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Thanks for confirming the feedback. I do appreciate how much work went into gathering the data and presenting it – thank you!
For data analysis, when trying to determine a correlation value on noisy data with a truncated range (ie, where the scatter is a significant fraction of the range) the simple linear fit in excel is generally going to be very misleading.
I prefer to determine RMS variation a hypothetical curve, for example I would hope that TDECQ is a good predictor of receiver sensitivity (for a receiver with the same EQ as the ref EQ) so it would have a 1:1 slope with measured sensitivity.
My posted review plotted a 1:1 slope graph (y=x + c) for which ‘c’ was optimized to minimize the RMS error for all the data points in each graph. This RMS value gives you an indication of how good a predictor TDECQ is for Rx sensitivity.
It sounds like a discussion about what the minimum number of taps should be for real EQ implementations would be useful.
I think it’s also very important (for credibility) to state what functionality was used when making the receiver sensitivity measurements – for example 12 tap T spaced FFE + 2 DFE.
(But use of DFE’s would definitely help low bandwidth parts a lot, but lead to error propagation, so comparing results at 2.4e-4 would no longer be valid).
Again, I appreciate how much work went into gathering the data, and presenting it – thank you!
Thank you for the review.
The trouble is that the data point looks as an outlier as the data is sparse. Doing the measurement on 10 devices was significantly time consuming. That device happened to have best RX sensitivity. Upon further review of chip level data, that device is part of normal distribution but has lower bandwidth than its peers.
The feedback I have is
Thanks and BR,
Prashant P Baveja, Ph.D
Deputy Manager, R&D
Applied Optoelectronics, Inc. (NASDAQ: AAOI)
Jess Pirtle Blvd
+1-281-295-1800 Ext. 287
Copyright 2016, Applied Optoelectronics, Inc.