arXiv subjects

Hyunsung Cho

Publications and source records attributed to Hyunsung Cho.

9 recordsLinked to original sources

arXiv2026

Atomic-Scale Mechanisms of SiO$_2$ Plasma-Enhanced Chemical Vapor Deposition Revealed by Molecular Dynamics with a Machine-Learning Interatomic Potential

Plasma-enhanced chemical vapor deposition (PECVD) of silicon dioxide (SiO$_2$) is widely used for low-temperature fabrication of dielectric thin films, yet its atomic-scale growth mechanisms remain incompletely understood. In this work, we investigate SiO$_2$ PECVD using silane and N$_2$O as source gases via molecular dynamics simulations driven by a machine-learning interatomic potential. By systematically varying the oxidant-to-silane-derived species ratio $r$, we elucidate the evolution of film stoichiometry, density, and hydrogen content. Formation of the Si-O-Si network primarily proceeds via oxidation of surface Si-H groups to form Si-OH species, followed by condensation of neighboring Si-OH groups that produces H$_2$O as the dominant byproduct. At low $r$, H$_2$ formation via reactions between Si-H and Si-OH groups also contributes to the network formation. Increasing oxidant supply promotes the network formation through oxidation of residual Si-H species, suppressing hydrogen incorporation and leading to saturation of the Si/O ratio. Rapid chemisorption of silane-derived species, together with steric hindrance from pre-deposited species, results in localized growth and surface roughness. We further show that high-kinetic-energy plasma species can etch SiO$_2$ films, which potentially limits growth rates and enhances surface roughness under high RF-power conditions. These results provide atomic-scale insight into PECVD growth and guidance for optimizing film composition and quality.

cond-mat.mtrl-sci↗

arXiv2026

Simulating Human Audiovisual Search Behavior

Locating a target based on auditory and visual cues$\unicode{x2013}$such as finding a car in a crowded parking lot or identifying a speaker in a virtual meeting$\unicode{x2013}$requires balancing effort, time, and accuracy under uncertainty. Existing models of audiovisual search often treat perception and action in isolation, overlooking how people adaptively coordinate movement and sensory strategies. We present Sensonaut, a computational model of embodied audiovisual search. The core assumption is that people deploy their body and sensory systems in ways they believe will most efficiently improve their chances of locating a target, trading off time and effort under perceptual constraints. Our model formulates this as a resource-rational decision-making problem under partial observability. We validate the model against newly collected human data, showing that it reproduces both adaptive scaling of search time and effort under task complexity, occlusion, and distraction, and characteristic human errors. Our simulation of human-like resource-rational search informs the design of audiovisual interfaces that minimize search cost and cognitive load.

cs.HC↗

arXiv2025

Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance

Understanding hydrogen diffusion is critical for improving the reliability and performance of oxide thin-film transistors (TFTs), where hydrogen plays a key role in carrier modulation and bias instability. In this work, we investigate hydrogen diffusion in amorphous IGZO ($a$-IGZO) and $c$-axis aligned crystalline IGZO (CAAC-IGZO) using machine learning interatomic potential molecular dynamics (MLIP-MD) simulations. We construct accurate phase-specific MLIPs by fine-tuning SevenNet-0, a universal pretrained MLIP, and validate the models against a comprehensive dataset covering hydrogen-related configurations and diffusion environments. Hydrogen diffusivity is evaluated over 650--1700 K, revealing enhanced mobility above 750 K in $a$-IGZO due to the glassy matrix, while diffusion at lower temperatures is constrained by the rigid network. Arrhenius extrapolation of the diffusivity indicates that hydrogen in $a$-IGZO can reach the channel/insulator interface within $10^{4}$ seconds at 300--400 K, likely contributing to negative bias stress-induced device degradation. Trajectory analysis reveals that long-range diffusion in $a$-IGZO is enabled by a combination of hydrogen hopping and flipping mechanisms. In CAAC-IGZO, hydrogen exhibits high in-plane diffusivity but severely restricted out-of-plane transport due to a high energy barrier along the $c$-axis. This limited vertical diffusion in CAAC-IGZO suggests minimal impact on bias instability. This work bridges the atomic-level hydrogen transport mechanism and device-level performance in oxide TFTs by leveraging large-scale MLIP-MD simulations.

cond-mat.mtrl-sci↗

arXiv2025

Augmented Reality Productivity In-the-Wild: A Diary Study of Usage Patterns and Experiences of Working With AR Laptops in Real-World Settings

Augmented Reality (AR) is increasingly positioned as a tool for knowledge work, providing beneficial affordances such as a virtually limitless display space that integrates digital information with the user's physical surroundings. However, for AR to supplant traditional screen-based devices in knowledge work, it must support prolonged usage across diverse contexts. Until now, few studies have explored the effects, opportunities, and challenges of working in AR outside a controlled laboratory setting and for an extended duration. This gap in research limits our understanding of how users may adapt its affordances to their daily workflows and what barriers hinder its adoption. In this paper, we present findings from a longitudinal diary study examining how participants incorporated an AR laptop -- Sightful's Spacetop EA -- into their daily work routines. 14 participants used the device for 40-minute daily sessions over two weeks, collectively completing 103 hours of AR-based work. Through survey responses, workspace photographs, and post-study interviews, we analyzed usage patterns, workspace configurations, and evolving user perceptions. Our findings reveal key factors influencing participants' usage of AR, including task demands, environmental constraints, social dynamics, and ergonomic considerations. We highlight how participants leveraged and configured AR's virtual display space, along with emergent hybrid workflows that involved physical screens and tasks. Based on our results, we discuss both overlaps with current literature and new considerations and challenges for the future design of AR systems for pervasive and productive use.

