Researchers at Shanghai University have developed a physics-constrained, data-efficient artificial intelligence framework that enables accurate thermal field inversion in chiplet-based packaging systems using only limited temperature measurements. The approach addresses a critical challenge in advanced heterogeneous integration, where increasing power density and material heterogeneity complicate thermal monitoring and reliability assessment.

Chiplet-based packaging integrates multiple heterogeneous chiplets into a single system, offering improved flexibility, yield, and cost efficiency compared to traditional system-on-chip designs. However, the resulting increase in power density and deterioration of heat dissipation conditions can lead to localized overheating, threatening system stability and long-term reliability. Accurately reconstructing the temperature field of such systems from sparse sensor data is therefore essential, yet remains a difficult inverse problem in practical engineering applications.

A joint research team from NIMS, Tokyo University of Science, and Kobe University has developed a new artificial intelligence (AI) device that exploits ion behavior to perform information processing. The team succeeded in reducing the computational load to about 1/100 of that required for conventional deep learning. The technology is expected to contribute to enhancing the information processing performance of "edge AI" operating directly on terminal equipment (an edge device). This research was published in ACS Nano on October 14, 2025.

The results have been published in the prestigious journal Nature Nanotechnology and analyze the behavior of polaritons, hybrid light–matter quasiparticles. The research is part of the TWISTOPTICS project, led by Pablo Alonso González, a professor at the University of Oviedo, and funded by an ERC Consolidator Grant from the European Research Council

Manufacturing complex fluids industrially often involves mechanical forces that introduce internal stress and damage microscopic structures, compromising a material’s integrity and functionality. While rheo-optical techniques are available for evaluating structure–stress relationship, they do not accurately link mechanical stresses to observable optical changes under extensional stress. Addressing this, researchers from NITech, Japan, have now developed a novel technique for simultaneous measurement of macroscopic extensional stress and microscopic changes to optical properties during uniaxially extensional flow.