Offering for Material Suppliers
Imagine all you could do with a real-time connection to your customers?
With Athinia™’s ability to safeguard data, device makers are willing to share an unprecedented amount of information with suppliers, unlocking opportunities to improve performance, time to market, and customer satisfaction.
Our secure data ecosystem helps material suppliers:
Understand material impact on device performance and make improvements where they matter
Athinia™’s secure data-sharing platform enables suppliers to have access to key semiconductor manufacturing data they never had before.
This level of transparency provides insight into which parameters matter most in & beyond the norm (i.e., Certificate of Analysis).
Streamline and optimize change notification and other quality processes by predicting materials performance based on historical data
With the ability to collaborate, combine and contextualize data, material suppliers can better understand parameters that matter the most and focus resources to drive performance.
Remain competitive by qualifying new materials sooner, with the possibility of achieving higher margins, and improving customer satisfaction
With access to limitless analytics capabilities, including the most sophisticated AI and machine learning frameworks, suppliers can differentiate based on quality, performance and time to market.
How It Works
Consolidated view of all relevant data
- Combine data from multiple sources, including your quality, manufacturing, and in-process data, powered by Athinia™’s proprietary software and process.
- Data ontology is used to structure the information so it can be easily analyzed easily.
- Obfuscation and normalization of data enable confidentiality.
- Same process completed with device makers to get both parties ready to exchange information securely.
Possibility to analyze materials and in-fab quality performance data
- Material suppliers and device makers can get immediate access to pre-determined single and multi-parameter analysis.
- Creation of regression models for prediction based on historical data.
- Identification of critical parameters beyond the norm (i.e., Certificate of Analysis) that influence in-fab performance.
Take action based on our findings and your insights to improve quality
- Joint definition of changes to critical parameters to help drive performance.
- Measure and monitor the impact of newly implemented actions.
- Use predictive models for future planning based on real-time data.