Robust chemical analysis with graphene chemosensors and machine learning.

Ion-sensitive field-effect transistors (ISFETs) have emerged as indispensable tools in chemosensing applications1-4. ISFETs operate by converting changes in the composition of chemical solutions into electrical signals, making them ideal for environmental monitoring5,6, healthcare diagnostics7 and industrial process control8. Recent advancements in ISFET technology, including functionalized multiplexed arrays and advanced data analytics, have improved their performance9,10. Here we illustrate the advantages of incorporating machine learning algorithms to construct predictive models using the extensive datasets generated by ISFET sensors for both classification and quantification tasks. This integration also sheds new light on the working of ISFETs beyond what can be derived solely from human expertise. Furthermore, it mitigates practical challenges associated with cycle-to-cycle, sensor-to-sensor and chip-to-chip variations, paving the way for the broader adoption of ISFETs in commercial applications. Specifically, we use data generated by non-functionalized graphene-based ISFET arrays to train artificial neural networks that possess a remarkable ability to discern instances of food fraud, food spoilage and food safety concerns. We anticipate that the fusion of compact, energy-efficient and reusable graphene-based ISFET technology with robust machine learning algorithms holds the potential to revolutionize the detection of subtle chemical and environmental changes, offering swift, data-driven insights applicable across a wide spectrum of applications.

Three-dimensional integration of two-dimensional field-effect transistors.

In the field of semiconductors, three-dimensional (3D) integration not only enables packaging of more devices per unit area, referred to as 'More Moore'1 but also introduces multifunctionalities for 'More than Moore'2 technologies. Although silicon-based 3D integrated circuits are commercially available3-5, there is limited effort on 3D integration of emerging nanomaterials6,7 such as two-dimensional (2D) materials despite their unique functionalities7-10. Here we demonstrate (1) wafer-scale and monolithic two-tier 3D integration based on MoS2 with more than 10,000 field-effect transistors (FETs) in each tier; (2) three-tier 3D integration based on both MoS2 and WSe2 with about 500 FETs in each tier; and (3) two-tier 3D integration based on 200 scaled MoS2 FETs (channel length, LCH = 45 nm) in each tier. We also realize a 3D circuit and demonstrate multifunctional capabilities, including sensing and storage. We believe that our demonstrations will serve as the foundation for more sophisticated, highly dense and functionally divergent integrated circuits with a larger number of tiers integrated monolithically in the third dimension.

A Crayfish-Inspired Sensor Fusion Platform for Super Additive Integration of Visual, Chemical, and Tactile Information.

The concept of cross-sensor modulation, wherein one sensor modality can influence another's response, is often overlooked in traditional sensor fusion architectures, leading to missed opportunities for enhancing data accuracy and robustness. In contrast, biological systems, such as aquatic animals like crayfish, demonstrate superior sensor fusion through multisensory integration. These organisms adeptly integrate visual, tactile, and chemical cues to perform tasks such as evading predators and locating prey. Drawing inspiration from this, we propose a neuromorphic platform that integrates graphene-based chemitransistors, monolayer molybdenum disulfide (MoS2) based photosensitive memtransistors, and triboelectric tactile sensors to achieve "Super-Additive" responses to weak chemical, visual, and tactile cues and demonstrate contextual response modulation, also referred to as the "Inverse Effectiveness Effect." We hold the view that integrating bio-inspired sensor fusion principles across various modalities holds promise for a wide range of applications.

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.

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.

Radiation Resilient Two-Dimensional Electronics.

Limitations in cloud-based computing have prompted a paradigm shift toward all-in-one "edge" devices capable of independent data sensing, computing, and storage. Advanced defense and space applications stand to benefit immensely from this due to their need for continual operation in areas where maintaining remote oversight is difficult. However, the extreme environments relevant to these applications necessitate rigorous testing of technologies, with a common requirement being hardness to ionizing radiation. Two-dimensional (2D) molybdenum disulfide (MoS2) has been noted to enable the sensing, storage, and logic capabilities necessary for all-in-one edge devices. Despite this, the investigation of ionizing radiation effects in MoS2-based devices remains incomplete. In particular, studies on gamma radiation effects in MoS2 have been largely limited to standalone films, with few device investigations; to the best of our knowledge, no explorations have been made into gamma radiation effects on the sensing and memory capabilities of MoS2-based devices. In this work, we have used a statistical approach to study high-dose (1 Mrad) gamma radiation effects on photosensitive and programmable memtransistors fabricated from large-area monolayer MoS2. Memtransistors were divided into separate groups to ensure accurate extraction of device characteristics pertaining to baseline performance, sensing, and memory before and after irradiation. All-MoS2 logic gates were also assessed to determine the gamma irradiation impact on logic implementation. Our findings show that the multiple functionalities of MoS2 memtransistors are not severely impacted by gamma irradiation even without dedicated shielding/mitigation techniques. We believe that these results serve as a foundation for more application-oriented studies going forward.

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.

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.

Insect-Inspired, Spike-Based, in-Sensor, and Night-Time Collision Detector Based on Atomically Thin and Light-Sensitive Memtransistors.

Detecting a potential collision at night is a challenging task owing to the lack of discernible features that can be extracted from the available visual stimuli. To alert the driver or, alternatively, the maneuvering system of an autonomous vehicle, current technologies utilize resource draining and expensive solutions such as light detection and ranging (LiDAR) or image sensors coupled with extensive software running sophisticated algorithms. In contrast, insects perform the same task of collision detection with frugal neural resources. Even though the general architecture of separate sensing and processing modules is the same in insects and in image-sensor-based collision detectors, task-specific obstacle avoidance algorithms allow insects to reap substantial benefits in terms of size and energy. Here, we show that insect-inspired collision detection algorithms, when implemented in conjunction with in-sensor processing and enabled by innovative optoelectronic integrated circuits based on atomically thin and photosensitive memtransistor technology, can greatly simplify collision detection at night. The proposed collision detector eliminates the need for image capture and image processing yet demonstrates timely escape responses for cars on collision courses under various real-life scenarios at night. The collision detector also has a small footprint of ∼40 µm2 and consumes only a few hundred picojoules of energy. We strongly believe that the proposed collision detectors can augment existing sensors necessary for ensuring autonomous vehicular safety.

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.

Gaussian synapses for probabilistic neural networks.

The recent decline in energy, size and complexity scaling of traditional von Neumann architecture has resurrected considerable interest in brain-inspired computing. Artificial neural networks (ANNs) based on emerging devices, such as memristors, achieve brain-like computing but lack energy-efficiency. Furthermore, slow learning, incremental adaptation, and false convergence are unresolved challenges for ANNs. In this article we, therefore, introduce Gaussian synapses based on heterostructures of atomically thin two-dimensional (2D) layered materials, namely molybdenum disulfide and black phosphorus field effect transistors (FETs), as a class of analog and probabilistic computational primitives for hardware implementation of statistical neural networks. We also demonstrate complete tunability of amplitude, mean and standard deviation of the Gaussian synapse via threshold engineering in dual gated molybdenum disulfide and black phosphorus FETs. Finally, we show simulation results for classification of brainwaves using Gaussian synapse based probabilistic neural networks.