Electrically tunable giant Nernst effect in two-dimensional van der Waals heterostructures.

The Nernst effect, a transverse thermoelectric phenomenon, has attracted significant attention for its potential in energy conversion, thermoelectrics and spintronics. However, achieving high performance and versatility at low temperatures remains elusive. Here we demonstrate a large and electrically tunable Nernst effect by combining the electrical properties of graphene with the semiconducting characteristics of indium selenide in a field-effect geometry. Our results establish a new platform for exploring and manipulating this thermoelectric effect, showcasing the first electrical tunability with an on/off ratio of 103. Moreover, photovoltage measurements reveal a stronger photo-Nernst signal in the graphene/indium selenide heterostructure compared with individual components. Remarkably, we observe a record-high Nernst coefficient of 66.4 µV K-1 T-1 at ultralow temperatures and low magnetic fields, an important step towards applications in quantum information and low-temperature emergent phenomena.

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2.

Machine learning and signal processing on the edge are poised to influence our everyday lives with devices that will learn and infer from data generated by smart sensors and other devices for the Internet of Things. The next leap toward ubiquitous electronics requires increased energy efficiency of processors for specialized data-driven applications. Here, we show how an in-memory processor fabricated using a two-dimensional materials platform can potentially outperform its silicon counterparts in both standard and nontraditional Von Neumann architectures for artificial neural networks. We have fabricated a flash memory array with a two-dimensional channel using wafer-scale MoS2. Simulations and experiments show that the device can be scaled down to sub-micrometer channel length without any significant impact on its memory performance and that in simulation a reasonable memory window still exists at sub-50 nm channel lengths. Each device conductance in our circuit can be tuned with a 4-bit precision by closed-loop programming. Using our physical circuit, we demonstrate seven-segment digit display classification with a 91.5% accuracy with training performed ex situ and transferred from a host. Further simulations project that at a system level, the large memory arrays can perform AlexNet classification with an upper limit of 50 000 TOpS/W, potentially outperforming neural network integrated circuits based on double-poly CMOS technology.

Logic-in-memory based on an atomically thin semiconductor.

The growing importance of applications based on machine learning is driving the need to develop dedicated, energy-efficient electronic hardware. Compared with von Neumann architectures, which have separate processing and storage units, brain-inspired in-memory computing uses the same basic device structure for logic operations and data storage1-3, thus promising to reduce the energy cost of data-centred computing substantially4. Although there is ample research focused on exploring new device architectures, the engineering of material platforms suitable for such device designs remains a challenge. Two-dimensional materials5,6 such as semiconducting molybdenum disulphide, MoS2, could be promising candidates for such platforms thanks to their exceptional electrical and mechanical properties7-9. Here we report our exploration of large-area MoS2 as an active channel material for developing logic-in-memory devices and circuits based on floating-gate field-effect transistors (FGFETs). The conductance of our FGFETs can be precisely and continuously tuned, allowing us to use them as building blocks for reconfigurable logic circuits in which logic operations can be directly performed using the memory elements. After demonstrating a programmable NOR gate, we show that this design can be simply extended to implement more complex programmable logic and a functionally complete set of operations. Our findings highlight the potential of atomically thin semiconductors for the development of next-generation low-power electronics.

Self-sensing, tunable monolayer MoS2 nanoelectromechanical resonators.

Excellent mechanical properties and the presence of piezoresistivity make single layers of transition metal dichalcogenides (TMDCs) viable candidates for integration in nanoelectromechanical systems (NEMS). We report on the realization of electromechanical resonators based on single-layer MoS2 with both piezoresistive and capacitive transduction schemes. Operating in the ultimate limit of membrane thickness, the resonant frequency of MoS2 resonators is primarily defined by the built-in mechanical tension and is in the very high frequency range. Using electrostatic interaction with a gate electrode, we tune the resonant frequency, allowing for the extraction of resonator parameters such as mass density and built-in strain. Furthermore, we study the origins of nonlinear dynamic response at high driving force. The results shed light on the potential of TMDC-based NEMS for the investigation of nanoscale mechanical effects at the limits of vertical downscaling and applications such as resonators for RF-communications, force and mass sensors.