Semiconductor companies are racing to compete in the highly competitive IoT era, developing products that will collect and transmit sensor data between objects, animals and people for countless applications. From a manufacturing perspective, these companies must also excel in the IIoT (Industrial Internet of Things) by aggregating and analyzing terabytes of data to continually improve product yield, quality and productivity. However, many companies do not have the internal expertise to collect, let alone analyze the billions of data points that are generated from manufacturing operations quickly enough to make impactful decisions.
In his latest video, Optimal+ CTO Michael Schuldenfrei discusses how semiconductor companies can use big data analytics’ ability to collect and share large amounts of test data across multiple test environments significantly improving quality while simultaneously lowering the cost of test.
Michael highlights ways to improve quality while reducing the cost of tests through combining the global data infrastructure with the Optimal+ Exact platform. These highlights include:
- Using Data Feed Forward to share data across multiple test silos and enable product engineers to make more informed decisions on a given device
- Collecting data across all operations to find patterns
- Identifying relationships across test environments and using this data to identify algorithms to detect outliers and improve yield, quality and productivity
- Combining data across different geographies to create an adaptive test environment
To learn more about how big data analytics are impacting high volume manufacturing operations, check out Michael’s complete video on YouTube.