Hello,
I
am
Laurence
Montagner.
I'm
working
for
Microchip Technology Rousset.
I
am
in
Aerospace
&
Defense
Group.
I'm
going
to
show
you
how
we
use
JMP
to
develop
a
predictive
single
event
latchup
model.
This
project,
called
SELEST,
started
four
years
ago
to
develop
an
internal
SEL
prediction
tool.
This
work
was
funded
by
the
CNES,
the
French
space
agency.
Two
posters
were
presented
at
RADECS
conferences
in
2019
and
2021.
In
the
first
poster,
it
was
a
new
approach
and
the
feasibility
of
using
an
analytical
model.
That's
what
we
are
talking
about
today
and
that's
how
I'm
going
to
present.
The
second
poster,
it
was
with
a
more
accurate
model
using
a
neural
network
approach.
The
context,
to
address
the
new
space
market
that's
been
low
cost,
that
means
that
our
circuits
are
going
to
be
launched
in
low
Earth
orbit.
We
are
going
to
reuse
COTSâ
circuits
on
the
shelves
âto
make
them
radiation
tolerant
to
meet
space
agency
specifications.
We
need
to
analyze
a
lot
of
product
to
know
if
we
can
make
them
[inaudible 00:01:33]
tolerant.
W
e
need
a
model
and
a
predictive
tool
to
gain
the
time
and
money
before
any
experimental
tests.
When
they
are
sent
in
space,
those
circuits
are
under
radiations.
We
have
several
source
of
radiations:
the
sun,
cosmic
rays,
and
Van Allen belts.
The
sun
and
cosmic
rays
emit
electron,
protons,
and
ions.
As
for
Van Allen belts,
the
inner
belts
emit
proton
and
the
outer
one
emits
electrons.
Those
particles,
when
they
strike
our
circuits,
causes
damages.
We
have
two
family
of
damages.
One,
the
TIDâ
the
total ionizing doseâw
e
don't
talk
about
it
today.
We
are
going
to
focus
on
SEE,
single event
effect.
We
are
going
to
focus
more
specifically
on
single event
latchup.
This
single event
latchup leads the
component
to
the
destruction.
That's
why
we
need
absolutely
to
predict
this
phenomenon.
The
mechanism
on
the
single
event
latchup
is
very
similar
to
the
electrical
latchup,
but
it
is
not
provoked
by
the
same
causes.
In
the
single
event
latchup,
this
is
when
heavy
ions is
striking
sensitive
devices
as inverters
[inaudible 00:03:33]
.
In
the
worst
case,
it
is
striking
in
the
middle
of
these
devices,
and
it
triggered
a
parasitic thyristor,
composed
here
of
an
NPN
and
PNP
bipolars.
When
the
supply
of
the
circuit
is upper,
that's
the
Vhold
of
this
parasitic
thyristor.
At
that
time, the thyristor
is
still
on,
and
we
can
lead
to
destruction.
As
you
have
understood,
this
parameter
of
Vhold
is
very
important
for
us.
I
just
talked
about
energy,
the
energy
of
the
heavy
ions.
There
is
a
physical
quantity very
important,
and
is
criteria
of
a space
agency.
This
is
the
LET,
linear
energy
transfer.
This
is
amount
of
energy
lost
in
the
matter
by
unit
track
lengths.
For
ESA,
European
Space
Agency,
circuit
is
said
to
be
immune
to
latchup when
this
value
is
above
60
mega-electronvolts centimeters square
per
milligram.
Our
objective
is
to
be
able
to
predict
the
V
hold
and
the
LET threshold
of
a
circuit, so
we
need
to
build
a
model.
We
use
TCAD
Sentaurus to
run
some
simulation
to
build
the
model
with
a
DOE.
For
the
DOE,
we
need
to
define
the input
and
outputs.
As
you
have
guessed
output,
V
hold
and
LET threshold.
Regarding
input,
for
the
first
try,
we
decide
to
define
four
output,
two
from
the
process,
epi
thickness
and
epi do
se,
and
two
from
the
design,
the
length
between
the
two
well of
the
test
structure
of
the
inverter,
and
the
length
between
the
top
and
the
well
.
Keep
in
mind
that
if
the
Vhold
obtained
by
simulation
is
upper
of
the
supply
of
the
circuit,
at
that
time,
the
circuit
is
immune
to
SEL.
If
the
Vhold
is
inferior
to
the
supply
of
the
circuit
at
that
time,
it
is
possible
to
have
a
single
event
latchup,
and
we
are
very
interested
to
know
the
LET threshold.
In
the
flow,
we
are
going
to
build
a
DOE
with
JMP,
a
Full
Factorial
DOE.
We
are
going
to
input
this
DOE
in
the
TCAD
Sentaurus.
We
are
going
to
run
our
simulations.
We
are
going
to
take
the
output,
Vhold and
LET threshold.
