arXiv subjects

Minji Hong

Publications and source records attributed to Minji Hong.

3 recordsLinked to original sources

arXiv2026

Shallow Trap States Control Electrical Performance of Amorphous Oxide Semiconductor Thin-Film Transistors

The performance of n-type amorphous oxide semiconductor thin-film transistors (TFTs) is largely controlled by the density of states (DoS) near the conduction band mobility edge. Here, the full subgap DoS of amorphous InGaZnO (a-IGZO) TFTs, used in display panels and dynamic random-access memory (DRAM) development, is measured by ultrabroadband photoconduction (UP-DoS) microscopy to within 0.1 eV of the mobility edge. The measured subgap DoS for 25 TFT processing conditions accurately predicts each transfer curve, showing how shallow defect states are electron traps that rigidly tune subthreshold swing, threshold voltage and drift mobility. For a set of TFTs, the subthreshold transfer characteristics can be independently simulated from the experimental shallow defect DoS, with no adjustable parameters. The full transfer curve is simulated by introducing a single parameter: the conduction band tail energy. Additionally, the simulation reveals that the shallow trap density controlling subthreshold behavior can be directly extracted from transfer curves. Finally, a systematic In-enrichment study, combined with DFT+U DoS simulations, enables identification of vacancy cation coordination environments for all experimentally observed subgap peaks. The dominant trap controlling conventional a-IGZO TFT performance is centered at ~0.32 eV below the conduction band mobility edge and is assigned to a Ga-Ga-In oxygen vacancy defect.

physics.app-ph↗

arXiv2025

Chunk Knowledge Generation Model for Enhanced Information Retrieval: A Multi-task Learning Approach

Traditional query expansion techniques for addressing vocabulary mismatch problems in information retrieval are context-sensitive and may lead to performance degradation. As an alternative, document expansion research has gained attention, but existing methods such as Doc2Query have limitations including excessive preprocessing costs, increased index size, and reliability issues with generated content. To mitigate these problems and seek more structured and efficient alternatives, this study proposes a method that divides documents into chunk units and generates textual data for each chunk to simultaneously improve retrieval efficiency and accuracy. The proposed "Chunk Knowledge Generation Model" adopts a T5-based multi-task learning structure that simultaneously generates titles and candidate questions from each document chunk while extracting keywords from user queries. This approach maximizes computational efficiency by generating and extracting three types of semantic information in parallel through a single encoding and two decoding processes. The generated data is utilized as additional information in the retrieval system. GPT-based evaluation on 305 query-document pairs showed that retrieval using the proposed model achieved 95.41% accuracy at Top@10, demonstrating superior performance compared to document chunk-level retrieval. This study contributes by proposing an approach that simultaneously generates titles and candidate questions from document chunks for application in retrieval pipelines, and provides empirical evidence applicable to large-scale information retrieval systems by demonstrating improved retrieval accuracy through qualitative evaluation.

cs.IR↗

arXiv2025

SLM-Based Agentic AI with P-C-G: Optimized for Korean Tool Use

We propose a small-scale language model (SLM) based agent architecture, Planner-Caller-Generator (P-C-G), optimized for Korean tool use. P-C-G separates planning, calling, and generation by role: the Planner produces an initial batch plan with limited on-demand replanning; the Caller returns a normalized call object after joint schema-value validation; and the Generator integrates tool outputs to produce the final answer. We apply a Korean-first value policy to reduce execution failures caused by frequent Korean-to-English code switching in Korean settings. Evaluation assumes Korean queries and Korean tool/parameter specifications; it covers single-chain, multi-chain, missing-parameters, and missing-functions scenarios, and is conducted via an LLM-as-a-Judge protocol averaged over five runs under a unified I/O interface. Results show that P-C-G delivers competitive tool-use accuracy and end-to-end quality while reducing tokens and maintaining acceptable latency, indicating that role-specialized SLMs are a cost-effective alternative for Korean tool-use agents.

cs.CL↗