongly correlated metallic oxides for the design of robust and flexible multi-terminal memristive transistor-like devices. We have studied different structures patterned on YBa2Cu3O7-δ films, which are able to display gate modulable non-volatile volume MIT, driven by field-induced oxygen diffusion within the system. The key advantage of these materials is the possibility to homogeneously tune the oxygen diffusion not only in a confined filament or interface, as observed in widely explored binary and complex oxides, but also in the whole material volume. Another important advantage of correlated oxides with respect to devices based on conducting filaments is the significant reduction of cycle-to-cycle and device-to-device variations. In this work, we show several device configurations in which the lateral conduction between a drain-source channel (synaptic weight) is effectively controlled by active gate-tunable volume resistance changes, thus providing the basis for the design of robust and flexible transistor-based artificial synapses.