We
put
all
results
and
input
in
JMP.
We
are
going
to
screen
data
and
build
a
model
with
JMP.
Now,
let's
go
to
a JMP
data
table
that
we
use
to
study
the
feasibility
of
using
an
analytical
model.
This
is
the
table
with
our
four
input
and
two
output
here,
and
the
full
DOE,
one
value
per
color.
To
input
here,
we
can
see
a
preview
of
what
we
have
in
each
distribution.
To
have
a
better
display
and
have
a
better
exploration
of
data,
I
begin
by
putting
distribution
of
input
and
output.
We
can
check
that
for
each
input,
we
have
the
same
number
at
each
value
in
each
input,
so
it
is
okay.
We
can
go
at
the
end,
and
we
can
see
that
for
the
output,
we
have
something
to
note
quickly,
that
the
V
hold
is
inferior
to
the
supply
of
the
circuit,
which
is
1.95.
I
can
put
the
limit
on
the
graph.
We
can
have
a
single
event
latchup.
We
have
the
value
of the LET threshold in here.
What
we
see
that
some
value
are
upper that
100.
We
can
highlight
it
to
see
if
we
see
something
special
on
our
other
input.
We
can note
quickly
that
the
Body
Ties
for
one
value,
whatever
other
input
value,
are
the
source
of
the LET
threshold
upper of 100.
If
we
can
see
upper
value
for
the
LET
threshold
upper
of
60,
there
is
not
the
same
effect,
maybe,
for
the
EpiThick.
With
this
first
analysis,
we
can
explore
our
data
and
have
an
idea
of
what
we
have.
Second
graph
to
be
plotted
for
this
analysis.
It's
a
very
interesting
plot.
Variability/A
ttribute Gauge
chart.
We
are
going
to
plot
all
our
input
in
X,
and
in
Y,
our
output.
Now,
we
can
analyze
first
if
we
are
main
effect
or
interaction.
We
can
connect
cell
means,
and
we
can
see
that
there
is
maybe
a
problem
on
the
T
CAD
results.
It
is
not
a
problem.
We
can
continue
our
analysis
and
study
to
know
if
it
is
feasible
or
not
to
have
the
analytic
model
because
it
is
just
one
dot,
but
we
must
note
what
happened
here.
We
saw
it
quickly
with
this kind of
graph.
If
there
is
a
problem
on
our
foot,
if
we
have
all
our
data
for
each
condition.
We
have
all
data,
but
this
one
is
to
be
analyzed.
As
for
the
Vhold,
there
is
no
problem
on
our
output.
We
can
see
that
what
we
know
of
the
physics,
that
when
the
spacing
between
the
two
wells
is
higher,
the
Vhold
is
increasing.
This
result
is
consistent.
If
we
see
at
the
BodyT
ie,
the
Body
Tie
is
increasing,
the
Vhold is
decreasing.
That's
what
we
know,
too.
Here
we
can
see
what
we
have
already
remarked
on
the
previous
analysis.
Here,
for
this
value
of
Body
Tie,
for
the
LET threshold,
we
are
at
a
value
of
100
mega-
electronvolt.
That's
what
we
already
noted.
We
have
a
trend
that
when
the
[inaudible 00:12:07]
is
increasing,
the
LET threshold is
increasing,
and
it
is
in
agree
what
we
know.
It
is
interesting.
We
can
say
that
with
this
graph
on
Vhold
,
we
have
a
main
effect
of
the
[inaudible 00:12:29]
.
We
can
keep
the
same
graph,
same
analyzing,
but
by
changing
the
order
of
the
input.
Re
call
, better.
Remove
this
input,
recall,
and
we
can
have
the
graph.
What
we
can
see
on
this
graph,
this
representation,
by
just
changing
the
order
of
the
input,
that
the
EpiD
ose,
for
some
value
of
other
input
have
no
effect
for
some
condition
here.
So
we
can
deduce
there
is
interaction
between
inputs.
For
LET threshold
, an
interaction
slightly
here
and
no
effect
for
other
value.
Now,
we
have
checked,
we
have
no
problem
on
our
data.
We
have
seen
we
have
some
main
factors.
We
can,
by
curiosity,
use
another
tool.
It
is
a p
artition
to
know
which
input
is
the
first
to
appear.
Then
on
the
LET threshold,
what
we
see,
so
Body
Tie
first,
that's
what
we
have
seen,
and
the
EpiThick.
We
can
continue
by
[inaudible 00:14:24]
BodyT
ie,
too.
Body
Tie
is
very
important.
As
for
Vhold,
the
BodyT
ie
is
important.
A
fter
the
spacing isâ¦
Now, we
have
a
more
accurate
idea
of
what
we
have
in
our
data,
we
can
go
and
build
our
model.
We
put
our
input.
I
don't
know
what
I
have
done.
I
remove our
input.
