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.

The review, entitled “Design strategies for high entropy materials in water electrolysis: enhancing activity, stability, and reaction kinetics,” presents an integrated framework guiding the development of HEMs from atomic-level tuning to industrial-scale application. “The core advantage of HEMs lies in their multi-element composition, which brings synergistic effects that single-element catalysts cannot achieve,” said Dr. Jing Zhang, the first author and a PhD candidate at Shanghai University. “This allows us to simultaneously optimize activity, stability, and reaction kinetics.”

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.