arXiv subjects

Haotian Su

Publications and source records attributed to Haotian Su.

5 recordsLinked to original sources

arXiv2025

Scaling Nanoribbon Transistors with Monolayer Transition Metal Dichalcogenides

Nanoscale transistors require aggressive reduction of all channel dimensions: length, width, and thickness. While monolayer two-dimensional semiconductors (2DS) offer ultimate thickness scaling, good performance has largely been achieved only in micrometer-wide channels. Here, we demonstrate both $\it{n}$- and $\it{p}$-type nanoribbon transistors based on monolayer 2DS, fabricated using a multi-patterning process, reaching channel widths and lengths down to 25-30 nm. 'Anchored' contacts improve device yield, while nanoscale imaging, including tip-enhanced photoluminescence, reveals minimal edge degradation. The devices reach on-state currents up to 560, 420, and 130 $\mu$A $\mu$m$^{-1}$ at 1 V drain-to-source voltage for $\it{n}$-type MoS$_{2}$, WS$_{2}$, and $\it{p}$-type WSe$_{2}$, respectively, integrated with thin high-$\kappa$ dielectrics. These results surpass prior reports for single-gated nanoribbons, the WS$_{2}$ by over 100 times, even in normally off (enhancement-mode) transistors. Taken together, these findings suggest that top down patterned 2DS nanoribbons are promising building blocks for future nanosheet transistors.

cond-mat.mtrl-sci↗

arXiv2025

Flexible radiofrequency carbon nanotube transistors operating at frequencies above 100 GHz

The development of the sixth generation of wireless communications technology (6G) requires terminals that can operate at frequencies above 100 GHz. For human-centric applications, these terminals should also be flexible and have low power. However, current flexible radiofrequency transistors typically have lower maximum frequencies, in part due to the poor thermal conductivity of flexible substrates. Here, we report radiofrequency transistors that are based on aligned carbon nanotube arrays on flexible substrates and have current gain cutoff frequencies ($f_{\text{T}}$) and power gain cutoff frequencies ($f_{\text{max}}$) above 100 GHz. This is achieved by using electro-thermal co-design to improve the heat dissipation and radiofrequency performance of the devices. The transistors exhibit an on-state current of 0.947 mA $\mu$m$^{-1}$, a transconductance of 0.728 mS $\mu$m$^{-1}$, a peak extrinsic $f_{\text{T}}$ of 152 GHz, a peak extrinsic $f_{\text{max}}$ of 102 GHz, and a power consumption under 200 mW mm$^{-1}$. We also show that the devices can be used to create flexible radiofrequency amplifiers with an output power of 64 mW mm$^{-1}$ and a 11 dB power gain in the K-band.

physics.app-ph↗

arXiv2025

High-field Breakdown and Thermal Characterization of Indium Tin Oxide Transistors

Amorphous oxide semiconductors are gaining interest for logic and memory transistors compatible with low-temperature fabrication. However, their low thermal conductivity and heterogeneous interfaces suggest that their performance may be severely limited by self-heating, especially at higher power and device densities. Here, we investigate the high-field breakdown of ultrathin (~4 nm) amorphous indium tin oxide (ITO) transistors with scanning thermal microscopy (SThM) and multiphysics simulations. The ITO devices break irreversibly at channel temperatures of ~180 {\deg}C and ~340 {\deg}C on SiO${_2}$ and HfO${_2}$ substrates, respectively, with failure primarily caused by thermally-induced compressive strain near the device contacts. Combining SThM measurements with simulations allows us to estimate a thermal boundary conductance (TBC) of 35 ${\pm}$ 12 MWm${^-}$${^2}$K${^-}$${^1}$ for ITO on SiO${_2}$, and 51 ${\pm}$ 14 MWm${^-}$${^2}$K${^-}$${^1}$ for ITO on HfO${_2}$. The latter also enables significantly higher breakdown power due to better heat dissipation and closer thermal expansion matching. These findings provide insights into the thermo-mechanical limitations of indium-based amorphous oxide transistors, which are important for more reliable and high-performance logic and memory applications.

physics.app-ph↗

arXiv2024

"We do use it, but not how hearing people think": How the Deaf and Hard of Hearing Community Uses Large Language Model Tools

Generative AI tools, particularly those utilizing large language models (LLMs), are increasingly used in everyday contexts. While these tools enhance productivity and accessibility, little is known about how Deaf and Hard of Hearing (DHH) individuals engage with them or the challenges they face when using them. This paper presents a mixed-method study exploring how the DHH community uses Text AI tools like ChatGPT to reduce communication barriers and enhance information access. We surveyed 80 DHH participants and conducted interviews with 11 participants. Our findings reveal important benefits, such as eased communication and bridging Deaf and hearing cultures, alongside challenges like lack of American Sign Language (ASL) support and Deaf cultural understanding. We highlight unique usage patterns, propose inclusive design recommendations, and outline future research directions to improve Text AI accessibility for the DHH community.

cs.HC↗

arXiv2024

"Real Learner Data Matters" Exploring the Design of LLM-Powered Question Generation for Deaf and Hard of Hearing Learners

Deaf and Hard of Hearing (DHH) learners face unique challenges in learning environments, often due to a lack of tailored educational materials that address their specific needs. This study explores the potential of Large Language Models (LLMs) to generate personalized quiz questions to enhance DHH students' video-based learning experiences. We developed a prototype leveraging LLMs to generate questions with emphasis on two unique strategies: Visual Questions, which identify video segments where visual information might be misrepresented, and Emotion Questions, which highlight moments where previous DHH learners experienced learning difficulty manifested in emotional responses. Through user studies with DHH undergraduates, we evaluated the effectiveness of these LLM-generated questions in supporting the learning experience. Our findings indicate that while LLMs offer significant potential for personalized learning, challenges remain in the interaction accessibility for the diverse DHH community. The study highlights the importance of considering language diversity and culture in LLM-based educational technology design.

cs.HC↗