Yield Loss Issue for an RFFE Product: Data Analytics and Machine Learning for Root Cause Searching (2022-EU-PO-960)
Dec 17, 2021 02:20 PM
| Last Modified: Mar 6, 2022 08:31 AM
Corinne BERGES, Six Sigma Master Black Belt, NXP Semiconductors D. Martin Knotter, Six Sigma Master Black Belt, Technical Director, Product Diagnostic Center (PDC), NXP Semiconductors Maresa Labellarte, Product Engineering, NXP Semiconductors
For a new WLAN product (sixth generation Wi-Fi), a yield loss issue is observed at the final test, when dies are packaged for shipment to customers. A project is launched to find the issue root causes. The main quality tool used is a fault tree analysis to dig deeper into each potential failure mode, without excluding some failure possibilities or jumping directly into an a priori conclusion.
NXP has been using machine learning methodology and algorithms from some years now, so machine learning was implemented in this yield loss case in parallel with typical data analytics or univariate analysis.
The final product is constituted by two dies, mounted on a laminate. No issue was observed at the die test step after die manufacturing and before its packaging. Furthermore, the yield loss issue was observed on the product model manufactured only on one type of laminates, which led to hypotheses on the laminate type impact.
Data analytics and machine learning analysis (partition, boosted trees or bootstrap models) were built. The analysis mainly dealt with the difference between the unit test and the final product test and on the difference in the laminate models.