Hi, men -

There are sometimes 3 columns on a datasheet which may contain min, nom,
and max values. Like Vds for example.

Is there any probability tacitly assigned to the values? I know that
nominal is the average, and I assume that the value lies between the two
middle one standard deviation points.

1. Is that a valid assumption?

2. Is the min between one and two standard deviations down from the mean?

Thanks and I apologize for asking such a basic question in this
professional group.

Cheers,
John

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On 12/14/2024 6:50 PM, John S wrote:

There are sometimes 3 columns on a datasheet which may contain min, nom, and max values. Like Vds for example.

Is there any probability tacitly assigned to the values? I know that nominal is
the average, and I assume that the value lies between the two middle one standard deviation points.

1. Is that a valid assumption?

2. Is the min between one and two standard deviations down from the mean?

Min and Max are "guaranteed" limits (as much as anything can be "guaranteed"). Typ(ical) is where the *process* tends to produce the most yield.

That can change, over time. But, it will always be constrained by [min,max].

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On 15/12/2024 12:50 pm, John S wrote:

Hi, men -

There are sometimes 3 columns on a datasheet which may contain min, nom,
and max values. Like Vds for example.

Is there any probability tacitly assigned to the values? I know that
nominal is the average, and I assume that the value lies between the two middle one standard deviation points.

1. Is that a valid assumption?

2. Is the min between one and two standard deviations down from the mean?

Thanks and I apologize for asking such a basic question in this
professional group.

I doubt very much that this hasever been spelled out, but a span of two standard deviations above and below the mean would include 95% of a
randomly distributed variable, which would mean that one customer in
twenty might complain about getting an out-of-spec component.

I haven't done it that often, which suggests that the industry has a
adopted a three standard deviation span which would include 99.5% of a
randomly distributed variable.

I have bitched about duff components enough to fit that, but most of
them were fine at room temperature and only fell out of spec after
they'd warmed up, or to be more precise, after they'd been stuck in a
box in an enclosed rack and had had time to warm up a bit.

--
Bill Sloman, Sydney

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John S <Sophi.2@invalid.org> wrote:

Hi, men -

There are sometimes 3 columns on a datasheet which may contain min, nom,
and max values. Like Vds for example.

Is there any probability tacitly assigned to the values? I know that
nominal is the average, and I assume that the value lies between the two middle one standard deviation points.

1. Is that a valid assumption?

2. Is the min between one and two standard deviations down from the mean?

Thanks and I apologize for asking such a basic question in this
professional group.

Parameters often have a gaussian distribution, You then can expect typ
values as the center of the bell curve and min/max values are cut
offs. With cut off at 1 sigma, about 35 % of the part would be out of tolerance, so cut off is at a higher sigma value, but manufacturers
will not tell you ar what value it is.
--
Uwe Bonnes bon@elektron.ikp.physik.tu-darmstadt.de

Institut fuer Kernphysik Schlossgartenstrasse 9 64289 Darmstadt
--------- Tel. 06151 1623569 ------- Fax. 06151 1623305 ---------

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On 15/12/2024 01:50, John S wrote:

Hi, men -

There are sometimes 3 columns on a datasheet which may contain min, nom,
and max values. Like Vds for example.

Is there any probability tacitly assigned to the values? I know that
nominal is the average, and I assume that the value lies between the two middle one standard deviation points.

1. Is that a valid assumption?

2. Is the min between one and two standard deviations down from the mean?

Thanks and I apologize for asking such a basic question in this
professional group.

It is actually a rather deep and difficult to answer question in general because for some components the answer can be "it depends".

Semiconductors I generally take it to mean ~3 sigma either side of the
mean but design with a bit of extra margin so the 0.5% tail doesn't
cause trouble.

But for some components like resistors and capacitors that may be
obtained in 10%, 5%, 2%, 1%, 0.1% tolerances you can find that the
frequency distribution of the components in the wider tolerance bins
consists of values that are almost *never* inside the narrower ones.
IOW you are guaranteed at least 2% error in the 5% parts.

It is a bit better today than it used to be when they made batches and
then selected from the process output. These days it is all a lot more reproducible and laser trimmed for precision parts.

--
Martin Brown

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Martin Brown <'''newspam'''@nonad.co.uk> wrote:

On 15/12/2024 01:50, John S wrote:

Hi, men -

There are sometimes 3 columns on a datasheet which may contain min, nom,
and max values. Like Vds for example.

