Hydra Computing provides a physics‑based simulation platform that solves Maxwell’s equations for high‑NA lithography using quantum‑inspired tensor‑network algorithms combined with AI.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional electromagnetic simulation methods such as FDTD and RCWA cannot scale to the Angstrom‑level feature sizes required for next‑generation semiconductor masks, creating a computational bottleneck that risks costly design errors and production delays. Probabilistic AI approaches used as shortcuts lack rigorous verification, leading to unreliable predictions.
Solution
Hydra Computing delivers a physics‑based simulation platform that solves Maxwell’s equations for lithography using quantum‑inspired tensor network algorithms combined with AI. By representing complex electromagnetic fields as compressed tensor networks, the engine retains full physical fidelity while dramatically reducing computational cost. The solution provides high‑NA lithography simulation with accuracy comparable to full‑wave solvers, enabling reliable mask design at Angstrom scales. Integrated AI training generates rigorous ground‑truth data for downstream machine‑learning models, improving their predictive reliability. The platform includes built‑in verification steps, ensuring that results can be trusted before committing to silicon. Results are accessible through a software suite with cloud analytics and API integration, fitting into existing design and verification workflows.
Target Audience
Primary customers are semiconductor manufacturers, mask design engineers, and lithography R&D teams that require accurate, high‑throughput simulation of Angstrom‑scale photomasks.
Features
- Tensor‑network based compression of electromagnetic fields that preserves full Maxwell‑equation fidelity
- Rigorous high‑NA lithography solver delivering accurate predictions at Angstrom‑scale feature sizes
- AI‑driven generation of ground‑truth datasets for training and validating downstream ML models
- Scalable performance that surpasses brute‑force FDTD/RCWA solvers, reducing compute time and cost
- Built‑in verification workflow to certify simulation outcomes before tape‑out
- API/SDK integration for seamless incorporation into existing mask design and EDA toolchains
- Cloud‑hosted analytics dashboard for result visualization, trend tracking, and collaborative review