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Lateral Heterostructure Field-Effect Transistors Based on Two-Dimensional Material Stacks with Varying Thickness and Energy Filtering Source.

ABSTRACT: The bandgap dependence on the number of atomic layers of some families of two-dimensional (2D) materials can be exploited to engineer and use lateral heterostructures (LHs) as high-performance field-effect transistors (FETs). This option can provide very good lattice matching as well as high heterointerface quality. More importantly, this bandgap modulation with layer stacking can give rise to steep transitions in the density of states (DOS) of the 2D material that can eventually be used to achieve sub-60 mV/decade subthreshold swing in LH-FETs thanks to an energy-filtering source. We have observed this effect in the case of a PdS₂ LH-FET due to the particular DOS of its bilayer configuration. Our results are based on ab initio and multiscale materials and device modeling and incite the exploration of the 2D-material design space in order to find more abrupt DOS transitions and better suitable candidates.

SUBMITTER: Marin EG

PROVIDER: S-EPMC7993756 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Traditional histology is the gold standard for tissue studies, but it is intrinsically reliant on two-dimensional (2D) images. Study of volumetric tissue samples such as whole hearts produces a stack of misaligned and distorted 2D images that need to be reconstructed to recover a congruent volume with the original sample's shape. In this paper, we develop a mathematical framework called Transformation Diffusion (TD) for stack alignment refinement as a solution to the heat diffusion equation. This general framework does not require contour segmentation, is independent of the registration method used, and is trivially parallelizable. After the first stack sweep, we also replace registration operations by operations in the space of transformations, several orders of magnitude faster and less memory-consuming. Implementing TD with operations in the space of transformations produces our Transformation Diffusion Reconstruction (TDR) algorithm, applicable to general transformations that are closed under inversion and composition. In particular, we provide formulas for translation and affine transformations. We also propose an Approximated TDR (ATDR) algorithm that extends the same principles to tensor-product B-spline transformations. Using TDR and ATDR, we reconstruct a full mouse heart at pixel size 0.92µm×0.92µm, cut 10µm thick, spaced 20µm (84G). Our algorithms employ only local information from transformations between neighboring slices, but the TD framework allows theoretical analysis of the refinement as applying a global Gaussian low-pass filter to the unknown stack misalignments. We also show that reconstruction without an external reference produces large shape artifacts in a cardiac specimen while still optimizing slice-to-slice alignment. To overcome this problem, we use a pre-cutting blockface imaging process previously developed by our group that takes advantage of Brewster's angle and a polarizer to capture the outline of only the topmost layer of wax in the block containing embedded tissue for histological sectioning.

Dataset Information

Lateral Heterostructure Field-Effect Transistors Based on Two-Dimensional Material Stacks with Varying Thickness and Energy Filtering Source.

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