Abstract: PECVD of a nanoscale SiO2 capping layer using low pressure SiCl4/O-2/Ar plasmas is numerically investigated. The purpose of this capping layer is to restore photoresist profiles with improved line edge roughness. A 2D plasma and Monte Carlo feature profile model are applied for this purpose. The deposited films are calculated for various operating conditions to obtain a layer with desired shape. An increase in pressure results in more isotropic deposition with a higher deposition rate, while a higher power creates a more anisotropic process. Dilution of the gas mixture with Ar does not result in an identical capping layer shape with a thickness linearly correlated to the dilution. Finally, a substrate bias seems to allow proper control of the vertical deposition rate versus sidewall deposition as desired.

Abstract: We calculate the contact resistance for an edge- and top-contacted 2D semiconductor. The contact region consists of a metal contacting a monolayer of MoS2 which is otherwise surrounded by SiO2. We use the quantum transmitting boundary method to compute the contact resistance as a function of the 2D semiconductor doping concentration. An effective mass Hamiltonian is used to describe the properties of the various materials. The electrostatic potentials are obtained by solving the Poisson equation numerically. We incorporate the effects of the image-force barrier lowering on the Schottky barrier and examine the impact on the contact resistance. At low doping concentrations, the contact resistance of the top contact is lower compared to edge contact, while at high doping concentrations, the edge contact exhibits lower resistance.

Abstract: Plasma medicine has attracted tremendous interest in a variety of medical conditions, ranging from wound healing to antimicrobial applications, even in cancer treatment, through the interactions of cold atmospheric plasma (CAP) and various biological tissues directly or indirectly. The underlying mechanisms of CAP treatment are still poorly understood although the oxidative effects of CAP with amino acids, peptides, and proteins have been explored experimentally. In this study, machine learning (ML) technology is introduced to efficiently unveil the interaction mechanisms of amino acids and reactive oxygen species (ROS) in seconds based on the data obtained from the reactive molecular dynamics (MD) simulations, which are performed to probe the interaction of five types of amino acids with various ROS on the timescale of hundreds of picoseconds but with the huge computational load of several days. The oxidative reactions typically start with H-abstraction, and the details of the breaking and formation of chemical bonds are revealed; the modification types, such as nitrosylation, hydroxylation, and carbonylation, can be observed. The dose effects of ROS are also investigated by varying the number of ROS in the simulation box, indicating agreement with the experimental observation. To overcome the limits of timescales and the size of molecular systems in reactive MD simulations, a deep neural network (DNN) with five hidden layers is constructed according to the reaction data and employed to predict the type of oxidative modification and the probability of occurrence only in seconds as the dose of ROS varies. The well-trained DNN can effectively and accurately predict the oxidative processes and productions, which greatly improves the computational efficiency by almost ten orders of magnitude compared with the reactive MD simulation. This study shows the great potential of ML technology to efficiently unveil the underpinning mechanisms in plasma medicine based on the data from reactive MD simulations or experimental measurements. In this study, since reactive molecular dynamics simulation can currently only describe interactions between a few hundred atoms in a few hundred picoseconds, deep neural networks (DNN) are introduced to enhance the simulation results by predicting more data efficiently. image

Abstract: Cold atmospheric plasma (CAP) is a promising technology for several medical applications, including the removal of biofilms from surfaces. However, the molecular mechanisms of CAP treatment are still poorly understood. Here we unravel the possible mechanisms of CAP‐induced oxidation of oligosaccharides, employing reactive molecular dynamics simulations based on the density functional‐tight binding potential. Specifically, we find that the interaction of oxygen atoms (used as CAP‐generated reactive species) with cellotriose (a model system for the oligosaccharides) can break structurally important glycosidic bonds, which subsequently leads to the disruption of the oligosaccharide molecule. The overall results help to shed light on our experimental evidence for cellotriose CAP. This oxidation by study provides atomic‐level insight into the onset of plasma‐induced removal of biofilms, as oligosaccharides are one of the main components of biofilm.