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1. Metal‐Guided Selective Growth of 2D Materials: Demonstration of a Bottom‐Up CMOS Inverter

   https://doi.org/10.1002/adma.201900861  

   Chiu, Ming‐Hui; Tang, Hao‐Ling; Tseng, Chien‐Chih; Han, Yimo; Aljarb, Areej; Huang, Jing‐Kai; Wan, Yi; Fu, Jui‐Han; Zhang, Xixiang; Chang, Wen‐Hao; et al (March 2019, Advanced Materials)

   Abstract 2D transition metal dichalcogenide (TMD) layered materials are promising for future electronic and optoelectronic applications. The realization of large‐area electronics and circuits strongly relies on wafer‐scale, selective growth of quality 2D TMDs. Here, a scalable method, namely, metal‐guided selective growth (MGSG), is reported. The success of control over the transition‐metal‐precursor vapor pressure, the first concurrent growth of two dissimilar monolayer TMDs, is demonstrated in conjunction with lateral or vertical TMD heterojunctions at precisely desired locations over the entire wafer in a single chemical vapor deposition (VCD) process. Owing to the location selectivity, MGSG allows the growth of p‐ and n‐type TMDs with spatial homogeneity and uniform electrical performance for circuit applications. As a demonstration, the first bottom‐up complementary metal‐oxide‐semiconductor inverter based on p‐type WSe₂and n‐type MoSe₂is achieved, which exhibits a high and reproducible voltage gain of 23 with little dependence on position. 

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2. Transfer learning for multi-material classification of transition metal dichalcogenides with atomic force microscopy

   https://doi.org/10.1088/2632-2153/ada2da  

   Moses, Isaiah_A; Reinhart, Wesley_F (January 2025, Machine Learning: Science and Technology)

   Abstract Deep learning models based on atomic force microscopy enhance efficiency in inverse design and characterization of materials. However, the limited and imbalanced data of experimental materials that are typically available is a major challenge. Also important is the need to interpret trained models, which are normally complex enough to be uninterpretable by humans. Here, we present a systemic evaluation of transfer learning strategies to accommodate low-data scenarios in materials synthesis and a model latent feature analysis to draw connections to the human-interpretable characteristics of the samples. While we imagine this framework can be used in downstream analysis tasks such as quantitative characterization, we demonstrate the strategies on a multi-material classification task for which the ground truth labels are readily available. Our models show accurate predictions in five classes of transition metal dichalcogenides (TMDs) (MoS₂, WS₂, WSe₂, MoSe₂, and Mo-WSe₂) with up to 89% accuracy on held-out test samples. Analysis of the latent features reveals a correlation with physical characteristics such as grain density, Difference of Gaussian blob, and local variation. The transfer learning optimization modality and the exploration of the correlation between the latent and physical features provide important frameworks that can be applied to other classes of materials beyond TMDs to enhance the models’ performance and explainability which can accelerate the inverse design of materials for technological applications. 

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3. Vapor–liquid assisted chemical vapor deposition of Cu ₂ X materials

   https://doi.org/10.1088/2053-1583/ac8435  

   Shehzad, M Arslan; Lee, Yea-Shine; Cheng, Matthew; Lebedev, Dmitry; Tyner, Alexander C; Das, Paul Masih; Gao, Zhangyuan; Goswami, Pallab; dos Reis, Roberto; Hersam, Mark C; et al (August 2022, 2D Materials)

   Abstract Transition metal dichalcogenides (TMDs) are known for their layered structure and tunable functional properties. However, a unified understanding on other transition metal chalcogenides (i.e. M 2 X) is still lacking. Here, the relatively new class of copper-based chalcogenides Cu 2 X (X = Te, Se, S) is thoroughly reported. Cu 2 X are synthesized by an unusual vapor–liquid assisted growth on a Al 2 O 3 /Cu/W stack. Liquid copper plays a significant role in synthesizing these layered systems, and sapphire assists with lateral growth and exfoliation. Similar to traditional TMDs, thickness dependent phonon signatures are observed, and high-resolution atomic images reveal the single phase Cu 2 Te that prefers to grow in lattice-matched layers. Charge transport measurements indicate a metallic nature at room temperature with a transition to a semiconducting nature at low temperatures accompanied by a phase transition, in agreement with band structure calculations. These findings establish a fundamental understanding and thrust Cu 2 Te as a flexible candidate for wide applications from photovoltaics and sensors to nanoelectronics. 

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4. Experimental observation of topological Z2 exciton-polaritons in transition metal dichalcogenide monolayers

   https://doi.org/10.1038/s41467-021-24728-y  

   Li, Mengyao; Sinev, Ivan; Benimetskiy, Fedor; Ivanova, Tatyana; Khestanova, Ekaterina; Kiriushechkina, Svetlana; Vakulenko, Anton; Guddala, Sriram; Skolnick, Maurice; Menon, Vinod M.; et al (July 2021, Nature Communications)

   Abstract The rise of quantum science and technologies motivates photonics research to seek new platforms with strong light-matter interactions to facilitate quantum behaviors at moderate light intensities. Topological polaritons (TPs) offer an ideal platform in this context, with unique properties stemming from resilient topological states of light strongly coupled with matter. Here we explore polaritonic metasurfaces based on 2D transition metal dichalcogenides (TMDs) as a promising platform for topological polaritonics. We show that the strong coupling between topological photonic modes of the metasurface and excitons in TMDs yields a topological polaritonic Z₂phase. We experimentally confirm the emergence of one-way spin-polarized edge TPs in metasurfaces integrating MoSe₂and WSe₂. Combined with the valley polarization in TMD monolayers, the proposed system enables an approach to engage the photonic angular momentum and valley and spin of excitons, offering a promising platform for photonic/solid-state interfaces for valleytronics and spintronics. 

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5. Imaging dynamic exciton interactions and coupling in transition metal dichalcogenides

   https://doi.org/10.1063/5.0087544  

   Purz, Torben L.; Martin, Eric W.; Holtzmann, William G.; Rivera, Pasqual; Alfrey, Adam; Bates, Kelsey M.; Deng, Hui; Xu, Xiaodong; Cundiff, Steven T. (June 2022, The Journal of Chemical Physics)

   Transition metal dichalcogenides (TMDs) are regarded as a possible material platform for quantum information science and related device applications. In TMD monolayers, the dephasing time and inhomogeneity are crucial parameters for any quantum information application. In TMD heterostructures, coupling strength and interlayer exciton lifetimes are also parameters of interest. However, many demonstrations in TMDs can only be realized at specific spots on the sample, presenting a challenge to the scalability of these applications. Here, using multi-dimensional coherent imaging spectroscopy, we shed light on the underlying physics—including dephasing, inhomogeneity, and strain—for a MoSe 2 monolayer and identify both promising and unfavorable areas for quantum information applications. We, furthermore, apply the same technique to a MoSe 2 /WSe 2 heterostructure. Despite the notable presence of strain and dielectric environment changes, coherent and incoherent coupling and interlayer exciton lifetimes are mostly robust across the sample. This uniformity is despite a significantly inhomogeneous interlayer exciton photoluminescence distribution that suggests a bad sample for device applications. This robustness strengthens the case for TMDs as a next-generation material platform in quantum information science and beyond. 

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