Scientists at Lawrence Livermore National Laboratory (LLNL) now have a new technique in their toolkit for nuclear forensics examinations, which is the art and science of extracting information about the provenance and history of nuclear materials.
The November 2025 issue of Electronic Device Failure Analysis includes: Chiplet Architectures for Scalable Integration; Thermal Approaches to Fault Isolation in 3D Structures; Avoiding Device Degradation through Nanoprobing; and FA Chatbot with Retrieval-Augmented Generation.
Advancing technologies like batteries, electronics, and pharmaceuticals relies on discovering and verifying new materials, a process traditionally slowed by costly, time-consuming quality checks using specialized instruments. While AI has accelerated material discovery by identifying promising candidates from vast databases, MIT engineers have now developed a new AI tool that streamlines the verification process, potentially reducing […]
An NSF-funded initiative led by Lehigh University’s Parisa Khodabakhshi is advancing machine learning models that integrate physical laws to accelerate the design of high-performance alloys for aerospace, automotive, and healthcare applications. This effort complements additive manufacturing—also known as 3D printing—which constructs objects layer by layer using materials like metals, polymers, and biomaterials, offering a powerful […]
Scientists are working to better understand how components in mixed materials like concrete or underground rock are distributed, which could lead to stronger materials and safer storage of substances like carbon dioxide or nuclear waste. A key tool in this effort is the Poisson model, which randomly divides space using flat surfaces to simulate how […]
