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Method for Identifying Outliers and Testing and Recovering Failed Modules

IP.com Disclosure Number: IPCOM000232641D
Publication Date: 2013-Nov-22
Document File: 3 page(s) / 63K

Publishing Venue

The IP.com Prior Art Database

Abstract

A method is disclosed for identifying outliers and improving the quality of shipped products. The method also identifies root causes, implements corrective actions, and recovers failed modules.

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Method for Identifying Outliers and Testing and Recovering Failed Modules

Outlier Identification and Management System (OIMS) identifies process and defect anomalies, improves process controls, and improves end-user quality and reliability and to identify the root cause of an outlier product. OIMS test limits represent the application of statistical techniques for the removal of abnormal parts during part level testing. OIMS uses statistical techniques to establish the limits on these test results, wherein the supplier needs to establish a minimum and maximum acceptable yield at critical process steps (at least one) by product or product family.

A method is disclosed for identifying outliers and improving the quality of shipped products. The method also identifies root causes, implements corrective actions, and recovers failed modules.

The method defines criteria for identifying module lots which are outside the normal production distribution and should be held for evaluation by an elimination team.

In accordance with an embodiment, a number of criteria are used to identify an outlier product. Examples of outlier criteria are, for example, percentage of good yielding modules, percentage of AC screen failures, percentage of PLL failures, percentage of IO parametric failures and percentage of sort failures. Outlier material may be scrapped or kept on hold for unique processing. The criteria are used on a technology, product, and PN basis.

The method can also determine a new/update module maverick limit. The limit determination is performed statistically using 3 sigma limits or using affordability criteria, such as 1% rule. An example for a new limit is shown in fig. 1.

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Figure 1

The procedure for determining a maverick begins with testing the lots and applying the results against disposi...