| Proj No. | A2200-251 |
| Title | Machine Learning Rapid Screening of 2D Materials in Quantum Transistor Research |
| Summary | 2D materials enable the development of next-generation transistors beyond Moore’s law, with the potential to achieve sub-1 nm technology nodes and ultralow-energy operation. Traditional methods for characterizing 2D materials, such as DFT simulations or experimental microscopy, are accurate but slow and resource-intensive. This project aims to develop a machine learning model that can rapidly identify and predict key properties of 2D materials, such as bandgap and layer characteristics, using publicly available datasets. The project will involve dataset curation, feature extraction, model training and evaluation, and visualization of results through an interactive interface. The outcome will be a prototype tool to support researchers in quickly screening 2D materials, contributing to the long-term goal of enabling transistor designs that approach physical limits. |
| Supervisor | Ast/P Song Peng (Loc:S1 > S1 B1B > S1 B1B 40, Ext: +65 67905438) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Photonics II (Loc: S1-B3b-16) |
| Single/Group: | Single |
| Area: | Microelectronics and Biomedical Electronics |
| ISP/RI/SMP/SCP?: |