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Abstract Synaptic devices with tunable weight hold great promise in enabling non-von Neumann architecture for energy efficient computing. However, conventional metal-insulator-metal based two-terminal memristors share the same physical channel for both programming and reading, therefore the programming power consumption is dependent on the synaptic resistance states and can be particularly high when the memristor is in the low resistance states. Three terminal synaptic transistors, on the other hand, allow synchronous programming and reading and have been shown to possess excellent reliability. Here we present a binary oxide based three-terminal MoS₂synaptic device, in which the channel conductance can be modulated by interfacial charges generated at the oxide interface driven by Maxwell-Wagner instability. The binary oxide stack serves both as an interfacial charge host and gate dielectrics. Both excitatory and inhibitory behaviors are experimentally realized, and the presynaptic potential polarity can be effectively controlled by engineering the oxide stacking sequence, which is a unique feature compared with existing charge-trap based synaptic devices and provides a new tuning knob for controlling synaptic device characteristics. By adopting a three-terminal transistor structure, the programming channel and reading channel are physically separated and the programming power consumption can be kept constantly low (∼50 pW) across a wide dynamic range of 10⁵. This work demonstrates a complementary metal oxide semiconductor compatible approach to build power efficient synaptic devices for artificial intelligence applications. 

Abstract Optical spectroscopy is indispensable for research and development in nanoscience and nanotechnology, microelectronics, energy, and advanced manufacturing. Advanced optical spectroscopy tools often require both specifically designed high-end instrumentation and intricate data analysis techniques. Beyond the common analytical tools, deep learning methods are well suited for interpreting high-dimensional and complicated spectroscopy data. They offer great opportunities to extract subtle and deep information about optical properties of materials with simpler optical setups, which would otherwise require sophisticated instrumentation. In this work, we propose a computational approach based on a conventional tabletop optical microscope and a deep learning model called ReflectoNet . Without any prior knowledge about the multilayer substrates, ReflectoNet can predict the complex refractive indices of thin films and 2D materials on top of these nontrivial substrates from experimentally measured optical reflectance spectra with high accuracies. This task was not feasible previously with traditional reflectometry or ellipsometry methods. Fundamental physical principles, such as the Kramers–Kronig relations, are spontaneously learned by the model without any further training. This approach enables in-operando optical characterization of functional materials and 2D materials within complex photonic structures or optoelectronic devices. 

Graphene nanoribbons (GNRs), when synthesized with atomic precision by bottom–up chemical approaches, possess tunable electronic structure, and high theoretical mobility, conductivity, and heat dissipation capabilities, which makes them an excellent candidate for channel material in post-silicon transistors. Despite their immense potential, achieving highly transparent contacts for efficient charge transport—which requires proper contact selection and a deep understanding of the complex one-dimensional GNR channel-three-dimensional metal contact interface—remains a challenge. In this study, we investigated the impact of different electron-beam deposited contact metals—the commonly used palladium (Pd) and softer metal indium (In)—on the structural properties and field-effect transistor performance of semiconducting nine-atom wide armchair GNRs. The performance and integrity of the GNR channel material were studied by means of a comprehensive Raman spectroscopy analysis, scanning tunneling microscopy (STM) imaging, optical absorption calculations, and transport measurements. We found that, compared to Pd, In contacts facilitate favorable Ohmic-like transport because of the reduction of interface defects, while the edge structure quality of GNR channel plays a more dominant role in determining the overall device performance. Our study provides a blueprint for improving device performance through contact engineering and material quality enhancements in emerging GNR-based technology. 

Technology advancements in history have often been propelled by material innovations. In recent years, two-dimensional (2D) materials have attracted substantial interest as an ideal platform to construct atomic-level material architectures. In this work, we design a reaction pathway steered in a very different energy landscape, in contrast to typical thermal chemical vapor deposition method in high temperature, to enable room-temperature atomic-layer substitution (RT-ALS). First-principle calculations elucidate how the RT-ALS process is overall exothermic in energy and only has a small reaction barrier, facilitating the reaction to occur at room temperature. As a result, a variety of Janus monolayer transition metal dichalcogenides with vertical dipole could be universally realized. In particular, the RT-ALS strategy can be combined with lithography and flip-transfer to enable programmable in-plane multiheterostructures with different out-of-plane crystal symmetry and electric polarization. Various characterizations have confirmed the fidelity of the precise single atomic layer conversion. Our approach for designing an artificial 2D landscape at selective locations of a single layer of atoms can lead to unique electronic, photonic, and mechanical properties previously not found in nature. This opens a new paradigm for future material design, enabling structures and properties for unexplored territories.