cs.HC↗

arXiv2025

Social Media Should Feel Like Minecraft, Not Instagram: Youth Visions for Meaningful Social Connections through Fictional Inquiry

We conducted co-design workshops with 23 participants (ages 15--24) to explore how youth envision ideal remote social connection. Using Fictional Inquiry (FI) within a Harry Potter-inspired narrative, we found that youth perceive a disconnect between platforms labeled ``social media'' (like Instagram) and those where they actually experience meaningful connections (like Minecraft or Discord). Participants envisioned an immersive platform prioritizing meaningful social connection through presence and immersion, natural self-expression, intuitive spatial navigation leveraging physical-world norms, and playful, low-stakes opportunities for friendship development. We synthesize these visions into six themes articulating relational needs that current platforms systematically marginalize. The FI method proved effective in generating innovative ideas while empowering youth by fostering hope and agency over social media's future. Our findings challenge ``doom'' narratives by reframing social media's harms as outcomes of specific design choices, demonstrating how design research can reopen space for imagining more supportive forms of mediated connection.

cs.HC↗

arXiv2024

SonoHaptics: An Audio-Haptic Cursor for Gaze-Based Object Selection in XR

We introduce SonoHaptics, an audio-haptic cursor for gaze-based 3D object selection. SonoHaptics addresses challenges around providing accurate visual feedback during gaze-based selection in Extended Reality (XR), e.g., lack of world-locked displays in no- or limited-display smart glasses and visual inconsistencies. To enable users to distinguish objects without visual feedback, SonoHaptics employs the concept of cross-modal correspondence in human perception to map visual features of objects (color, size, position, material) to audio-haptic properties (pitch, amplitude, direction, timbre). We contribute data-driven models for determining cross-modal mappings of visual features to audio and haptic features, and a computational approach to automatically generate audio-haptic feedback for objects in the user's environment. SonoHaptics provides global feedback that is unique to each object in the scene, and local feedback to amplify differences between nearby objects. Our comparative evaluation shows that SonoHaptics enables accurate object identification and selection in a cluttered scene without visual feedback.

cs.HC↗

arXiv2024

Auptimize: Optimal Placement of Spatial Audio Cues for Extended Reality

Spatial audio in Extended Reality (XR) provides users with better awareness of where virtual elements are placed, and efficiently guides them to events such as notifications, system alerts from different windows, or approaching avatars. Humans, however, are inaccurate in localizing sound cues, especially with multiple sources due to limitations in human auditory perception such as angular discrimination error and front-back confusion. This decreases the efficiency of XR interfaces because users misidentify from which XR element a sound is coming. To address this, we propose Auptimize, a novel computational approach for placing XR sound sources, which mitigates such localization errors by utilizing the ventriloquist effect. Auptimize disentangles the sound source locations from the visual elements and relocates the sound sources to optimal positions for unambiguous identification of sound cues, avoiding errors due to inter-source proximity and front-back confusion. Our evaluation shows that Auptimize decreases spatial audio-based source identification errors compared to playing sound cues at the paired visual-sound locations. We demonstrate the applicability of Auptimize for diverse spatial audio-based interactive XR scenarios.

cs.HC↗

arXiv2024

MineXR: Mining Personalized Extended Reality Interfaces

Extended Reality (XR) interfaces offer engaging user experiences, but their effective design requires a nuanced understanding of user behavior and preferences. This knowledge is challenging to obtain without the widespread adoption of XR devices. We introduce MineXR, a design mining workflow and data analysis platform for collecting and analyzing personalized XR user interaction and experience data. MineXR enables elicitation of personalized interfaces from participants of a data collection: for any particular context, participants create interface elements using application screenshots from their own smartphone, place them in the environment, and simultaneously preview the resulting XR layout on a headset. Using MineXR, we contribute a dataset of personalized XR interfaces collected from 31 participants, consisting of 695 XR widgets created from 178 unique applications. We provide insights for XR widget functionalities, categories, clusters, UI element types, and placement. Our open-source tools and data support researchers and designers in developing future XR interfaces.

cs.HC↗

arXiv2022

FLAME: Federated Learning Across Multi-device Environments

Federated Learning (FL) enables distributed training of machine learning models while keeping personal data on user devices private. While we witness increasing applications of FL in the area of mobile sensing, such as human activity recognition (HAR), FL has not been studied in the context of a multi-device environment (MDE), wherein each user owns multiple data-producing devices. With the proliferation of mobile and wearable devices, MDEs are increasingly becoming popular in ubicomp settings, therefore necessitating the study of FL in them. FL in MDEs is characterized by being not independent and identically distributed (non-IID) across clients, complicated by the presence of both user and device heterogeneities. Further, ensuring efficient utilization of system resources on FL clients in a MDE remains an important challenge. In this paper, we propose FLAME, a user-centered FL training approach to counter statistical and system heterogeneity in MDEs, and bring consistency in inference performance across devices. FLAME features (i) user-centered FL training utilizing the time alignment across devices from the same user; (ii) accuracy- and efficiency-aware device selection; and (iii) model personalization to devices. We also present an FL evaluation testbed with realistic energy drain and network bandwidth profiles, and a novel class-based data partitioning scheme to extend existing HAR datasets to a federated setup. Our experiment results on three multi-device HAR datasets show that FLAME outperforms various baselines by 4.3-25.8% higher F1 score, 1.02-2.86x greater energy efficiency, and up to 2.06x speedup in convergence to target accuracy through fair distribution of the FL workload.

cs.LG↗