Valentines' Day 2022 witnessed the completion of AMD's acquisition of Xilinx.
It's also my day one with AMD as a Xilinx employee and I felt a strong urge to write down something to mark this day.
This something certainly can't be a technical topic, then what to write?
Recalled it's an all-stock-no-cash acquisition, I thought it could be fun to dig into this CP's stock market past performances.
One of the stories I heard long time ago is about a guy who made a great fortune and was said planning to woo a young but not very rich lady. However, nobody really saw any moves from him and the rumors eventually died down. Half decades later, the same young lady made a big fortune and became much richer than that guy. She, instead of hesitation, proposed to this guy straight away with a great success!
Fancying about happy fairy tales sharing similar story lines, I pulled the historical market values and daily stock prices of AMD and Xilinx (start with the date when AMD appointed Lisa as CEO to take the helm) from the Internet and plotted them on the same chart. Guess what! the stock prices of both companies bear more similarities than differences! And indeed that the guy is much richer than the lady to begin with. Looking at the historical daily stock price, there is a great correlation with a coefficient of 0.93.
A born couple?! 😍
Anyway, on 14, Feb. 2022, AMD and Xilinx united as one! 💕
Together, we advance! 🚀
Go AMD! 💪
Welcome to my blog, a place to discuss and share ideas in the areas of IC characterization; test and measurement theories, methodologies and instrument knowledge/tips; test programming, data analysis and post-processing; system signal and power integrity analysis etc.
Tuesday, February 15, 2022
Valentines' Day 2022: AMD Acquires Xilinx
Saturday, January 29, 2022
Transmitter FIR Output Waveform Simulation
In this notebook, a step-by-step walk through of a 3-tap TX FIR output waveform generation is done to demonstrate the very basic concepts of TX FIR and its impact to signals going through channels. Useful codes are packed into helper functions to be reused in the future to futher explore automatic channel adaptation schemes such as TX FIR, FFE, RX CTLE and DFE etc.
Saturday, January 1, 2022
From S-parameters to Eye Diagrams
In this notebook, I briefly walked through a bare minimum serial link simulation procedure and demonstrated each step in python for:
- Remote site S-parameter file loading, pre-processing and system impulse response(IR) extraction
- IR to single-bit-response (SBR) conversion (where IR is the general system impulse response of the channel while SBR is the output of the single-bit input after going through the channel which is data-rate dependent)
- Transient simulation through time domain 1d convolution (pay attention to the numpy and the scipy output differences in function call convolve(a,v,"same") mode)
- Simple and fancier eye diagram plots
Acknowledgment: rainbow text generation tool provided by Rainbow Text Generator Multi Color Text.
Saturday, November 27, 2021
It's a common practice for RF, microwave and high speed design engineers to pre-process and validate the scattering parameters (S-parameters) reciprocity, passivity and causality before proceed to use it. This notebook will walk through a sample implementation in python to show a simple way of S-parameter manipulation and plot to provide a quick examination through visual inspection. It will cover the following steps:
- load a sample 4-port S-parameter file hosted on the Internet (load file through its URL)
- single-ended S-parameter to mixed-mode conversion
- Output magnitude in dB for specific measured frequency points of interest
- Insertion loss plot for report and visual inspection quality check purposes
Saturday, November 13, 2021
IEEE 802.3ck Linear Fit Pulse Response and Transmit Equalizer Normalized Coefficient Extraction in Python
Saturday, July 17, 2021
PRBS13Q and pre-coded PAM4 test pattern generation
- Upload the ipynb file to gist and create the link as suggested by some of the proposed solutions.
- Manual create a simple html file and embed the gist link in-between the <body></body>.
- Create post in "html" instead of "component" view.
- Preview and publish.
Friday, January 31, 2014
Spectrum Analyzer Based Phase Noise/Jitter Measurement with Golden PLL in Excel
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| Demo of the GUI |
- Take in phase noise in “frequency-phase noise” data pairs and calculate the RMS RJ value. This is the spectrum analyzer based clock source random jitter measurement method described in FC-MJSQ and can be used to study the characteristics of different kinds of clock sources.
- Pass the phase noise data through PLL and calculate the PLL filtered RMS RJ value. It can be used to study the PLL characteristics and its jitter shaping effect on clock sources.
