Projects

Deep Learning Enabled Optical Characterization of Nanomaterials (Area 1)

Characterization of nanomaterial morphologies with advanced microscopy and/or spectroscopy tools plays an indispensable role in nanoscience and nanotechnology research. However, the interpretation of imaging data heavily relies on the “intuition” of experienced researchers. As a result, many of the deep graphical features are often unused because of difficulties in processing the data and finding the correlations. Such difficulties can be well addressed by deep learning. In this work, we use the optical characterization of two-dimensional (2D) materials as a case study, and demonstrate a neural network based algorithm for the material and thickness identification of exfoliated 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can be used to predict physical properties of the materials. Finally, a transfer learning technique is applied to adapt the pretrained network to more optical characterization applications such as identifying layer numbers of chemically synthesized graphene domains. 

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Atomic Layer Substitution for Janus Monolayer Semiconductors and Their Heterostructures  (Area 1)

Manipulating materials with atomic-scale precision is essential for the development of next-generation material design toolbox. The family of 2D materials provides an ideal platform to realize atomic-level material architectures. We developed a novel atomic-scale material design tool that selectively breaks and forms chemical bonds of 2D materials at room temperature, called atomic-layer substitution (ALS), through which we can substitute the top layer chalcogen atoms within the 3-atom-thick transition-metal dichalcogenides using arbitrary patterns. Flipping the layer via transfer allows us to perform the same procedure on the other side, yielding programmable in-plane multi-heterostructures with different out-of-plane crystal symmetry and electric polarization. This electric dipole can be selectively patterned to be zero (MoS2, MoSe2), positive (MoSSe), and negative (MoSeS) on such a 2D material “canvas”, which would enable many novel nanostructures and devices with intriguing electrical and optoelectronic properties. 

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Synthesis of 2D Materials and Their Lateral Heterostructures (Area 1)

Diverse parallel stitched 2D heterostructures, including metal–semiconductor, semiconductor–semiconductor, and insulator–semiconductor, are synthesized directly through selective “sowing” of aromatic molecules as the seeds in the chemical vapor deposition (CVD) method. The methodology also enables the large‐scale fabrication of lateral heterostructures. 

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High-Performance Monolayer Transistors with Semimetallic Ohmic Contact (Area 2)

Atomically thin two-dimensional (2D) semiconductors have great potential for realizing ultimately scaled high-performance electronic devices. However, because of metal-induced gap states (MIGS), energy barriers at the metal-semiconductor interface, which fundamentally lead to high contact resistances and poor current-delivery capabilities, have restrained the advancement of 2D semiconductor transistors to date. In this project, we study a novel ohmic contact technology between semimetallic bismuth and semiconducting monolayer transition metal dichalcogenides (TMDs) where MIGS is sufficiently suppressed and degenerate states in the TMD are spontaneously formed in contact with bismuth. Through this approach, we achieve zero Schottky barrier height, a record-low contact resistance (RC ) of 123 Ω μm, and a record-high on-state current density (ION) of 1135 µA/µm on monolayer MoS2. We also demonstrate that excellent ohmic contacts can be formed on various monolayer semiconductors, including MoS2, WS2, and WSe2. Our reported RC values are a significant improvement for 2D semiconductors, and approaching the quantum limit. This technology unveils the full potential of high-performance monolayer transistors that are on par with the state-of-the-art 3D semiconductors, enabling further device down-scaling and extending Moore’s Law.

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An Ultrasensitive Thermo-Mechanical Mid-Infrared Detector (Area 2)

With nanoengineering, it is possible to fabricate nanometer-sized quantum tunneling barriers that can be tuned mechanically. Such a tremendous mechanical tunability can be harnessed for mechanical sensors and many other types of sensors with extremely high sensitivity. Here we demonstrate two nanostructures that implement such a mechanically tunable tunneling barrier and use them for either a mechanical/strain sensor or a mid-infrared bolometric detector. The first nanostructure is the self-assembled graphene nanoflake network composed of a resistance network of sub-micron graphene flakes that connect with each other with <100 nm overlap. The second nanostructure is a metal nano-gap with the gap defined by self-assembled monolayers (SAMs) . The proposed structures show high gauge factors and/or improved linear dynamic range as a strain sensor. Such mechanical sensors can also be integrated with a thermal actuator to realize a highly sensitive, uncooled bolometer-type mid-infrared detector . The measured temperature coefficient of resistance (TCR) can be as high as 5 K-1, which is more than one order of magnitude better than the state of the art.  

Light-Matter Interactions in 2D Lateral Heterostructures (Area 2)

A variety of unique light-matter interaction phenomena have been discovered in graphene, which promise many novel optoelectronic applications. Most of the effects are only accessible by breaking the spatial symmetry. The recent development of direct synthesis of lateral heterostructures offers new opportunities to achieve the desired asymmetry. As a proof of concept, we study the photothermoelectric effect in an asymmetric lateral heterojunction between the Dirac semimetallic monolayer graphene and the parabolic semiconducting monolayer MoS2. Very different hot-carrier cooling mechanisms on the graphene and the MoS2 sides allow us to resolve the asymmetric thermalization pathways of photoinduced hot carriers spatially with electrostatic gate tunability. We also demonstrate the potential of graphene-2D semiconductor lateral heterojunctions as broadband infrared photodetectors. The proposed structure shows an extreme in-plane asymmetry and provides a new platform to study light-matter interactions in low-dimensional systems. 

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Graphene-CMOS Monolithic Integration for Thermal Imaging Systems (Area 3)

The low dimensionality of 2D materials and the easy and universal transfer process make it possible to decouple the low-temperature fabrication process from the high-temperature growth of high-quality 2D materials and realize the monolithic integration between 2D material based technologies and conventional silicon CMOS technologies. As a proof-of-concept, we developed a back-end-of-line process to fabricate a graphene thermopile mid-infrared detector array directly onto a specifically designed silicon CMOS integrated circuit chip. Such a monolithically integrated graphene-CMOS system can perform real-time thermal imaging that can potentially be used in next generation night vision goggles, surveillance cameras as well self-driving systems.  

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