POSTECH Professor Jonghun Kam’s research team, in collaboration with KRIHS, NDMI, Stanford University, and the National University of Singapore (NUS), presents AI-based, region-specific water conservation scenarios using drought news data.

- A KAIST–Sungkyunkwan University joint research team demonstrates a two-dimensional semiconductor structure in which electricity flows without obstruction

- Expected to serve as a key source technology for reducing power loss in AI semiconductors and ultra-low-power semiconductors

Ultra-thin 2D semiconductors exhibit strong optical responses, enabling compact photonic structures. Few-layer transition metal dichalcogenides (TMDCs) combine high refractive index, nonlinearity, and anisotropy, making them suitable for miniaturized elements. Scientists from Skolkovo Institute of Science and Technology in Moscow report a laser-assisted method for direct formation of TMDC diffractive elements from chemical precursors via localized reactions. This scalable approach enables fabrication of high-resolution gratings and lenses for chip integration and thin-film focusing applications.

Digital projection lithography is a promising alternative technology for the fabrication of functional micro/nanostructures. Researchers in China have developed a digital phase-shift mask projection lithography method that breaks the diffraction limit of conventional DMD-based maskless lithography. By introducing programmable phase control using dual spatial light modulators, the system enables high-contrast interference imaging and sub-100 nm patterning. The approach provides a low-cost, flexible, and scalable route for advanced semiconductor and nanophotonic manufacturing.

- KAIST, Sungkyunkwan University, and Korea University Anam Hospital analyzed real-world lifelog data from 1,224 older adults, identifying imminent diagnostic risk of cerebrovascular disease with 96.5% accuracy.

- The technology is expected to help shift the healthcare paradigm from hospital-centered and patient-initiated care to predictive and preventive care.

Existing electrochemical biosensing systems generally rely on external computing units for data analysis. The necessity of analog-to-digital conversion and data transfer leads to high complexity, low working efficiency and concern of privacy. Zhao et al. realize the in-sensor computing in electrochemical biosensing for the first time, which uses a single neuromorphic organic photoelectrochemical transistor to simultaneously perform multi-target biosensing and artificial neural network classification. The strategy permits in-situ training using real-time data, helping solving the issue of device-to device variations. The proof-of-concept application is demonstrated by direct classification of microRNA fingerprints in real biological samples, which provides a new paradigm for simpler, faster and more secure AI-driven electrochemical biosensing.

Researchers have developed BattFailScholar, a knowledge-augmented large language model framework that dramatically improves battery failure diagnosis. By equipping AI with a structured "case library" of over 11,000 battery failure examples, the system boosts diagnostic accuracy by 19.7% over standard LLMs, effectively handles rare failure types, and provides reliable risk assessments with confidence scores. Beyond diagnosis, it can infer missing links in failure chains with over 85% accuracy, offering a new tool for safer battery development.

With artificial intelligence (AI) workloads driving rising demands for computing power, bandwidth, and energy efficiency, integrated photonic neural networks (PNNs) offer a promising route beyond electronic bottlenecks. This review surveys emerging PNN technologies from devices and computing architectures to integration, packaging, and systems, highlighting optoelectronic co-design, key challenges, and their potential as next-generation AI accelerators.