Analytics 101

How End-to-End Analytics Are Becoming Useful for Engineers

Engineers need to understand how manufacturing is progressing. For that, they need data points to get accurate outcomes out of testing. The earlier you get the results, the faster you detect the issues and implement the changes. Previously, most of the data was siloed. However, upcoming feed-forward applications have eased the decision-making process with end-to-end analytics. Connected data sources enable them to make the process better and more relevant. In this Semiconductor Engineering, Annie Meixner shares how engineers should use end-to-end analytics for better semiconductor manufacturing outcomes.

Where End-to-End Analytics Is Useful

Learn About the Usage

A semiconducting processing unit progresses chronologically. ‘Upstream’ is the early production phase, while ‘downstream’ denotes the later stage. Simply having all the data in one basket will not help. You must make connections. With end-to-end analytics, you have the power to analyze and get the right directions for process manufacturing.

Leverage End-to-End Analytics for Semiconductor Manufacturing

In the manufacturing sector, engineers typically use feed-forward data analysis. “Product test engineers were early adopters of the technology, and they successfully use wafer level test data to determine the best subsequent test or assembly choices, while skipping the long burn-in test,” says the author.

Connect the Dots

Data tags enable engineers to track the data sources each manufacturing phase generates. It might be possible to have different naming conventions across the supply chain. However, using the same terms can help engineers to track equipment better by tracing the wafers and testing history. So, you need a good traceability parameter for better end-to-end analytics. Losing the trail can lead to unaccounted defects and flawed testing scenarios.

“The promise of end-to-end analytics is predicated on data integrity, use cases, and connectivity between the various data sources,” says the author. Use cases can uphold the data-building process. However, engineers must still keep an eye out for changing customer demands and profits.

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