Presentation
Late Breaking Results: Power Mesh Construction for 3D-IC with Backside Power Delivery
DescriptionA 3D-IC architecture packs a design with enhanced functionality
and density into a small footprint while improving performance
and lowering costs. By leveraging through-silicon via (TSV) and
die-to-die bump technology, power can be efficiently delivered to
the top and bottom dies. Moreover, advanced backside power deliv-
ery technology enables a more streamlined power delivery network
for 3D-IC. However, the deployment of TSVs and power/ground
bumps while achieving fast and accurate verification further com-
plicates power delivery network (PDN) construction. To tackle this
challenge, in this work, we collect golden IR drop results from com-
mercial tools for different configurations and densities on TSVs,
backside metals, die-to-die bumps and C4 bumps then apply and
compare with the efficient tree-based methods (Random Forest and
XGBoost) and deep neural networks (U-Net and Inception U-Net).
In saving runtime, compared to heuristic manual approach, Ran-
dom Forest can achieve a 5.8𝑋 speedup (including commercial tool
run time). In reducing the power mesh metal ratio, we devise an
Inception U-Net combined with image rotation to generate end-
to-end IR-drop prediction results for 3D-IC, achieving an MSE of
0.002/0.005 (bottom/top die), 𝑅2 of 0.97/0.95 (bottom/top die), saving
7.6% metal usage, and delivering more than a 5% improvement in
IR-drop results, which are very close to the golden results reported
by commercial tools. The experiments are conducted on industrial
homogeneous design integrations manufactured by a 3nm process.
Our results show that our approach can quickly and accurately
predict IR drop, establish a PDN with small resource usage, and
greatly reduce the time for back-and-forth verification.
and density into a small footprint while improving performance
and lowering costs. By leveraging through-silicon via (TSV) and
die-to-die bump technology, power can be efficiently delivered to
the top and bottom dies. Moreover, advanced backside power deliv-
ery technology enables a more streamlined power delivery network
for 3D-IC. However, the deployment of TSVs and power/ground
bumps while achieving fast and accurate verification further com-
plicates power delivery network (PDN) construction. To tackle this
challenge, in this work, we collect golden IR drop results from com-
mercial tools for different configurations and densities on TSVs,
backside metals, die-to-die bumps and C4 bumps then apply and
compare with the efficient tree-based methods (Random Forest and
XGBoost) and deep neural networks (U-Net and Inception U-Net).
In saving runtime, compared to heuristic manual approach, Ran-
dom Forest can achieve a 5.8𝑋 speedup (including commercial tool
run time). In reducing the power mesh metal ratio, we devise an
Inception U-Net combined with image rotation to generate end-
to-end IR-drop prediction results for 3D-IC, achieving an MSE of
0.002/0.005 (bottom/top die), 𝑅2 of 0.97/0.95 (bottom/top die), saving
7.6% metal usage, and delivering more than a 5% improvement in
IR-drop results, which are very close to the golden results reported
by commercial tools. The experiments are conducted on industrial
homogeneous design integrations manufactured by a 3nm process.
Our results show that our approach can quickly and accurately
predict IR drop, establish a PDN with small resource usage, and
greatly reduce the time for back-and-forth verification.
Event Type
Late Breaking Results
TimeMonday, July 276:27pm - 6:30pm PDT
LocationExhibit Hall
Similar Presentations