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Validation of high-speed interface performance in a given design space from a Signal Integrity (SI) perspective requires Bit Error Rate (BER) computation. Eye Height (EH) and Eye Width (EW) are used to determine the quality of an interface for a given set of design parameters and frequency of operation. EH, EW and BER estimation requires Time Domain (TD) simulation of complex channel models over billions of bits, which is a time, compute power and memory intensive process. Statistical and optimization techniques such as Design of Experiments (DoE) based on generation of design sets that span the design space optimally exist today. However, it has been shown that DoE based simulations might result in in-accurate sensitivity analysis for highly nonlinear design spaces. Also, the size of a DoE set scales exponentially with the number of design variables. It has been shown in [5] that TD metrics EH and EW, in absence of cross-talk, can be mapped from FD metrics like Return Loss (RL) and Insertion Loss (IL) using Artificial NeuralNetworks (ANN). The training of the ANNs requires DoE for the existing method. In this paper, an alternative technique to DoE, for generating a training set for ANN is presented, which remains constant over several number of design variables, and scales only in the number of FD metrics used to map to TD metrics and the number of samples in FD. Simulations for SATA 3.0 channel topology with and without cross-talk in TD are presented to quantify the accuracy of the said approach.

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 timedomain and frequency-domain to train a multi-layer perceptron (MLP) type of artificial neural network (ANN) to predict eyeheight 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.

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