Re: [802.3_50G] review of baveja_3cd_01

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Mike Hi,

We do not have access to DMLs. Can we use EMLs as a replacement?

Thanks and Br,

Prashant P Baveja, Ph.D

Deputy Manager, R&D

Applied Optoelectronics, Inc. (NASDAQ: AAOI)

13139 Jess Pirtle Blvd
Sugar Land, TX 77478
USA
www.ao-inc.com

+1-281-295-1800 Ext. 287
+1-281-295-1888 (fax)

Prashant_Baveja@xxxxxxxxxx

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From: Dudek, Mike [mailto:Mike.Dudek@xxxxxxxxxx]
Sent: Tuesday, November 07, 2017 3:44 PM
To: STDS-802-3-50G@xxxxxxxxxxxxxxxxx
Subject: Re: [802.3_50G] review of baveja_3cd_01_1117

I too very much appreciate you bringing in this data.

One further suggestion for improvement. Please add a wider range of TDECQ values in the analysis of TDECQ versus Rx sensitivity (to reduce the effect of noise) . This could be obtained by including the room temp data and preferably one or two points from Mach Zander modulators with very low TDECQ in the same graph.

From: Jonathan King [mailto:jonathan.king@xxxxxxxxxxx]
Sent: Tuesday, November 07, 2017 1:28 PM
To: STDS-802-3-50G@xxxxxxxxxxxxxxxxx
Subject: Re: [802.3_50G] review of baveja_3cd_01_1117

Hi Prashant,

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!

Best wishes

jonathan

From: Prashant Baveja [mailto:Prashant_Baveja@xxxxxxxxxx]
Sent: Tuesday, November 07, 2017 12:56 PM
To: Jonathan King <jonathan.king@xxxxxxxxxxx>; STDS-802-3-50G@xxxxxxxxxxxxxxxxx
Subject: [EXTERNAL]: RE: review of baveja_3cd_01_1117

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

1) Measure more devices, lets call it 20, reflecting normal wafer level distribution, this will allow more data loading on low bandwidth side

2) Turn on the TDECQ optimizer

3) Measure with and without time center optimization.

4) Rereport the correlation or lack of thereof at next ad-hoc