arXiv:2606.29272v1 Announce Type: new Abstract: Technology computer-aided design (TCAD) semiconductor device simulation is fundamentally constrained by the high computational cost of iteratively solving coupled drift-diffusion equations. Existing ML surrogates either reduce internal physics to macroscopic scalar regressions, or rely on single-step mappings that lack the iterative refinement required to resolve stiff, coupled fields. To address this, we introduce PCGD, a Physics-Guided Conditional Graph Diffusion framework operating natively on unstructured TCAD meshes to predict coupled electr
The increasing computational demands of semiconductor design and manufacturing necessitate more efficient simulation methods to sustain advancements in Moore's Law and beyond.
Improving TCAD simulation efficiency directly impacts the speed and cost of semiconductor development, a critical bottleneck in the compute supply chain and global technological leadership.
This new physics-guided AI approach offers a path to significantly reduce the computational cost and time required for simulating complex semiconductor devices, potentially accelerating chip design cycles.
- · Semiconductor device manufacturers
- · AI compute infrastructure providers
- · Electronic Design Automation (EDA) companies
- · Advanced computing sectors
- · Traditional TCAD simulation vendors (without AI adoption)
- · Companies reliant on conventional, slower design cycles
Faster and cheaper semiconductor design leads to more frequent and optimized product iterations.
Accelerated chip development could intensify competition and innovation in AI hardware, quantum computing, and other advanced tech fields.
Reduced R&D costs for chips could lower the barrier to entry for new hardware startups, diversifying the compute supply chain.
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Read at arXiv cs.LG