Automated frequency selection for machine-learning based EH/EW prediction from S-Parameters

Abstract

In the field of High Speed SerDes (HSS) channel analysis and design, the most widely accepted metrics for gauging signal integrity are Time Domain (TD) metrics: Bit Error Rate (BER), Eye-Height (EH) and Eye-Width (EW). With increasing bit-rates, TD simulations are getting compute-time intensive especially as the BER criterion is getting lower. Learning based mapping of Frequency Domain (FD) S-Parameter data to EH/EW in TD provides a fast alternative solution for thorough design-space exploration. A key challenge in this mapping procedure is the identification of the optimal frequency points in the S-Parameter data that are used for training the learning network. This paper outlines a methodology to identify the minimal set of critical frequency points using a Fast Correlation Based Feature (FCBF) selection algorithm. This technique is applied for prediction of EH/EW for a PCIe Gen 3 interface and the prediction accuracy is quantified.