Automated Frequency Selection for Machine-Learning based EH/EW prediction from S-Parameters

Ambasana, Nikita and Gope, Dipanjan and Mutnury, Bhyrav and Anand, Gowri (2015) Automated Frequency Selection for Machine-Learning based EH/EW prediction from S-Parameters. In: 24th IEEE Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), OCT 25-28, 2015, San Jose, CA, pp. 53-55.

PDF IEEE_24th_Con_Ele_Per_Ele_Pac_Sys_53_2015.pdf - Published Version Restricted to Registered users only Download (1MB) | Request a copy

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.

Item Type:	Conference Proceedings
Series.:	IEEE Conference on Electrical Performance of Electronic Packaging and Systems-EPEPS
Publisher:	IEEE
Additional Information:	Copy Right of this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Keywords:	Signal Integrity; PCIe; ANN; Eye-Height; Total FEXT; Insertion Loss; Feature Selection
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	20 Jul 2016 09:09
Last Modified:	20 Jul 2016 09:09
URI:	http://eprints.iisc.ac.in/id/eprint/54229

Actions (login required)

View Item