Healing LER using directed self assembly: treatment of EUVL resists with aqueous solutions of block copolymers

Overcoming the resolution-LER-sensitivity trade-off is a key challenge for the development of novel resists and processes that are able to achieve the ITRS targets for future lithography nodes. Here, we describe a process that treats lithographic patterns with aqueous solutions of block copolymers to facilitate a reduction in LER. A detailed understanding of parameters affecting adhesion and smoothing is gained by first investigating the behavior of the polymers on planar smooth and rough surfaces. Once healing was established in these model systems the methodology is tested on lithographically printed features where significant healing is observed, making this a promising technology for LER remediation

ImGeoNet: Image-induced Geometry-aware Voxel Representation for Multi-view 3D Object Detection

We propose ImGeoNet, a multi-view image-based 3D object detection framework that models a 3D space by an image-induced geometry-aware voxel representation. Unlike previous methods which aggregate 2D features into 3D voxels without considering geometry, ImGeoNet learns to induce geometry from multi-view images to alleviate the confusion arising from voxels of free space, and during the inference phase, only images from multiple views are required. Besides, a powerful pre-trained 2D feature extractor can be leveraged by our representation, leading to a more robust performance. To evaluate the effectiveness of ImGeoNet, we conduct quantitative and qualitative experiments on three indoor datasets, namely ARKitScenes, ScanNetV2, and ScanNet200. The results demonstrate that ImGeoNet outperforms the current state-of-the-art multi-view image-based method, ImVoxelNet, on all three datasets in terms of detection accuracy. In addition, ImGeoNet shows great data efficiency by achieving results comparable to ImVoxelNet with 100 views while utilizing only 40 views. Furthermore, our studies indicate that our proposed image-induced geometry-aware representation can enable image-based methods to attain superior detection accuracy than the seminal point cloud-based method, VoteNet, in two practical scenarios: (1) scenarios where point clouds are sparse and noisy, such as in ARKitScenes, and (2) scenarios involve diverse object classes, particularly classes of small objects, as in the case in ScanNet200.Comment: ICCV'23; project page: https://ttaoretw.github.io/imgeonet

Prevalence and molecular characterization of plasmidmediated beta-lactamase genes among nosocomial Staphylococcus aureus isolated in Taiwan

Purpose: To analyze the drug susceptibility phenotypes and the patterns of plasmid-mediated β- lactamase genes among nosocomial Staphylococcus aureus drug resistance isolates in Taiwan.Methods: The antibiotic susceptibilities of 617 clinical Staphylococcus aureus isolates collected from 2005 - 2009 from Chiayi Christian Hospital (Chiayi, Taiwan) were examined in vitro against 8 antimicrobial agents using agar diffusion method. Among the clinical isolates, 114 strains of methicillinsensitive Staphylococcus aureus and 45 strains of methicillin-resistant Staphylococcus aureus (MRSA) isolates were selected for plasmid profile analysis. The patterns of β-lactamase genes presented in plasmids were investigated by polymerase chain reaction analysis.Results: Most test strains were resistant to multiple antibiotics, particularly for the traditional agents such as ampicillin, penicillin, cephalexin and kanamycin. Plasmid profile analysis revealed that up to 36 % of the clinical strains harbored plasmids and were able to develop multi-drug resistant. Among them, most of the isolates harbored at least one plasmid (range 1 – 7) with a size range of 2.3 to 23 Kb. Among the several types of β-lactamases, blaTEM was the most prevalent.Conclusion: The results obtained from this study can serve as a valuable reference for the future control for clinical antibiotic resistant strains and more thorough discussions on resistance mechanisms.Keywords: Staphylococcus aureus, Antibiotic susceptibility, Nosocomial pathogens, Plasmid profile, β- lactamase

UNIT project: Universe NN-body simulations for the Investigation of Theoretical models from galaxy surveys

We present the UNIT $N$-body cosmological simulations project, designed to provide precise predictions for nonlinear statistics of the galaxy distribution. We focus on characterizing statistics relevant to emission line and luminous red galaxies in the current and upcoming generation of galaxy surveys. We use a suite of precise particle mesh simulations (FastPM) as well as with full $N$-body calculations with a mass resolution of $\sim 1.2\times10^9\,h^{-1}$M$_{\odot}$ to investigate the recently suggested technique of Angulo & Pontzen 2016 to suppress the variance of cosmological simulations We study redshift space distortions, cosmic voids, higher order statistics from $z=2$ down to $z=0$. We find that both two- and three-point statistics are unbiased. Over the scales of interest for baryon acoustic oscillations and redshift-space distortions, we find that the variance is greatly reduced in the two-point statistics and in the cross correlation between halos and cosmic voids, but is not reduced significantly for the three-point statistics. We demonstrate that the accuracy of the two-point correlation function for a galaxy survey with effective volume of 20 ($h^{-1}$Gpc)$^3$ is improved by about a factor of 40, indicating that two pairs of simulations with a volume of 1 ($h^{-1}$Gpc)$^3$ lead to the equivalent variance of $\sim$150 such simulations. The $N$-body simulations presented here thus provide an effective survey volume of about seven times the effective survey volume of DESI or Euclid. The data from this project, including dark matter fields, halo catalogues, and their clustering statistics, are publicly available at http://www.unitsims.org.Comment: 12 pages, 9 figures. This version matches the one accepted by MNRAS. The data from this project are publicly available at: http://www.unitsims.or

Assessing the Decision-Making Process in Human-Robot Collaboration Using a Lego-like EEG Headset

Human-robot collaboration (HRC) has become an emerging field, where the use of a robotic agent has been shifted from a supportive machine to a decision-making collaborator. A variety of factors can influence the effectiveness of decision-making processes during HRC, including the system-related (e.g., robot capability) and human-related (e.g., individual knowledgeability) factors. As a variety of contextual factors can significantly impact the human-robot decision-making process in collaborative contexts, the present study adopts a Lego-like EEG headset to collect and examine human brain activities and utilizes multiple questionnaires to evaluate participants’ cognitive perceptions toward the robot. A user study was conducted where two levels of robot capabilities (high vs. low) were manipulated to provide system recommendations. The participants were also identified into two groups based on their computational thinking (CT) ability. The EEG results revealed that different levels of CT abilities trigger different brainwaves, and the participants’ trust calibration of the robot also varies the resultant brain activities

Covariance matrices for variance-suppressed simulations

Cosmological $N$-body simulations provide numerical predictions of the structure of the universe against which to compare data from ongoing and future surveys. The growing volume of the surveyed universe, however, requires increasingly large simulations. It was recently proposed to reduce the variance in simulations by adopting fixed-amplitude initial conditions. This method has been demonstrated not to introduce bias in various statistics, including the two-point statistics of galaxy samples typically used for extracting cosmological parameters from galaxy redshift survey data. However, we must revisit current methods for estimating covariance matrices for these simulations to be sure that we can properly use them. In this work, we find that it is not trivial to construct the covariance matrix analytically, but we demonstrate that EZmock, the most efficient method for constructing mock catalogues with accurate two- and three-point statistics, provides reasonable covariance matrix estimates for variance-suppressed simulations. We further investigate the behavior of the variance suppression by varying galaxy bias, three-point statistics, and small-scale clustering.Comment: 9 pages, 7 figure