Records buyers look for
- Process recipes linked to run results
- Yield, defect and failure analyses
- Characterization and metrology data
- Equipment maintenance and refurbishment records
- Lab notebooks and experiment reports
Why AI buyers want them
Process knowledge in semiconductors and materials is valuable and almost never public. Records that connect inputs, settings and measured outcomes are what scientific AI models need.
What to check first
- Customer-owned process IP and foundry data usually need exclusion.
- Export-control rules may apply to some technology.
- Trade secrets you want to keep should be scoped out.
You’re likely a fit if
- You own your processes or materials know-how
- Run data and results are stored together over years
- Export-control and customer-IP limits are understood
Frequently asked questions
Won’t licensing data expose our trade secrets?
Only if you include them. Scope the licence to what you’re comfortable sharing and restrict onward use in the contract.
Do equipment refurbishers qualify?
Often. Repair, calibration and refurbishment histories can be distinctive technical records.
How can engineering teams license semiconductor research collections?
Scope a defined set of experiments, clear customer IP and export-control limits, then license a de-identified copy. See how to license research data collections.
Do export controls apply to process data?
They can, for advanced-node and specialist materials technology. Classify records before licensing; see export controls and AI data licensing.