Buffer-parameterized ML surrogates enable cross-technology PCB signal integrity prediction and optimization by treating buffer characteristics as dynamic inputs, with neural networks outperforming alternatives on large datasets and delivering large speedups over full simulations.
AI-Based SI-Compliant PCB Design Support for DDR Technology Enhanced by Transfer Learning
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Buffer-Parameterized Machine Learning Surrogate Models for Cross-Technology Signal Integrity Analysis and Optimization
Buffer-parameterized ML surrogates enable cross-technology PCB signal integrity prediction and optimization by treating buffer characteristics as dynamic inputs, with neural networks outperforming alternatives on large datasets and delivering large speedups over full simulations.