Buffer-Parameterized Machine Learning Surrogate Models for Cross-Technology Signal Integrity Analysis and Optimization
The paper introduces a machine learning method that treats IC buffer characteristics as dynamic inputs alongside PCB parameters to create a single surrogate model capable of predicting signal integrity across different technologies without needing retraining for each new technology. This allows for efficient, automated optimization across complex design spaces.