The synthesis of large, defect-free two-dimensional materials (2DMs) such as graphene is a major challenge toward industrial applications. Chemical vapor deposition (CVD) on liquid metal catalysts (LMCats) is a recently developed process for the fast synthesis of high-quality single crystals of 2DMs. However, up to now, the lack of in situ techniques enabling direct feedback on the growth has limited our understanding of the process dynamics and primarily led to empirical growth recipes. Thus, an in situ multiscale monitoring of the 2DMs structure, coupled with a real-time control of the growth parameters, is necessary for efficient synthesis. Here we report real-time monitoring of graphene growth on liquid copper (at 1370 K under atmospheric pressure CVD conditions) via four complementary in situ methods: synchrotron X-ray diffraction and reflectivity, Raman spectroscopy, and radiation-mode optical microscopy. This has allowed us to control graphene growth parameters such as shape, dispersion, and the hexagonal supra-organization with very high accuracy. Furthermore, the switch from continuous polycrystalline film to the growth of millimeter-sized defect-free single crystals could also be accomplished. The presented results have far-reaching consequences for studying and tailoring 2D material formation processes on LMCats under CVD growth conditions. Finally, the experimental observations are supported by multiscale modeling that has thrown light into the underlying mechanisms of graphene growth.

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The X-ray reflectivity technique can provide out-of-plane electron-density profiles of surfaces, interfaces, and thin films, with atomic resolution accuracy. While current methodologies require high surface flatness, this becomes challenging for naturally curved surfaces, particularly for liquid metals, due to the very high surface tension. Here, the development of X-ray reflectivity measurements with beam sizes of a few tens of micrometres on highly curved liquid surfaces using a synchrotron diffractometer equipped with a double crystal beam deflector is presented. The proposed and developed method, which uses a standard reflectivity θ–2θ scan, is successfully applied to study in situ the bare surface of molten copper and molten copper covered by a graphene layer grown in situ by chemical vapor deposition. It was found that the roughness of the bare liquid surface of copper at 1400 K is 1.25 ± 0.10 Å, while the graphene layer is separated from the liquid surface by a distance of 1.55 ± 0.08 Å and has a roughness of 1.26 ± 0.09 Å.

In this paper, we present a comprehensive study of the unique thermal behavior in monolithic 3D ICs. In particular, we study the impact of the thin inter-layer dielectric (ILD) between the device tiers on vertical thermal coupling. In addition, we develop a fast and accurate compact full-chip thermal analysis model based on non-linear regression technique. Our model is extremely fast and highly accurate with an error of less than 5%. This model is incorporated into a thermal-aware 3D-floorplanner that runs without significant runtime overhead. We observe up to 22% reduction in the maximum temperature with insignificant area and performance overhead.

Liquid metal catalysts have recently attracted attention for synthesizing high-quality 2D materials facilitated via the catalysts' perfectly smooth surface. However, the microscopic catalytic processes occurring at the surface are still largely unclear because liquid metals escape the accessibility of traditional experimental and computational surface science approaches. Hence, numerous controversies are found regarding different applications, with graphene (Gr) growth on liquid copper (Cu) as a prominent prototype. In this work, novel in situ and in silico techniques are employed to achieve an atomic-level characterization of the graphene adsorption height above liquid Cu, reaching quantitative agreement within 0.1 Å between experiment and theory. The results are obtained via in situ synchrotron X-ray reflectivity (XRR) measurements over wide-range q-vectors and large-scale molecular dynamics simulations based on efficient machine-learning (ML) potentials trained to first-principles density functional theory (DFT) data. The computational insight is demonstrated to be robust against inherent DFT errors and reveals the nature of graphene binding to be highly comparable at liquid Cu and solid Cu(111). Transporting the predictive first-principles quality via ML potentials to the scales required for liquid metal catalysis thus provides a powerful approach to reach microscopic understanding, analogous to the established computational approaches for catalysis at solid surfaces.