AI scalability will require full-stack co-optimization, not just bigger data centers. AI workloads require a 10X compute efficiency gain over 10 years, making collaboration across algorithms, ...
Standalone GPUs are being replaced by heterogeneous SoCs and chiplets that combine CPUs, GPUs, and NPUs to eliminate memory ...
Supports higher throughput, reduced cycle time, and lower cost per package, while enabling integration of increasingly ...
A researcher from the Okinawa Institute of Science and Technology (OIST) proposes redesigning the illumination systems and projectors used in high-NA EUV lithography to reduce optical effects and ...
On-die monitors, localized analytics, and lifecycle data are giving architects new ways to close the gap between design ...
Mask costs are not stopping leading-edge scaling, but they increasingly influence design, node, and process choices. High-NA EUV will tighten requirements for CD, EPE, local CDU, mask 3D modeling, ...
Researchers from University of Wisconsin-Madison and AMD Research and Advanced Development published a technical paper titled ...
Researchers from Google and University of California, Berkeley published a technical paper titled “Google’s Training ...
A new architecture enables higher data rates and densities while remaining pin-compatible with traditional DIMM.
Yu Ma. As AI-driven workloads continue to push the boundaries of compute scale, power efficiency, and bandwidth density, ...
Increased density at advanced nodes, multi-die assemblies, and the rollout of AI everywhere are making it much more challenging to ensure that memory will function properly over its expected lifetime.
The verification gap emerges not from a lack of computational power but from the multiphysics nature of 3D-IC behavior.
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