Applications for machine learning and Artifical Intelligence
What we mean
Together with you we define if your use cases are suitable for application of machine learning and neural nets. Examples are automated classification of defect images and wafer signatures, detection of anomalies and discovery of connections between production related data like material‑, quality‑, sensor‑, tool‑, tracking data.
How we do it
We train you on the necessary base for understanding the algorithm. In workshops we discuss with your process experts and technologists the possible applications. With our ‚Analytic Lab‘ we can test and verify your use cases. Through optimization of algorithm prototypes will be developed. Those can show the general potentials of your applications as well as being feasible for use in production. After successful prove of concept phase application can also be implemented real time.
- Requirement analysis
- Defect Engineering
- Process control methodology
- Yield correlation and prediction, other methods
- Signature- und image analysis
- Machine Learning / Artifical Intelligence
- Personas and interfaces
- Sustainable change
- Convanit Analytics Lab
- Prototyping
- Implementation & Deployment Support