MBSE for multiphysics; traceability vs. tracking; UALink VIP; EM simulation; code migration; SOI.
Synopsys’ Jayraj Nair checks out how a model-based systems engineering workflow can help manage the complex multiphysics analysis needed to optimize heterogeneous systems.
Siemens’ Melville Bryant explains the difference between semiconductor traceability and tracking and why they’re both essential, especially for complex multi-die devices.
Cadence’s Jamdagni Trivedi checks out VIP options for validating various topologies of the UALink Protocol Level Interface.
Keysight’s Stephen Slater finds that advancements in finite element method solvers and GPU acceleration mean that electromagnetic simulations can achieve both accuracy and solve-time reduction.
Arm’s Zach Lasiuk addresses how to improve AI code generation for Arm platform migrations by embedding architectural knowledge directly into the coding agent’s workflow.
SEMI’s Gity Samadi argues that silicon-on-insulator is a foundational technology that’s crucial for advancing the next wave of edge AI innovation.
Plus, check out the blogs featured in the latest Manufacturing, Packaging & Materials newsletter:
Siemens’ Ben Green explains why reliability is now a competitive advantage, and what’s needed to achieve it.
Amkor’s Nathan Whitchurch details how to close the gap between warpage simulation results and actual measurement data.
Synopsys’ Travis Brist explains how high‑NA EUV’s reduced field size is driving new innovation in optical proximity correction and mask synthesis.
Lam Research’s Brett Lowe shows how to match a simulated device to its real-world counterpart with sub-nanometer accuracy.
ASE’s Vincent Lin digs into co-packaged optics and such challenges as bandwidth density mismatches, assembly complexity, and fiber alignment.
SEMI’s Samer Bahou and Jaegwan Shim discuss how major companies are using AI to transform semiconductor operations for next-generation AI systems.
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