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Government of Canada – DTPR AI Register

AI-Assisted Prediction of Semiconductor Fabrication Outcomes

Research & Development

What it collects

Operational data
Anonymized data
  • Semiconductor design layout files (e.g. photonic circuit designs) submitted by researchers for virtual fabrication trials. These are engineering schematics, not personal data.
About a measurement
Anonymized data
  • Fabrication measurement and imaging data from Applied Nanotools Inc., including measurements of fabricated semiconductor components used to train and validate the model.
Run by
National Research Council Canada (NRC)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Not stated by the Helpful Places.
Your copy
You cannot see the data it holds about you. What you can do

What it is for

Research & Development

This system is a machine learning model developed by the National Research Council Canada in collaboration with McGill University. It predicts how semiconductor component designs will behave after physical fabrication, allowing researchers and designers to run virtual trials and refine designs before sending them to a manufacturer. The system uses internal research data and fabrication data from Applied Nanotools Inc. It is used by Government of Canada employees and does not process personal information.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Semiconductor design layout files (e.g. photonic circuit designs) submitted by researchers for virtual fabrication trials. These are engineering schematics, not personal data.
About a measurement
Anonymized data
  • Fabrication measurement and imaging data from Applied Nanotools Inc., including measurements of fabricated semiconductor components used to train and validate the model.

Processing

Classification & Prediction
  • A deep learning model that predicts fabrication outcomes — specifically how imperfections introduced during physical manufacturing will affect the optical performance of semiconductor components. Outputs are predicted performance metrics for a given design layout.

What it does

Deciding (Analytical AI)
Human decides
  • The model predicts the fabrication outcome (optical performance) of semiconductor design layouts. Designers review the predictions and decide whether to proceed to manufacturing, making this a human-decides mode.

Outputs

A recommendation or prediction
Anonymized data
  • Predicted fabrication outcomes for submitted semiconductor design layouts — specifically optical performance metrics accounting for expected fabrication imperfections. Designers use these predictions to refine designs before committing to physical manufacturing.

Run by

National Research Council Canada (NRC)
  • The National Research Council Canada (NRC) is the federal government department that developed and deploys this machine learning model in collaboration with McGill University to support semiconductor research.

Government of Canada AI Register — Virtual semiconductor fabrication model

Built by

Not stated by the Helpful Places.

Kept for

Not stated by the Helpful Places.

Shared with

Not available to me
  • The system is used exclusively by Government of Canada employees and does not expose data or outputs to members of the public. AI use is not disclosed to users outside the NRC research context.

Stored

Not stated by the Helpful Places.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the Helpful Places, not the vendor.

Your rights

  • Right to Algorithmic TransparencyThe system is used internally by NRC employees. The AI register entry is publicly available at open.canada.ca. No formal mechanism is described for members of the public to request additional information about the system's logic.

Risks and safeguards

No risks or safeguards have been published for this system yet.