Hi Lars, I agree with you, that if there is data out there, it isn't easy to find, many thanks for sharing!
Fitting to the full model had limited improvements, the b coefficient was quite large making it essentially equal to the ln(x) function you fitted in excel. It is attached as "Lars_FitToMil55310.png". So on further thought, the B term can't model a device aging even faster than it should shortly after infancy. In the two extreme cases either B is large and (Bt)>>1 so the be B term ends up just being an additive bias, or B is small, and ln(x) is linearized (or slowed down) during the first bit of time. You can approximated the MIL 55310 between two points in time as f(t2) - f(t1) = Aln(t2/t1) A = ( f(t2) - f(t1) )/ln(t2/t1) Looking at some of your plots it looks like between the end of year 1 and year 10 you age from 20 ppb to 65 ppb, A ~ 20 The next plot "Lars_ForceAcoef", is a fit with the A coefficient forced to be 2 and 20. The 20 doesn't end-up fitting well on this time scale. Looking at the data a little more, I wondered if the first 10 day are going through some behavior that isn't representative of long-term aging, like warm-up, retrace (I'm sure bob could name half a dozen more examples). So the next two plots are fits of the 4 data points after day10, and seem to fit well, "Lars_FitAfterDay10.png", "Lars_1Year.png". If you are willing to share the next month, we can add that to the fit. Cheers, On Fri, Nov 18, 2016 at 1:26 PM, Lars Walenius <[email protected]> wrote: > > Hopefully someone can find the correct a and b for a*ln(bt+1) with stable32 or matlab for this data set: > Days ppb > 2 2 > 4 3.5 > 7 4.65 > 8 5.05 > 9 5.22 > 12 6.11 > 13 6.19 > 25 7.26 > 32 7.92
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