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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.

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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
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
Depositing User: Id for Latest eprints
Date Deposited: 20 Jul 2016 09:09
Last Modified: 20 Jul 2016 09:09
URI: http://eprints.iisc.ac.in/id/eprint/54229

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