The unrelenting pressure for semiconductor manufacturers to get a high quality, competitively priced product tested and to market on time is a continual reality. It is not a static scenario; the need to innovate and update processes is constantly ongoing due to device proliferation and market innovation.
Device characterization is an area of process and product innovation where the ability to collect and manage large amounts of data can directly shorten a device’s time to market. Characterization is normally performed on relatively few devices, but each individual device can be tested tens or hundreds of times per part, which creates an opportunity to use a big data solution to source, manage and analyze this aggregated data more effectively.
Fortunately, big data solutions are inherently more efficient in overcoming the painstaking challenge of frequent and manual characterization testing, but these solutions need to extend beyond pure automation benefits – they must also facilitate enterprise-wide collaboration via a shared, transparent, centralized data set. This focus on shared, clean data enables full visibility throughout the organization, resulting in greater characterization precision for future products.
In a recent video, Optimal+ CTO Michael Schuldenfrei highlights how the Optimal+ NPI solution more efficiently collects and manages large volumes of data generated through characterization testing, and specifically, how it directly expedites the semiconductor characterization process for IDMs and Fab companies. This short video reviews:
- Expediting data collection and cleansing
- Using the “Sandbox” to extract, transform and load raw characterization data
- Data management
- Establishing a centralized data set hub for data collection, management, storing and analysis
To learn more about how big data analytics is speeding up the semiconductor characterization process, check out this video