Close

Presentation

N3Litho: A GPU-Native Numerical-Neural Framework for 3D Mask Modeling, 3D Propagation, and 3D Aerial Image Simulation
DescriptionAccurate and efficient lithography simulation is critical for virtual fabrication and shift-left manufacturing.
While thin-mask models and single-plane aerial images suffice at older nodes, advanced nodes require 3D in-resist aerial images to capture depth-dependent effects from mask thickness, sidewalls, and multilayer stacks.
Rigorous EM solvers provide this fidelity but are too slow for ILT or full-chip use, and thin-mask learning methods fail to model the underlying vector field needed for 3D propagation.
This paper introduces a new paradigm that reconstructs the full 3D in-resist vector field from the image-plane vector field and 3D mask/film-stack parameters.
Based on this insight, we develop N3Litho, a GPU-native numerical-neural framework that couples a GPU-accelerated vectorial Abbe solver with an NGP-based multi-resolution hash backbone for fast 3D field reconstruction.
The method achieves near-EM fidelity with orders-of-magnitude speedup, and the resulting 3D aerial images can be directly used by resist models, OPC/ILT pipelines, and hotspot detection flows