arXiv subjects

Stephanie Liu

Publications and source records attributed to Stephanie Liu.

4 recordsLinked to original sources

arXiv2026

Band alignment of grafted diamond/GaN p-n heterojunctions interfaced with ALD Al2O3 and SiNx/Al2O3

Diamond and gallium nitride are complementary semiconductors for forming p-n junctions because of their respective doping limitations. Understanding the band alignment of grafted diamond/GaN heterojunctions is therefore essential for optimizing diode performance. In this study, the band alignment of diamond/Al2O3/GaN and diamond/Al2O3/SiNx/GaN heterostructures was determined by X-ray photoelectron spectroscopy. Both structures exhibit type-II band alignment, but with different band offsets. The band offsets of the diamond/Al2O3/SiNx/GaN heterojunction are larger by 0.42 eV than those of diamond/Al2O3/GaN. This difference is attributed to a modification of the interfacial electrostatic potential, which may arise from a reduced density of positive fixed charges in the interfacial dielectric near the diamond/Al2O3 interface after insertion of the SiNx layer. These results demonstrate that interfacial-layer engineering provides an effective strategy for tailoring the band alignment of grafted diamond/GaN heterojunctions, offering guidance for the design of p-n diodes with tunable rectifying characteristics.

cond-mat.mtrl-sci↗

arXiv2026

Beyond the Basics: Leveraging Large Language Model for Fine-Grained Medical Entity Recognition

Extracting clinically relevant information from unstructured medical narratives such as admission notes, discharge summaries, and emergency case histories remains a challenge in clinical natural language processing (NLP). Medical Entity Recognition (MER) identifies meaningful concepts embedded in these records. Recent advancements in large language models (LLMs) have shown competitive MER performance; however, evaluations often focus on general entity types, offering limited utility for real-world clinical needs requiring finer-grained extraction. To address this gap, we rigorously evaluated the open-source LLaMA3 model for fine-grained medical entity recognition across 18 clinically detailed categories. To optimize performance, we employed three learning paradigms: zero-shot, few-shot, and fine-tuning with Low-Rank Adaptation (LoRA). To further enhance few-shot learning, we introduced two example selection methods based on token- and sentence-level embedding similarity, utilizing a pre-trained BioBERT model. Unlike prior work assessing zero-shot and few-shot performance on proprietary models (e.g., GPT-4) or fine-tuning different architectures, we ensured methodological consistency by applying all strategies to a unified LLaMA3 backbone, enabling fair comparison across learning settings. Our results showed that fine-tuned LLaMA3 surpasses zero-shot and few-shot approaches by 63.11% and 35.63%, respectivel respectively, achieving an F1 score of 81.24% in granular medical entity extraction.

cs.AI↗

arXiv2026

Grafted Low-Leakage Si/AlN p-n Diodes Enabled by Fluorinated AlN Interface

Ultrawide-bandgap AlN is a promising material for next-generation power electronics; however, its practical implementation is hindered by unstable surface chemistry and the high activation energy of p-type dopants. In particular, high-temperature rapid thermal annealing (RTA), required for forming low-resistance contacts on n-type AlN, leads to the formation of thick and defective surface oxides that degrade heterojunction performance. In this work, we present an interface engineering approach based on fluorination-induced AlFx formation combined with SiNx passivation to suppress defect-assisted leakage in p-Si/n-AlN heterojunction diodes fabricated via semiconductor grafting. A low-damage pseudo-atomic layer etching process is employed to remove RTA-induced oxides and restore a near-stoichiometric AlN surface. Subsequent XeF2 treatment forms an ultrathin AlFx layer, which is stabilized by an atomic-layer-deposited SiNx capping layer prior to p-Si nanomembrane integration. Electrical measurements show that the engineered AlFx/SiNx interface reduces reverse leakage current by several orders of magnitude compared to untreated or oxide-removed AlN surfaces, while preserving forward conduction characteristics. Temperature-dependent analysis indicates strong suppression of Poole-Frenkel emission and a shift of leakage onset to higher reverse bias, ultimately limited by bulk AlN crystal quality. X-ray photoelectron spectroscopy and transmission electron microscopy confirm the formation of Al-F bonds, reduced Al-O content, and the presence of a thin interfacial SiOx/SiON layer. These results establish AlFx/SiNx passivation as an effective strategy for stabilizing AlN interfaces and enabling low-leakage ultrawide-bandgap heterojunction devices.

cond-mat.mtrl-sci↗

arXiv2025

Preliminary Demonstration of Diamond-GaN pn Diodes via Grafting

Ultrawide bandgap (UWBG) semiconductors exhibit exceptional electrical and thermal properties, offering strong potential for high power and high frequency electronics. However, efficient doping in UWBG materials is typically limited to either n type or p type, constraining their application to unipolar devices. The realization of pn junctions through heterogeneous integration of complementary UWBG or WBG semiconductors is hindered by lattice mismatch and thermal expansion differences. Here, we report the preliminary demonstration of diamond GaN heterojunction pn diodes fabricated via grafting. A single crystalline p plus diamond nanomembrane was integrated onto an epitaxially grown c plane n plus GaN substrate with an ultrathin ALD Al2O3 interlayer. The resulting diodes exhibit an ideality factor of 1.55 and a rectification ratio of over 1e4. Structural and interfacial properties were examined by AFM, XRD, Raman, and STEM, providing critical insights to guide further optimization of diamond GaN pn heterojunction devices.

cond-mat.mtrl-sci↗