As demand for optical communication capacity continues to grow, the 2 µm wavelength region is emerging as a promising new window for future photonic systems. Researchers have developed a high-speed InGaAs/GaAsSb type-II superlattice photodetector that is heterogeneously integrated on silicon via micro-transfer printing. By combining a backside Au reflector with a low-index BCB cavity, the device achieves broadband operation from 0.8 to 2.5 µm and a 57.9% responsivity enhancement at 2 µm, while maintaining 17–18.7 GHz bandwidth and clear 50 Gbps eye diagrams. The work provides a practical route toward broadband, high-speed photodetectors for silicon photonics and next-generation optical interconnects.

The explosion of artificial intelligence (AI) and the era of Big Data have exposed a critical flaw in traditional computer hardware: the slow and energy-consuming data transfer between memory and processors, commonly known as the von Neumann bottleneck. Every day, when our devices process AI queries, they must move massive amounts of data back and forth, causing severe delays and battery drain.

To address this challenge, researchers at The University of Hong Kong (HKU) have created a chip with a minimalist architecture that performs complex searches directly where the data is stored, fundamentally bypassing this bottleneck. The research team, led by Professor Can Li from the Department of Electrical and Computer Engineering of the Faculty of Engineering and the Centre for Advanced Semiconductors and Integrated Circuits (CASIC), has developed a novel analogue content-addressable memory (CAM) utilising two-dimensional (2D) MoS2 flash memories.

A research team has developed porous sodium-alginate composite gel microspheres capable of capturing uranium(VI) from aqueous solutions with high capacity and selectivity.