- Pass the phase noise data through PLL first and then a golden PLL. It can be used for transmitter compliance test.
- One-button click to perform phase noise measurement from the clock source directly if spectrum analyzer is connected to the host PC through GPIB. This will automate the phase noise raw data acquisition into the Excel instead of manually cut/paste.
- One-button click to perform screen capture of the spectrum analyzer for documentation and reporting purposes.
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| Figure 2: trapezoidal integration equation |
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| Figure 3: TX PLL transfer function |
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| Figure 4: SSB noise power at the TX PLL output |
To achieve capability 3:
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| Figure 5: first type golden PLL transfer function |
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| Figure 6: SSB noise power calculation |
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| Figure 7: calculated phase noise output |
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| Figure 8: transfer function plots |
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| Figure 9: phase noise plots |
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| Figure 10: named ranges for GPIB interface information |
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| Figure 12: phase noise manual input interface |
- Navigate to sheet “PNData”, click on “Delete PN Data” to clear old data points.
- Copy the external data and paste them into column A and B from row 2 onwards.
- Navigate to sheet “MainGUI” and click “Manual PN Data”. All the calculations will be done automatically with results updated instantly.
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| Figure 13: finished GUI |
Saturday, November 10, 2012
Confidence Level and Test Time Excel Calculator at Specified BER and Data Rate
To serve the above-mentioned purpose without sweat on the mathematics, you can actually make use of some online calculators such as the one from JitterTime. You can input target BER, data rate, measured errors and test time to calculate the confidence level. However, if you has a target CL in mind and want to know the test time or number of bits transmitted, then you need to "play with" the test time iteratively until you hit your CL target if use this online calculator. Alternatively, you need to look at the graph on the same website to find your answer indirectly.
To make life easier, I implemented similar calculators in Excel based on the same Poisson equation. It not only provides the flexibility of using the calculator without the access of the Internet but also provides the capability of calculating the test time and transmitted number of bits for specific CL targets using bisection root finding method with a 0.01 second accuracy. It can be downloaded here and hopefully can be of some use for you.
Take note, if you find that the Excel calculator doesn't response to your input. It's most likely because of your Excel disabled the Macro. If that is the case, you need to enable the Macro by going to "Options..." to "Enable the contents" first.
Sunday, May 20, 2012
Measurement Automation: How to Save Screens of 86100C DCA-J, SDA830 and E4440A/E4445A to Local PC Through GPIB
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| Picture from agilent.com |
- Save screen image from Agilent DCA-J 86100C to local PC as GIF files.
- Save screen image from LeCroy SDA830ZI-A to local PC as PNG files.
- Save screen image from Agilent E4440A/E4445A spectrum analyzer to local PC as GIF files.
- Dump raw spectrum data from Agilent E4440A/E4445A spectrum analyzer to local PC as text files.
- Dump raw phase noise log plot data from Agilent E4440A/E4445A spectrum analyzer to local PC as text files.
Please read on here ...
Saturday, February 11, 2012
Finally, 50G Hz electrical BW with 32Gbps hardware CDR module is available for DCA from Agilent
I have been looking for high bandwidth, high fidelity CDR modules for high speed (25Gbps and beyond) SerDes characterizations and finally it's here! It is Agilent 86108B Precision Waveform Analyzer which claimed to have "industry best intrinsic jitter below 50 femtoseconds, channel bandwiths to 50GHz, and clock recovery to 32Gbps ...."
If you are interested to know more, "Tips and Techniques for Accurate Characterization of 28Gb/s " is one of their good application notes to start with.
Saturday, August 20, 2011
AC Signal RMS vs. Peak Value
If you are interested, please read the full article here ...
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Saturday, June 11, 2011
Gaussian PDF, CDF, Error Function and Q-Transformation
Original also means the contents are my own understandings, thus may not be 100% correct. This is why I tried to give as more details as possible and attach the sample codes, if any, that used for preparing all the figures presented in the articles. Thus, your comments to correct wrong observations and conclusions, contributions to extend the scope of the articles are certainly warmly welcomed and greatly appreciated.
With that said, let me go back to the fields to continue today's topic: "PDF and CDF of Normal/Gaussian-distributions, Error Function and Q-transformation".
If you are an electrical engineer who want to brush up the very basic statistics that leads to a better understanding of random jitter modeling, dual-Dirac model based DJ, RJ decomposition and jitter extrapolation using bath-tube curve, you may like to read on here ....
