Research

As device scaling continues to follow Moore's Law, the limitations of conventional 3D materials are increasingly evident. In response, 2D Transition Metal Dichalcogenides (TMDCs) have gained significant attention as promising candidates for next-generation semiconductor materials.

TMDCs exhibit unique advantages such as enhanced short-channel control, high carrier mobility, and van der Waals contact interfaces, making them ideal for addressing the challenges of ultra-scaled devices. However, to fully realize the potential of 2D TMDCs for More Moore and More than Moore applications, several key challenges remain:

• Semiconductor Devices for "More Moore": Utilizing 2D materials to enable reliable processes for achieving sub-10 nm channel lengths and advancing 'More Moore' scaling.

• Next-generation Device Architecture: Reducing contact resistance, subthreshold Swing (SS), and short channel effects as well as improving charge carrier mobility, and enhancing device stability through extensive research on innovative transistor designs using 2D materials, including gate/source/drain design, MESFETs and Tunneling FETs.

• TCAD Device Simulations: Accurately modeling semiconductor devices to analyze electrical characteristics, evaluate material properties, and optimize designs for advanced device performance. 

Our research focuses on advancing fabrication methods, exploring novel device geometries, and integrating 2D materials into complex systems. These efforts aim to bridge the gap between fundamental material properties and practical device applications, contributing to the next-generation nanoelectronic technologies.

As semiconductor devices approach the physical and performance limits of traditional bulk materials, two-dimensional (2D) materials such as transition metal dichalcogenides (TMDCs) and boron nitride (BN) have emerged as promising alternatives. These materials offer unique advantages, including high carrier mobility, tunable electronic properties, and van der Waals interfaces, positioning them as strong candidates for next-generation semiconductor and optoelectronic applications.

The advancement of scalable synthesis methods is essential for unlocking the potential of high-performance 2D materials. Our research focuses on developing novel techniques for large-area, high-quality synthesis to fully realize their capabilities:

• CVD-based 2D Materials: Enabling scalable synthesis with precise control over thickness and uniformity through Chemical Vapor Deposition (CVD).

• Solution-processed 2D Materials: Optimizing dispersion techniques using tailored solvents for cost-effective integration and addressing scalability challenges.

Synthesized 2D materials demonstrate significant potential for applications in photonic devices, transistors, flexible electronics, and memory systems. Our work highlights how 2D material synthesis techniques bridge the gap between material properties and real-world applications, advancing modern semiconductor technologies.

Furthermore, we focus on 3D vdW Monolithic Integration, which seamlessly incorporates 2D materials into multilayer device architectures. This approach paves the way for compact and multifunctional systems, advancing next-generation applications such as sensors, logic devices, and memory solutions. 

Traditional Von Neumann architectures face limitations due to sequential data processing and continuous data transfer between the CPU and memory, leading to the Von Neumann bottleneck. This inefficiency has driven the shift toward neuromorphic computing, inspired by the brain's parallel processing capabilities. Neuromorphic systems significantly improve computational efficiency and reduce power consumption, making them ideal for AI and machine learning applications.

The development of neuromorphic systems relies on artificial synaptic and neuronal devices. Our research exploits the unique properties of 2D materials to develop high-performance neuromorphic devices through:

• 2D Materials for Synaptic Devices: Our research focuses on utilizing the unique properties of 2D materials to mimic synaptic functionalities through material design and innovative device architectures. By engineering chemical and physical properties of 2D materials, we optimize the electrical and optoelectrical properties to enhance their suitability for synaptic and neuronal behaviors. These engineered 2D materials can be employed in various device architectures to explore new computing methods to fully harness the potential of neuromorphic systems for large-scale AI tasks and dynamic learning.

Additionally, we investigate the integration of components like sensors to create multimodal neuromorphic systems, advancing biologically inspired architectures for next-generation AI applications.