Application of artificial neural networks for eye-height/width prediction from s-parameters

Signal speeds of high speed serial data links double almost every generation and with increasing speeds, simulation and modeling challenges are getting more complex. The present popular and widely accepted metric for simulating a high speed link from signal integrity (SI) perspective is Bit Error Rate (BER) testing. SI engineers look at eye-height and eye-width to determine the quality of an interface for a given set of design parameters. In order to perform BER simulations, time domain simulations need to be performed over billions of bits for serial links using statistical approaches and these simulations are time, processing power and memory intensive. Design of Experiments (DoE) is typically used to decrease the number of time-domain simulations needed to cover the design space, however it is sometimes in-accurate as compared to full-factorial design sweeps. End to end channel simulation in frequency domain is much faster and less resource intensive. In this paper, a DoE based set of channel parameters are simulated in both time-domain and frequency-domain to train a multi-layer perceptron (MLP) type of artificial neural network (ANN) to predict eye-height from frequency domain metrics like return loss (RL) and insertion loss (IL). This results in a significant speed-up towards a more accurate all corner study as compared to DoE based analysis.