Crystal growth, without the guesswork.
Starting with predictive maintenance: we catch thermocouple drift and degradation before it costs you a run.
Member of NVIDIA InceptionMost growers aren't ready to adopt off-the-shelf simulation software, and they shouldn't have to be. Before a twin can predict anything, the data beneath it has to be trustworthy. That's the work we do with you, and every layer is useful on its own.
What-if runs and predictions your team uses.
See during a run whether it's on track, while there's still time to act.
Learn what normal looks like for each heater, thermocouple and sensor, so drift is caught before it costs a run.
Clean pipelines, data quality, validation and the infrastructure to keep it running.
That's why we start with discovery, not a demo.
How we workGrowing a crystal a particular way means holding a gradient to fractions of a degree. When a thermocouple reads half a degree off, the furnace holds a gradient nobody chose, the crystal cools along a different profile, and the run is lost. We find those errors by reading how your thermocouples behave relative to each other, across millions of past readings and the thousands arriving from the run in progress. This is the work we deliver today.
Vertical scale exaggerated. Real drift of this kind is a fraction of a degree and does not show on a furnace readout.
Drift is inherent to thermocouples. It is gradual, and small enough that no engineer can track it by hand across every sensor and every run, so it stays invisible until a run comes out wrong or a furnace stops unexpectedly.
Half a degree of error is enough to change how the crystal cools. The controller corrects toward the wrong number, the screen still reads the setpoint, and the real gradient moves away from the one your recipe calls for.
Each sensor on its own looks healthy. What moves first is how the sensors sit relative to one another: which reads warmer, by how much, at which stage of the run. Machine learning and deep learning models weave together millions of readings from your run history and the thousands coming off the furnace now, and surface the subtle shifts in those relationships. Drift becomes a number you can act on and a replacement you can schedule between runs.
A growth run takes weeks to months, and for most of that time the only thing you can see is instrument data. We use that data to watch the growth itself, so problems surface during the run instead of at metrology.
Metrology tells you what happened once the boule is out. Process data can tell you while the furnace is still running.
Models learn the shape of a healthy run from your historical process data, then compare the run in progress against it.
A signal that a run is drifting off course is only worth having while there is still time to respond to it.
We help material suppliers produce semiconductor materials that let their customers build more performant, more cost-effective devices. Better material upstream means better products downstream.
A high-quality substrate is the foundation every chip is built on. When that foundation is strong, you can build far more complex structures with far less chance of failure.
Where the digital twin fits
The twin we are building aims to improve that foundation before a single ingot is grown, testing process changes in simulation so substrate quality is more predictable run to run.
A pilot is scoped small on purpose. We work alongside your engineers, on your furnaces and your data, for as long as the phase takes. This is not software you install and are left to run. You set the criteria before it starts, and every phase leaves your team something it keeps.
We work with the data your furnaces already produce. No new sensors, no rip and replace.
We establish what normal looks like for each furnace and each sensor, and report what the data shows.
Drift detection and run monitoring your team can act on, measured against criteria you set.
What works on the pilot furnace rolls out to the rest, and to the next capability.
We talk through one furnace: what you grow, what you already measure, and where runs go wrong. You leave knowing whether step 01 is worth doing and what it would involve. No preparation needed from your team.
