1. Assess what you have
We structure your existing runs, metrology and tool logs, including failures, and report what they can already answer.
2. Model the process
We build surrogate and physics-informed models linking process variables to the properties you care about.
3. Design fewer experiments
Bayesian experimental design selects each next run to maximize information.
4. Hand over the window
You receive a process window map, the model, and ranked setpoint recommendations with honest uncertainty. Yours to keep and reuse.
© 2026 Mina AI Labs LLC. General information only; not professional advice.