Dr. Dong-Soo Han's research team at the Korea Institute of Science and Technology (KIST) Semiconductor Technology Research Center, in collaboration with the research teams of Prof. Jung-Il Hong at DGIST and Prof. Kyung-Hwan Kim at Yonsei University, has developed a device principle that can utilize "spin loss," which was previously thought of as a simple loss, as a new power source for magnetic control.

Inverse lithography technology (ILT) is driving transformative innovations in semiconductor patterning processes. This paper reviews the evolution of ILT, providing an analysis of the applications in semiconductor manufacturing. In recent years, artificial intelligence (AI) has introduced breakthroughs for ILT, such as convolutional neural networks, generative adversarial networks, and model-driven deep learning, demonstrating potential in large-scale integrated circuit design and fabrication. This paper discusses future directions for ILT, which is expected to provide insights into semiconductor industry development.

A recent study developed a highly accurate risk prediction framework for preterm birth (PTB) that could broaden the potential of AI-driven multi-omics applications in precision obstetrics and biomedical research.

The model, deeply integrating genomics, transcriptomics, and large language models (LLMs) for the first time for PTB risk prediction, has shown its effectiveness and clinical application prospects.

The research was conducted by a collaborative team led by BGI Genomics, together with Professor Huang Hefeng's team, Shenzhen Longgang Maternal and Child Health Hospital, Fujian Maternity and Child Health Hospital, and OxTium Technology. The research was published in npj Digital Medicine on August 20th.