Application of Artificial Neural Networks to Statistical Analysis and Nonlinear Modeling of High-Speed Interconnect Systems

In designing robust high-speed interconnect systems, the effects of parameter variations on system performance must be studied using statistical analyses. These analyses require repetitive circuit simulations to account for the randomness in parameter values caused by manufacturing and environmental changes. An accurate modeling technique is also essential for capturing the nonlinear relationships between channel parameters and performance. In this paper, the application of artificial neural networks to accurately capture the nonlinear mappings between parameters and performance to speed up the analysis of high-speed interconnect systems is described. An efficient set of data that uses a few simulations or experiments based on orthogonal arrays is proposed to train the neural network. The neural network can then serve to accurately and efficiently generate performance distributions. The usefulness and accuracy of the proposed approach is verified using an extreme data rate memory system that operates at a data rate of 3.2 Gb/s. The histograms and descriptive statistics of the eye height and timing jitter are compared with those obtained from traditional Monte Carlo, regression model, and worst case analyses