[SI-LIST] Re: model to measurement correlation - (taken from Re: 2D vs 3D EM based signal integrity simulators)
- From: "Grossman, Brett" <brett.grossman@xxxxxxxxx>
- To: "Jason.R.Miller@xxxxxxx" <Jason.R.Miller@xxxxxxx>
- Date: Mon, 25 Jan 2010 10:38:35 -0800
Thanks Jason.
I recall Bruce Archambeault of IBM referencing FSV in a DesignCon presentation
4 or 5 years ago. As I understood it at the time, FSV seemed like a very
useful method to replicate the way engineers visually analyze data (like what I
describe as the eyeball method, but more repeatable/consistent). Are you aware
if anyone has made an implementation of this method generally available? It
would be cool to perform a comparison between what folks use...
Thanks again,
-Brett
-----Original Message-----
From: Jason.R.Miller@xxxxxxx [mailto:Jason.R.Miller@xxxxxxx]
Sent: Monday, January 25, 2010 10:08 AM
To: Grossman, Brett
Cc: si-list@xxxxxxxxxxxxx
Subject: Re: [SI-LIST] model to measurement correlation - (taken from Re: 2D vs
3D EM based signal integrity simulators)
Feature Selective Validation (FSV) is a good technique for quantifying
the data comparison between simulated and measured s-parameters:
https://www.dora.dmu.ac.uk/xmlui/handle/2086/279
Jason Miller
Grossman, Brett wrote:
> For my own curiosity...
> I've seen the term 'correlation' used a lot when considering whether
> s-parameter simulation results (a.k.a. model results) agree with
> measurements. I am curious as to what people on this list generally consider
> as correlation.
>
> To achieve a measure of 'correlation', what I've personally observed being
> applied much of the time is a technique we call "The Eyeball Method." Use of
> this technique is often characterized by the user making some statement like
> "you can 'see*' that the measured and simulated insertion loss curves are
> right on top of each other." I'd say in the majority of papers I review this
> is the method employed.
>
> [*hence the reason we call it the eyeball method]
>
> Probably the next most common method I've seen is to take a scalar difference
> between a measured and simulated response, and allow the residual to be the
> measure of agreement. This is probably the second most used method in papers
> I review, and it is a very distant second (IMHO).
>
> We presented a method based on EVM a couple years ago which maintains the
> vector nature of the data to a degree. It was meant to compare a simulation
> to a distribution of measurements, but has also been applied to one
> measurement vs. one simulation comparisons.
>
> There are other methods we've described in past papers, and I believe that
> all of these methods have their place. I don't think I could describe them
> as well as I can if I hadn't used them all myself, so I don't feel as if I am
> picking on anyone by asking a question.
>
> What I am curious about from this list is:
>
>
> 1. What method do you use to quantitatively describe the agreement between
> simulated and measured s-parameters?
>
> Any comments?
>
> Thanks,
> -Brett
> Brett Grossman
> Sr. Staff SI Engineer
> Signal Integrity Pathfinding - Sort Test Technology Development
> [cid:image001.gif@01CA9DA3.2E08F710]<http://www.linkedin.com/in/brettgrossman>
>
>
>
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