3D Visualization Revolutionizes Future Chip Designs with NVIDIA and Ansys Collaboration

In
a
groundbreaking
development,
NVIDIA
Omniverse
and
Modulus
are
empowering
Ansys
to
accelerate
3D
simulation
workflows,
a
key
component
in
building
next-generation
semiconductor
systems,
according
to
the

NVIDIA
Blog.

The
Role
of
3D-ICs
in
Semiconductor
Design

Multi-die
chips,
known
as
three-dimensional
integrated
circuits
(3D-ICs),
represent
a
revolutionary
step
in
semiconductor
design.
These
chips
are
vertically
stacked
to
create
a
compact
structure
that
enhances
performance
without
increasing
power
consumption.
However,
as
chips
become
denser,
they
present
more
complex
challenges
in
managing
electromagnetic
and
thermal
stresses.
Advanced
3D
multiphysics
visualizations
become
essential
to
design
and
diagnostic
processes
to
address
these
challenges.

Insights
from
the
Design
Automation
Conference

At
the
Design
Automation
Conference,
a
global
event
showcasing
the
latest
developments
in
chips
and
systems,
Ansys
demonstrated
how
it
leverages
NVIDIA
technology
to
overcome
these
challenges.
Ansys
uses
NVIDIA
Omniverse,
a
platform
of
application
programming
interfaces,
software
development
kits,
and
services,
to
enable
3D
visualizations
of
simulation
results.
This
platform
powers
visualizations
of
3D-IC
results
from
Ansys
solvers,
allowing
engineers
to
evaluate
phenomena
like
electromagnetic
fields
and
temperature
variations
to
optimize
chips
for
faster
processing,
increased
functionality,
and
improved
reliability.

Enhanced
Simulation
Capabilities

With
Ansys
Icepak
on
the
NVIDIA
Omniverse
platform,
engineers
can
simulate
temperatures
across
a
chip
according
to
different
power
profiles
and
floor
plans.
Identifying
chip
hot-spots
can
lead
to
better
chip
designs
and
auxiliary
cooling
devices.
However,
these
3D-IC
simulations
are
computationally
intensive,
limiting
the
number
of
simulations
and
design
points
users
can
explore.

Using
NVIDIA
Modulus,
combined
with
novel
techniques
for
handling
arbitrary
power
patterns
in
the
Ansys
RedHawk-SC
electrothermal
data
pipeline
and
model
training
framework,
the
Ansys
R&D
team
is
exploring
the
acceleration
of
simulation
workflows
with
AI-based
surrogate
models.
Modulus
is
an
open-source
AI
framework
for
building,
training,
and
fine-tuning
physics-ML
models
at
scale
with
a
simple
Python
interface.

AI
Surrogate
Models
for
Real-Time
Results

The
NVIDIA
Modulus
Fourier
neural
operator
(FNO)
architecture
can
parameterize
solutions
for
a
distribution
of
partial
differential
equations.
Ansys
researchers
created
an
AI
surrogate
model
that
efficiently
predicts
temperature
profiles
for
any
given
power
profile
and
a
defined
floor
plan
based
on
system
parameters
like
heat
transfer
coefficient,
thickness,
and
material
properties.
This
model
offers
near
real-time
results
at
significantly
reduced
computational
costs,
allowing
Ansys
users
to
explore
a
wider
design
space
for
new
chips.

Following
a
successful
proof
of
concept,
the
Ansys
team
will
explore
the
integration
of
such
AI
surrogate
models
for
its
next-generation
RedHawk-SC
platform
using
NVIDIA
Modulus.
As
more
surrogate
models
are
developed,
the
team
will
also
look
to
enhance
model
generality
and
accuracy
through
in-situ
fine-tuning.
This
will
enable
RedHawk-SC
users
to
benefit
from
faster
simulation
workflows,
access
to
a
broader
design
space,
and
the
ability
to
refine
models
with
their
own
data
to
foster
innovation
and
safety
in
product
development.

To
see
the
joint
demonstration
of
3D-IC
multiphysics
visualization
using
NVIDIA
Omniverse
APIs,
visit
Ansys
at
the
Design
Automation
Conference,
running
June
23-27,
in
San
Francisco
at
booth
1308
or
watch
the
presentation
at
the
Exhibitor
Forum.

Image
source:
Shutterstock