2D contact modeling outlines a comprehensive investigation into reducing contact resistance in 2D Transition Metal Dichalcogenide (TMD) field effect transistors through analyzing ohmic contacts with various types of metals using Density Functional Theory (DFT) calculations. The study emphasizes distinguishing characteristics between the metal electrodes and their impact on channel transport properties, including the material symmetry, surface contact rotation, and interface charge redistribution. We introduce a novel approach using a Fermi-level-degenerated TMD layer or a semimetal as an interlayer between the semiconductor and the conventional metal. This method explores shifts in the Fermi level to enhance charge transport. 

Additionally, various dopants in TMD materials are examined for reducing contact resistance, leveraging selective phase transitions and understanding electronic properties. The investigation extends to managing van der Waals (vdW) gaps in 2D top-contact devices to alleviate contact resistance by controlling Metal-Induced Gap States (MIGS) and tunneling resistivity, aiming to identify optimal vdW gap. 

AOS modeling and simulation research aims to explain phenomena occurring in reality through simulations and achieve superior performance.

In the atomistic level study using Density Functional Theory (DFT), we investigate changes in parameters such as composition, doping, and interstitial hydrogen to understand variations in bandgap, Fermi level, charge neutrality level, and effective mass. This exploration helps identify how specific structures influence various properties. Additionally, we examine not only the materials themselves but also the characteristics at the interfaces when metal-AOS junctions are formed.

In the device level study utilizing Technology Computer-Aided Design (TCAD), we explain the phenomena occurring in actual devices through physics models. For example, we model and simulate a wide range of phenomena, including changes in threshold voltage due to channel thickness, variations in mobility due to temperature and channel thickness, and changes in Schottky Barrier Height (SBH) and contact resistance due to the SBH pinning effect.

Furthermore, we use material properties derived from Density Functional Theory (DFT) as input for our physics models, allowing us to develop more accurate and detailed simulations of the electrical characteristics influenced by specific factors. 

As artificial intelligence becomes an essential part of our daily lives, we need more powerful devices to handle the many parameters in neural networks. Neuromorphic devices, which mimic the human brain, are promising candidates to meet these demands. Our focus in this field is on materials and technologies that enhance performance and reliability while maintaining compatibility with traditional silicon CMOS technology. Our research focuses on modeling emerging devices like RRAM (Resistive Random Access Memory) and ECRAM (Electrochemical Random Access Memory) to enhance and validate their specifications and uncover the underlying physical mechanisms.

Artificial Intelligence (AI) has become a pivotal area of research, revolutionizing various fields with its vast capabilities. Among the many applications of AI, our laboratory focuses on leveraging AI for material discovery and compact modeling.
Compact modeling is crucial for the simulation and design of semiconductor devices. Traditional physics-based compact modeling often involves prolonged development periods and struggles to address unknown physical phenomena. To overcome these challenges, our research employs data-driven machine learning techniques for compact modeling.

Moreover, the discovery of new functional materials can drive fundamental innovations across a wide range of technological applications, including semiconductor devices and advanced materials. Our laboratory focuses on harnessing AI to explore and discover groundbreaking functional materials. This approach holds the potential to develop new materials with exceptional electrical and thermal conductivity, contributing to various high-performance applications.
To achieve this, we aim to develop AI models that can efficiently explore and predict synthesizable material systems with diverse compositions and structures. Furthermore, we strive to create AI models capable of reverse engineering material compositions to achieve targeted properties.