Is there any probability tacitly assigned to the values? I know that
nominal is the average, and I assume that the value lies between the two
middle one standard deviation points.

1. Is that a valid assumption?

2. Is the min between one and two standard deviations down from the mean?

Thanks and I apologize for asking such a basic question in this
professional group.

It is actually a rather deep and difficult to answer question in general because for some components the answer can be "it depends".

Semiconductors I generally take it to mean ~3 sigma either side of the
mean but design with a bit of extra margin so the 0.5% tail doesn't
cause trouble.

Nah, all parts nowadays follow their spice models exactly.

Cheers

Phil “alias Dr Tung In Chic” Hobbs


--
Dr Philip C D Hobbs Principal Consultant ElectroOptical Innovations LLC / Hobbs ElectroOptics Optics, Electro-optics, Photonics, Analog Electronics

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On Sun, 15 Dec 2024 13:08:02 +0000, Martin Brown
<'''newspam'''@nonad.co.uk> wrote:

On 15/12/2024 01:50, John S wrote:

Hi, men -

There are sometimes 3 columns on a datasheet which may contain min, nom,
and max values. Like Vds for example.

Is there any probability tacitly assigned to the values? I know that
nominal is the average, and I assume that the value lies between the two
middle one standard deviation points.

1. Is that a valid assumption?

2. Is the min between one and two standard deviations down from the mean?

Thanks and I apologize for asking such a basic question in this
professional group.

It is actually a rather deep and difficult to answer question in general >because for some components the answer can be "it depends".

Semiconductors I generally take it to mean ~3 sigma either side of the
mean but design with a bit of extra margin so the 0.5% tail doesn't
cause trouble.

I can imagine that production test and trim create a nonsymmetric
distribution of some spec, and chop off the tails. And make a hole in
the middle where premium parts have been binned away to sell at a
higher price.

But for some components like resistors and capacitors that may be
obtained in 10%, 5%, 2%, 1%, 0.1% tolerances you can find that the
frequency distribution of the components in the wider tolerance bins
consists of values that are almost *never* inside the narrower ones.
IOW you are guaranteed at least 2% error in the 5% parts.

That doesn't seem to be the pattern nowadays. We buy ultra-cheap 1%
resistors by the reel, and they are really 0.1% resistors. In a
production line, with laser trimming, seems like all the resistors are
right on.

There's no reason to buy surface-mount resistors that are spec'd worse
than 1%. It's easier to design knowing all the various Rs in stock are
better than 1%.

What we pay extra for is low tempco. And thoe are usually way better
than their spec. Tempco is a nuisance to test for, at the PPM level.

Caps are not usually laser trimmed so are not as good. And they have
plenty of other issues that work against accuracy.

It is a bit better today than it used to be when they made batches and
then selected from the process output. These days it is all a lot more >reproducible and laser trimmed for precision parts.

Some part data sheets have histograms, which can hint at what you'll
see in real life.

Abs max voltage ratings are especially interesting. Most silicon
mosfets seem to zener reliably at 1.2x abs max drain voltage. Some
have gate zeners, some don't. Some fets and RF parts are reliable at
2x abs max data sheet voltage.

RF schotttky detector diodes can have silly ratings like 2 volts max
reverse. If you need their absurdly low drop and capacitance, you've
got to test them and break the rules.

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On 15/12/2024 11:44 pm, John R Walliker wrote:

On 15/12/2024 12:11, Uwe Bonnes wrote:

John S <Sophi.2@invalid.org> wrote:

Hi, men -

There are sometimes 3 columns on a datasheet which may contain min, nom, >>> and max values. Like Vds for example.

Is there any probability tacitly assigned to the values? I know that
nominal is the average, and I assume that the value lies between the two >>> middle one standard deviation points.

1. Is that a valid assumption?

2. Is the min between one and two standard deviations down from the
mean?

Thanks and I apologize for asking such a basic question in this
professional group.

Parameters often have a gaussian distribution, You then can expect typ
values as the center of the bell curve and min/max values are cut
offs. With cut off at 1 sigma, about 35 % of the part would be out of
tolerance, so cut off is at a higher sigma value, but manufacturers
will not tell you ar what value it is.

