Modeling ECD with machine learning for CMP simulation
Aug. 2, 2021
Accurate post-ECD models are essential to successful CMP simulation. Adding machine learning can improve your results, but which approach should you use?
Accurate modeling of post-ECD surface topography variation is crucial for correct CMP simulation. Siemens and the American University of Armenia collaborated to investigate and evaluate the use of machine learning (ML) modeling techniques to predict these complicated topography variations. Using various ML methods to model post-ECD surface profiles and comparing the results enabled them to determine which architectures and models provided the best combination of running time and accuracy.
Explore Raspberry Pi’s imaging and AI tools in this free webinar with expert insights from Naushir Patuck. Register now to attend live or watch the recording later.
Now that Tesla said it, automakers are following. The move to 48V has started. Switching to a 48V electrical system greatly reduces the current levels the vehicle's wiring harness...
Its a challenge to create highly efficient and compact designs while also adhering to strict electromagnetic interference (EMI) requirements imposed by groups such as Comit ...
This paper examines EMI in switch-mode power supplies, and provides technology examples to help designers quickly and easily pass industry-standard EMI tests.