Searcharxiv⌕ Search

arXiv subjects

Jaroslaw Pawlowski

Publications and source records attributed to Jaroslaw Pawlowski.

2 recordsLinked to original sources

Classical to Quantum Diffusive Transport in Atomically Thin Semiconductors Capped with High-k Dielectric

The dielectric environment surrounding semiconductors plays a crucial role in determining device performance, a role that becomes especially pronounced in atomically thin semiconductors where charge carriers are confined within a few atomic layers and strongly interact with their surroundings. High-k dielectrics, such as hafnium oxide (HfO2), have been shown to enhance the performance of two-dimensional (2D) materials by suppressing scattering from charged impurities and phonons, but most studies to date have focused on room-temperature transistor operation. Their influence on quantum transport properties at low temperatures remains largely unexplored. In this work, we investigate how capping monolayer molybdenum disulfide (MoS2) with HfO2 modifies its electronic behavior in the quantum regime. By comparing devices with and without HfO2 capping, we find that uncapped devices exhibit transport dominated by classical diffusive scattering, whereas capped devices show clear Fabry-Perot interference patterns, providing direct evidence of phase-coherent quantum transport enabled by dielectric screening. To gain further insight, we develop a tight-binding interferometer model that captures the effect of dielectric screening on conductive modes and reproduces the experimental trends. Our findings demonstrate that dielectric engineering provides a powerful route to control transport regimes in TMD devices.

cond-mat.mes-hall↗

Self-Normalized Density Map (SNDM) for Counting Microbiological Objects

The statistical properties of the density map (DM) approach to counting microbiological objects on images are studied in detail. The DM is given by U$^2$-Net. Two statistical methods for deep neural networks are utilized: the bootstrap and the Monte Carlo (MC) dropout. The detailed analysis of the uncertainties for the DM predictions leads to a deeper understanding of the DM model's deficiencies. Based on our investigation, we propose a self-normalization module in the network. The improved network model, called \textit{Self-Normalized Density Map} (SNDM), can correct its output density map by itself to accurately predict the total number of objects in the image. The SNDM architecture outperforms the original model. Moreover, both statistical frameworks -- bootstrap and MC dropout -- have consistent statistical results for SNDM, which were not observed in the original model. The SNDM efficiency is comparable with the detector-base models, such as Faster and Cascade R-CNN detectors.

cs.CV↗