Background Autism spectrum disorder (ASD) is characterized by abnormalities in social communication and limited and repetitive behavioral patterns. Children with ASD who lack social communication skills will eventually not interact with others and will lack peer relationships when compared to ordinary people. Thus, it is necessary to develop a program to improve social communication abilities using digital technology in people with ASD. Objective We intend to develop and apply a metaverse-based child social skills training program aimed at improving the social interaction abilities of children with ASD aged 7-12 years. We plan to compare and analyze the biometric information collected through wearable devices when applying the metaverse-based social skills training program to evaluate emotional changes in children with ASD in stressful situations. Methods This parallel randomized controlled study will be conducted on children aged 7-12 years diagnosed with ASD. A metaverse-based social skills training program using digital technology will be administered to children who voluntarily wish to participate in the research with consent from their legal guardians. The treatment group will participate in the metaverse-based social skills training program developed by this research team once a week for 60 minutes per session for 4 weeks. The control group will not intervene during the experiment. The treatment group will use wearable devices during the experiment to collect real-time biometric information. Results The study is expected to recruit and enroll participants in March 2022. After registering the participants, the study will be conducted from March 2022 to May 2022. This research will be jointly conducted by Yonsei University and Dobrain Co Ltd. Children participating in the program will use the internet-based platform. Conclusions The metaverse-based Program for the Education and Enrichment of Relational Skills (PEERS) will be effective in improving the social skills of children with ASD, similar to the offline PEERS program. The metaverse-based PEERS program offers excellent accessibility and is inexpensive because it can be administered at home; thus, it is expected to be effective in many children with ASD. If a method can be applied to detect children's emotional changes early using biometric information collected through wearable devices, then emotional changes such as anxiety and anger can be alleviated in advance, thus reducing issues in children with ASD. Trial Registration Clinical Research Information Service KCT0006859; https://tinyurl.com/4r3k7cmj International Registered Report Identifier (IRRID) PRR1-10.2196/35960

In this study, we develop a reactive force field (ReaxFF) for a Si/O/H/F system to perform etching simulations of SiO2 with an HF etchant. Quantum mechanical (QM) training sets from density functional theory calculations, which contain structures of reactant/product and energies with bond dissociation, valence angle distortions, and reactions between SiO2 clusters and SiO2 slab with HF gases, are used to optimize the ReaxFF parameters. Structures and energies calculated using the ReaxFF match well with the QM training sets. Using the optimized ReaxFF, we conduct molecular dynamics simulations of the etching process of SiO2 substrates with active HF molecules. The etching yield and number of reaction products with different incident energies of the HF etchant are investigated. These simulations show that the developed ReaxFF offers insights into the atomistic surface reaction of the SiO2 etching process.

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With the advent of complex and sophisticated architectures in semiconductor device manufacturing, atomic-resolution accuracy and precision are commonly required for industrial plasma processing. This demands a comprehensive understanding of the plasma–material interactions—particularly for forming fine high-aspect ratio (HAR) feature patterns with sufficiently high yield in wafer-level processes. In particular, because the shape distortion in HAR pattern etching is attributed to the deviation of the energetic ion trajectory, the detailed ion–surface interactions need to be thoroughly investigated. In this study, molecular dynamics (MD) simulations were utilized to obtain a fundamental understanding of the collisional nature of accelerated Ar ions on the fluorinated Si surface that may appear on the sidewall of the HAR etched hole. High-fidelity data for ion–surface interaction features representing the energy and angle distributions (EADs) of sputtered atoms for varying degrees of surface F coverage and ion incident angles were obtained via extensive MD simulations. A deep learning-based reduced-order modeling (DL-ROM) framework was developed for efficiently predicting the characteristics of the ion–surface interactions. In the ROM framework, a conditional variational autoencoder (CVAE) was implemented to obtain regularized latent representations of the distributional data with the condition of the governing factors of the physical system. The proposed ROM framework accurately reproduced the MD simulation results and significantly outperformed various DL-ROMs, such as autoencoder (AE), sparse AE (SAE), contractive AE (CAE), denoising AE (DAE), and variational AE (VAE). From the inferred features of the sputtering yield and EADs of sputtered/scattered species, significant insights can be obtained regarding the ion interactions with the fluorinated surface. As the ion incident angle deviated from the glancing-angle range (incident angle > 80°), diffuse reflection behavior was observed, which can substantially affect the ion transport in the HAR patterns. Moreover, it was hypothesized that a shift in sputtering characteristics occurs as the surface F coverage varies, based on the inferred EADs. This conjecture was confirmed through detailed MD simulations that demonstrated the fundamental relationship between surface atomic conformations and their sputtering behavior. Combined with additional atomistic-scale investigations, this framework can provide an efficient way to reveal various fundamental plasma–material interactions which are highly demanded for the future development of semiconductor device manufacturing.

As semiconductor device structures become more complex and sophisticated, the formation of finer and deeper patterns is required. To achieve a higher yield for mass production as the number of process steps increases and process variables become more diverse, process optimization requires extensive engineering effort to meet the target process requirements, such as uniformity. In this study, we propose an efficient process design framework that can efficiently search for optimal process conditions by combining deep learning (DL) with plasma simulations. To establish the DL model, a dataset was created using a two-dimensional (2D) hybrid plasma equipment model (HPEM) code for an argon inductively coupled plasma (ICP) system under a given process window. The DL model was implemented and trained using the dataset to learn the functional relationship between the process conditions and their consequential plasma states, which was characterized by 2D field data. The performance of the DL model was confirmed by comparison of the output with the ground truth, validating its high consistency. Moreover, the DL results provide a reasonable interpretation of the fundamental features of plasmas and show a good correlation with the experimental observations in terms of the measured etch rate characteristics. Using the designed DL, an extensive exploration of process variables was conducted to find the optimal processing condition using the multi-objective particle swarm optimization (MOPSO) algorithm for the given objective functions of high etch rate and its uniform distribution. The obtained optimal candidates were evaluated and compared to other process conditions experimentally, demonstrating a fairly enhanced etch rate and uniformity at the same time. The proposed computational framework substantially reduced trial-and-error repetitions in tailoring process conditions from a practical perspective. Moreover, it will serve as an effective tool to narrow the processing window, particularly in the early stages of development for advanced equipment and processes.