Blog Review: November 29

Electro-thermal design; AI in EDA; early virtual prototyping; EMI in power devices.


Siemens’ Matt Walsh checks out electro-thermal design and how a Boundary Condition Independent Reduced Order Model (BCI-ROM) can capture accurate characteristics from a 3D thermal analysis, ready for use in a 1D circuit simulation.

Cadence’s Vinod Khera considers how EDA could benefit from the AI revolution by providing a productivity boost through virtual assistants and improving code quality.

Synopsys’ Johannes Stahl, Kamal Desai, and Vivek Prasad argue for employing a chip verification and virtual prototyping strategy early when designing SoCs for high-performance computing and the data center, starting in the initial phases of the SoC architecture design and continuing through every phase that follows.

Ansys’ Akanksha Soni provides a primer on system-on-chip (SoC) history, design flow, and pros and cons.

Keysight’s Emily Yan shares a program and set of experiments that explore specific aspects of electromagnetic interference within DC-DC power converters to explain the roots of EMI issues and show how to design and implement solutions.

Arm’s Nicholas Cook looks towards a future of ambient computing, where technology and devices fade into the background and don’t require people to consciously interact with them by using AI to adapt to preferences and environments.

Renesas’ Taisuke Kodama tries out a simple project of creating AI-generated code for an Arduino-compatible board.

SEMI’s James Amano introduces an effort to reduce PFAS dependency, mitigate PFAS-related semiconductor supply chain disruption, and encourage environmentally responsible risk management in cases where there are no alternatives to PFAS.

Plus, check out the blogs featured in the latest Manufacturing, Packaging & Materials newsletter:

Amkor’s Gabriel Chang and Ricky Zang examine methods for predicting laser-assisted bonding parameters and ideal solder temperature range for a black box substrate.

Synopsys’ Shela Aboud zeroes in on using simulations with physics-based models to develop and optimize semiconductor device technology.

eBeam Initiative’s Jan Willis shows how digital twins can bridge the data gap that keeps deep learning prototypes from moving to production.

Lam Research’s Daebin Yim lays out a technique for benchmarking the performance of ruthenium, cobalt, and copper in a damascene vehicle with varying critical dimensions.

Tignis’ David Park offers a way to help teams be more efficient through expedited onboarding and predictive maintenance.

University of Florida’s Toshi Nishida, University of Virginia’s Avik W. Ghosh and Mircea Stan, and University of Central Florida’s Swaminathan Rajaraman explain why across-the-stack innovation, from new materials and devices to novel computing models, is now required.

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