Getting more data after MC run directly in the Results table?

Stephan,

You should just need to set the evalType column for the measurement to be "sweeps". Then it will make the measurement across the sweep (i.e. the Monte Carlo sweep), rather than the default eval type which is "point" - that's only evaluated per individual Monte Carlo point and so cannot compute results across the whole set of mc iterations.

If you've built your post-processing functions properly, they should work across the sweep data too. 

Personally I've not created the functions you're asking for in SKILL (I have for many others, of course), but I have used the ADE MATLAB integration to do the same thing... see this video: Using the Enhanced Virtuoso ADE Product Suite and MATLAB Integration: A Practical Guide (Video [cc])

Andrew

Stephan,

You should just need to set the evalType column for the measurement to be "sweeps". Then it will make the measurement across the sweep (i.e. the Monte Carlo sweep), rather than the default eval type which is "point" - that's only evaluated per individual Monte Carlo point and so cannot compute results across the whole set of mc iterations.

If you've built your post-processing functions properly, they should work across the sweep data too. 

Personally I've not created the functions you're asking for in SKILL (I have for many others, of course), but I have used the ADE MATLAB integration to do the same thing... see this video: Using the Enhanced Virtuoso ADE Product Suite and MATLAB Integration: A Practical Guide (Video [cc])

Andrew

Thanks Andrew for quick reply. I use meesurements across sweeps. 

The key problem is that here ADE does not support all skill function, but only a few, like average, stddev, but not yet skewness and kurtosis.
Whenever you enter e.g. kurtosis(..) it will immediately changed to (kurtosis ..).

Getting the statistical functions is quite easy, all we need is the normal distribution pdf (~exp x^2), plus cdf and inverse cdf.

I already made them all in Skill. Only the application is currently restricted, which is a pity. Want to avoid Matlab, prefer skill. 

Here is the code for 2 such new functions. Doing the same for other distributions is easy too (e.g. for lognormal fits, etc.)

procedure(phiNorm(x)
  let( (invSqrt2Pi)
    invSqrt2Pi = 0.3989422804014327 ; 1/sqrt(2*pi)
    invSqrt2Pi * exp(-(x*x)/2.0)
  )
)

procedure(cdfNorm(x)
  let( (sign z tz a1 a2 a3 a4 a5 phi tpoly cdf)
    a1 = 0.319381530
    a2 = -0.356563782
    a3 =  1.781477937
    a4 = -1.821255978
    a5 =  1.330274429

    sign = 1.0
    z    = x
    when(z < 0.0
      sign = -1.0
      z = -z
    )

    tz  = 1.0 / (1.0 + 0.2316419 * z)
    phi = phiNorm(z)

    tpoly = a1*tz + a2*tz*tz + a3*tz*tz*tz + a4*tz*tz*tz*tz + a5*tz*tz*tz*tz*tz
    cdf   = 1.0 - phi * tpoly

    if(sign < 0.0 then
      1.0 - cdf
    else
      cdf
    )
  )
)

Also making a plot of the Gaussfit works fine (but not from ADE, but from CIW):

; PdfNormal(mu sigma @optional nPoints)
;   - Build a normal pdf waveform from given mean and sigma
;   - x-range = [mu - 5s, mu + 5s]
;   - nPoints samples (default 200)
; Usage:
;   wNorm = PdfNormal(0.0 1.0)
;   plot(wNorm)

procedure(PdfNormal(mu sigma @optional (nPoints 200))
  let( (startX endX dx sig2 normConst i xList yList xVal yVal)

    when(sigma <= 0.0
      return(nil)
    )

    when(nPoints < 2
      nPoints = 2
    )

    startX = mu - 5.0 * sigma
    endX   = mu + 5.0 * sigma
    when(endX <= startX
      return(nil)
    )

    dx       = (endX - startX) / (nPoints - 1)
    sig2     = sigma * sigma
    normConst = 1.0 / (sqrt(2.0 * 3.14159265358979) * sigma)

    xList = nil
    yList = nil

    for( i 0 nPoints-1
      xVal = startX + i * dx
      yVal = normConst * exp( -0.5 * ((xVal - mu)*(xVal - mu) / sig2) )
      xList = cons(xVal xList)
      yList = cons(yVal yList)
    )

    xList = reverse(xList)
    yList = reverse(yList)

    abMakeWaveform(yList xList)
  )
)

StephanWeber said:

The key problem is that here ADE does not support all skill function, but only a few, like average, stddev, but not yet skewness and kurtosis.
Whenever you enter e.g. kurtosis(..) it will immediately changed to (kurtosis ..).

That's not true. ADE absolutely supports all SKILL functions, provided that they return scalar values or waveforms, and that they handled value coming in which are either scalars, waveforms or families. kurtosis and skewness are absolutely supported in ADE. Just because it changes to show parenthesis before the function doesn't mean they are not supported (I didn't see that behaviour - I was trying with IC25.1 ISR4 - and the results are correctly computed).

I don't know how you are calling the functions above in ADE, but I suspect it's because they are not handling the arguments being potentially passed in as a waveform/family. You might need to use the standard pattern of a cond() function which does drIsWaveform, famIsFamily, famMap and so on around your code - as seen in many of my functions.

I don't have the time today to try out your code and adapt it, but you could contact customer support to have one of the team investigate.

Andrew

Andrew Beckett said:

You might need to use the standard pattern of a cond() function which does drIsWaveform, famIsFamily, famMap and so on around your code - as seen in many of my functions.

... and as explained e.g. at https://support.cadence.com/apex/ArticleAttachmentPortal?id=a1O0V000007MkO7UAK