In the case of some TI op-amps there are different grades for parameters
like offset voltage.  They mark the package of the higher spec versions before testing them and then throw away any that don't meet that higher spec.  I was told this by a TI applications engineer.
Apparently it is cheaper to throw away a few op-amps than to have
branches in the production line to cope with different grades.
This does suggest that most devices are much closer to the typical
values than one might expect from the limiting values.
For parameters that take a long time to test the typical values may
be much better than the limit values.
John

Another approach that I have heard of is to test all of the parts to the tightest limits (which it would have been designed to pass), and see
which grade the customers order, only have to keep one pile of stock,
and then mark them with whichever grade the customers ordered and paid
for. I don't know whether this actually happens much. If it does, then
it would not suit the manufacturer for customers to know.

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On 15/12/2024 12:50 pm, John S wrote:

Hi, men -

There are sometimes 3 columns on a datasheet which may contain min, nom,
and max values. Like Vds for example.

Is there any probability tacitly assigned to the values? I know that
nominal is the average, and I assume that the value lies between the two middle one standard deviation points.

1. Is that a valid assumption?

2. Is the min between one and two standard deviations down from the mean?

Thanks and I apologize for asking such a basic question in this
professional group.

Cheers,
John

For anything that has been trimmed (whether or not you know that it has
been trimmed, especially digitally trimmed), assuming that parts will
have a gaussian distribution in parameters is a mistake. If the part can auto-calibrate itself in use, even more so.

Also, batch-to-batch variation often exceeds part-to-part variation
within a batch. They might not know the batch-to-batch variation at the
time when they are writing the datasheet, as there is probably only one
batch. They can run "skew lots" where they ask the fab to deliberately
adjust the process parameters of some wafers to the upper and lower
limits of some parameters, but this is never exhaustive, and so the
limits in the datasheet will likely be sand-bagged (overly cautious) to
some degree.

Also, specs that are not a major selling point would likely be chosen to
be very easy to meet, because they really do not want to be throwing
away parts because of a spec that nobody cares about. For example, if
you buy a low-noise amplifier, you might reasonably expect that the
noise figure specification (which involves a trade-off with power and/or
chip area) is chosen such that they can meet it on say 99% of the
untested parts, so some of them will be only just passing by a margin
equal to the uncertainly of the tester calibration. On the other hand,
if there is a CMOS logic enable pin of the low-noise amplifier which
typically has a leakage of a couple of femtoamps, it might be specced
with a maximum leakage of 10 microamps, just because it would be stupid
to throw out one of these amplifiers if the package was slightly dirty
and leaked a picoamp instead of a femtoamp, and unreasonably expensive
to configure the tester to be able to tell the difference.

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On 16-12-2024 11:23, Chris Jones wrote:

On 15/12/2024 12:50 pm, John S wrote:

Hi, men -

There are sometimes 3 columns on a datasheet which may contain min,
nom, and max values. Like Vds for example.

Is there any probability tacitly assigned to the values? I know that
nominal is the average, and I assume that the value lies between the
two middle one standard deviation points.

1. Is that a valid assumption?

2. Is the min between one and two standard deviations down from the mean?

Thanks and I apologize for asking such a basic question in this
professional group.

Cheers,
John

For anything that has been trimmed (whether or not you know that it has
been trimmed, especially digitally trimmed), assuming that parts will
have a gaussian distribution in parameters is a mistake. If the part can auto-calibrate itself in use, even more so.

Also, batch-to-batch variation often exceeds part-to-part variation
within a batch. They might not know the batch-to-batch variation at the
time when they are writing the datasheet, as there is probably only one batch. They can run "skew lots" where they ask the fab to deliberately
adjust the process parameters of some wafers to the upper and lower
limits of some parameters, but this is never exhaustive, and so the
limits in the datasheet will likely be sand-bagged (overly cautious) to
some degree.

Also, specs that are not a major selling point would likely be chosen to
be very easy to meet, because they really do not want to be throwing
away parts because of a spec that nobody cares about. For example, if
you buy a low-noise amplifier, you might reasonably expect that the
noise figure specification (which involves a trade-off with power and/or
chip area) is chosen such that they can meet it on say 99% of the
untested parts, so some of them will be only just passing by a margin
equal to the uncertainly of the tester calibration. On the other hand,
if there is a CMOS logic enable pin of the low-noise amplifier which typically has a leakage of a couple of femtoamps, it might be specced
with a maximum leakage of 10 microamps, just because it would be stupid
to throw out one of these amplifiers if the package was slightly dirty
and leaked a picoamp instead of a femtoamp, and unreasonably expensive
to configure the tester to be able to tell the difference.

A guy at EEVblog forum has done a lot of measurements on resistors:

https://www.eevblog.com/forum/projects/smd-resistor-distributions/

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