AI-Accelerated Semiconductor TCAD SimulationNew

Background

Advanced nodes and 3D-IC stacking push thermal and electrical coupling to the limit. TCAD solves drift-diffusion or Monte-Carlo transport at device level, but full-chip design sweeps need thousands of such solves - too slow for interactive design loops.

Approach

• PhysicsNeMo TCAD examples: train surrogates on parameterized device simulations
• Inputs: geometry/doping profiles, bias conditions; outputs: field/current distributions or scalar figures of merit
• Deploy the surrogate inside design-space exploration, reserving TCAD for final verification

Why it matters

• Interactive design loops instead of overnight batch runs
• Consistent sensitivity information across the design space
• Pattern extends to power devices, memory macros, and interposer thermal studies

Use cases

• Early-stage device sizing with surrogate feedback
• Yield-aware corner analysis across process variation
• Packaging-level thermal co-exploration with device-level fidelity

Resources

• PhysicsNeMo examples/tcad with runnable configurations
• NVIDIA developer blog articles on TCAD and electronics cooling surrogates