We
have
run
a
Full
Factorial DOE
.
We
try
this
model.
We
put
our
output,
and
we
can
run
it.
We
can
run
the
model.
We
can
see
that
I
missed
something
here.
I
already
prepared
something.
Fit
Model,
EpiDose
,
Macros,
Full
Factorial.
Okay, run.
It's
better
now.
Or
is
something
wrong?
I
prefer
this
presentation
with
this
plot,
Actual
by
Predicted
Plot.
In
this
plot,
we
can
see
that
the
model
is
not
really
satisfying
because
we
have
a
lot
of
dots
far
from
the
red
area,
even
if
the
R-s
quared
is
at
0.81,
it's not fully
satisfying.
For
the
Vhold,
we
have
the
same
remark.
We
can
have
a
look
at
the
Profiler.
The
Profiler
can
show
us
the
good
trend,
but
we
are
not
going
to
have
a
long
time
on
this.
We
are
going
to
look
at
another
model
to
know
if
we
can
have
an
accurate
model
than
this
one.
We
are
going
to
try
another
one.
Not
so
far.
We
are
going
to
take
our
input,
and
we'll
try
Response
Surface,
and our
output,
and we
run
it.
We
are
going
to
remove EpiDose
because
there
is
no
effect.
We
can
see
that
this
model
is
better,
the
R-s
quare
is
better.
The
V
hold
is
a
good
model.
Now,
we
have
a
look
at
the
Profiler.
The
Profiler, the trends
are
good
for
all
parameter.
We
note
that
there
is
maybe
something
to
do
because
we
see
in
this
part
of
the
curve,
the
LET threshold
value
are
upper,
and
it
is
not
in
agree
with
the
physics
and
what
we
know,
of
course.
As
I
have
already
said,
it
is
a
first
try.
We
need
to
work
and
work
as
we've
done
on
the
inputs
and
accuracy
of
the
LET threshold to
have
better
results
here.
You
can
see
now
in
the
EpiDose,
we
have
quite
no
variation,
a
little
variation
here.
However,
this kind of
graph
is
very
interesting
for
us
because
when
we
want
to
make a
radiation
tolerant
circuit,
we
can
use
it
to
help
us
by
given
a
set
of
value.
We
can
use
here
the
Desirability
Function.
Now,
we
are
going
to
set
our desirabilities.
Here,
we
want
to
be
upper than
60 as
a
criteria
of
ESA.
I
put
60,
80,
and
100,
match
target.
Here,
for
the
Vhold,
we
want
to
maximize
it.
I
put
this
value,
2, 2.05.
Now,
I'm
going
to
maximize
desirability
.
Of
course,
it
works. And n
ow, not.
Match
target.
Target
.
Now,
maximize
this
desirability.
Now,
we
have
a
set
of
value
we
wish
because
here,
we
have
a
range
of
value
in
the
Body
Ties here.
We
can
play
with
it.
Here,
we
can
play
here
with
the
spacing.
The
other
representation
and
the
tool
we
like
using
in
JMP
is
the
Contour
Profiler
with
some
fixed
parameter.
We
can
have
a
contour
plot
here.
If
you
want
to
change
a
design
value,
I
put
in
axis
the
Body
Tie
and
the
Spacing
_AC.
We
are
going
to
put
our
famous
value
of
60
for
the
LET,
the
Lo L
imit
at
60.
To
have
a
LET
upper
than
60,
we
know
we
can
have
a
value
of
Body
Tie
up
to,
if
we
take
the
cross
here,
about
6.5.
We
can
use
the
range
of
the
spacing.
This
tool,
this
representation,
is
very
interesting
for
us
to
make
our
product
radiation
tolerant.
This
is
not
the
last
model
we
implement,
but
I
can
show
you
that
in
the
red
triangle,
we
can
save
the
prediction
formula
in
the
table
so
that
after,
we
can
take
it
and
encapsulate
it
if
we
want.
Okay,
that's
all
for
me
on
JMP.
Let's
go
back
to
the
presentation.
With
this
method,
we
built
another
model,
this
one
using
a
neural
network.
The
prediction
obtained
with
this
model
was
compared
to a
experiment
on
circuit.
We
can
see
that
when
the
experiment
shows
there
is
no
single
event
latchup,
the
prediction
by
the
SELEST,
the
internal
tool,
said
the
same
thing,
there
is
no
latchup.
When
experimentally
there
is
a
latchup,
SELEST,
even
if
there
is
a
difference
between
experimental
and
prediction,
show
there
is
a
latchup.
For
us,
it
was
a
good
result.
Not
so
accurate.
That's
why
the
work
continue
on
this
model
to
have
an
accurate
model.
That's
why
we
continue
working
on
DOE.
We
do
it
per
technology
node
for
having
a
better
accuracy
of
this
model.
Okay,
thank
you
for
your
attention.