You may also have interest in one of my previous articles talking about similar topics: "From Jitter Statistics to System BER"
Or simply look for it in May/2011 archives ...
Sunday, May 15, 2011
From Jitter Statistics to System Bit-Error-Ratio(BER)
A magic factor of 14 is most frequently cited in publications and literature when scale the unbounded system random jitter expressed in terms of its root mean square(RMS) value to the bounded peak-to-peak value at the specified system bit error ratio(BER) value of 1E-12. A good example is its use in the dual-Dirac model to calculate system total jitter from an estimated DJ and RJrms. This article tries to walk through the necessary mathematical equation derivations in detail and understand the physical meanings underlying the general statistical models involved.
The full article can be read here ....
Saturday, April 16, 2011
Periodic Jitter and Phase Modulation
It's been a while since my last working on this blog. To keep going on, I combed my PC and found some articles written some time ago which may worth sharing here.
Posted here is a topic briefly talking about periodic jitter caused by phase modulation and its extraction from the measured time interval error. The derivation of the analytical form relating the timing error and the signal spectrum is also shown in detail.
If you are interested, please read on ...
Sunday, March 6, 2011
Spectrum analysis of clock-like signals
Saturday, February 5, 2011
Real-time Oscilloscope Spectrum Analyzer and Matlab
This article is trying to understand a typical clock-like signal measured and interpreted in different domains(time domain and frequency domain) and try to correlate the different results produced from different sources such as measurements, analytical math equations or Matlab functions bridged by Discrete Fourier Transforms(DFT).
If you are interested, please read on....
Monday, January 10, 2011
Digital storage oscilloscope(DSO) fundamentals
This article has some equations and I didn't find a proper way of editing them using the blog build-in editor. And after I finished the article in google documents to take advantage of its equation editor function, I failed to find a proper way of publishing it to this blogger. I end up setting up a google website and published the article there through importing....
If you are interested to read this article, please follow the link shown below:
Thursday, January 6, 2011
Perl "for-loop" floating number issue and its solution
print "\n1st for-loop: start -> -0.05; stop -> 0.15; step -> 0.05 ...\n";
my $loop_cn=0;
my $tmp_start=-0.05;
my $tmp_stop=0.15;
my $tmp_step=0.05;
for(my $tmp=$tmp_start; $tmp<=$tmp_stop; $tmp+=$tmp_step)
{
$loop_cn++;
print "loop $loop_cn meets condition $tmp <= $tmp_stop with a step of $tmp_step\n";
}
print "\n2nd for-loop: start -> -0.5; stop -> 1.5; step -> 0.5 ...\n";
my $loop_cn=0;
my $tmp_start=-0.5;
my $tmp_stop=1.5;
my $tmp_step=0.5;
for(my $tmp=$tmp_start; $tmp<=$tmp_stop; $tmp+=$tmp_step)
{
$loop_cn++;
print "loop $loop_cn meets condition $tmp <= $tmp_stop with a step of $tmp_step\n";
}
print "\n3rd for-loop: start -> -0.06; stop -> 0.1; step -> 0.02 ...\n";
my $loop_cn=0;
my $tmp_start=-0.06;
my $tmp_stop=0.1;
my $tmp_step=0.02;
for(my $tmp=$tmp_start; $tmp<=$tmp_stop; $tmp+=$tmp_step)
{
$loop_cn++;
print "loop $loop_cn meets condition $tmp <= $tmp_stop with a step of $tmp_step\n";
# print sprintf("%.${10}g\n", $tmp);
# print "\n";
}
The execution results are shown as below:
It is very clear that the 1st and the 3rd loop is not working as expected while the 2nd loop works fine. Judged by the output of the 3rd for-loop “loop 4....” where the expected “0” is represented by a small number at the order of 1E-18, we know that the problem is caused by the inexact representation of the floating numbers due to limited machine precision. Thus, the attempt of using string comparison “le” in place of “<=” will not work either in this case.
Inspired by the solutions from: http://docstore.mik.ua/orelly/perl4/cook/ch02_04.htm , this issue can be addressed following:
1. Create a sub-routine for numerical comparison named as less_eq
The revised codes will give you the expected results for all cases as shown below:






















