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Introduction
In manufacturing, it was very common to use a golden unit as a reference unit for test development and debug. Multiple aspects such as product requirement, specification, functionality, choice of component and material are important to be considered in order to ensure all the golden unit is as close as it gets to the perfect unit. As an example, a new functional test script may need to be validated, and the golden unit will be used to make sure the new test is working as it should.
The Challenge
The performance of the golden unit may degrade overtime due to contamination, rapid deterioration of wire bond interconnects, etc. Generating a new golden unit as a replacement is a manual and tedious process as it involves sampling actual production units and verifying through trial and error. Additionally, it is not clearly defined what are the margins for concluding a unit in in fact a golden unit. Things gets a little hazy here as we’re looking at a good unit as well as how good it can be manufactured. In this paper, we will introduce a method that both removes any ambiguity and wastage of automatically finding golden units during production.
The Solution
Golden units are not only indicative of production process quality but the ideal product quality as well. With the power of big data analytics, real-time mass amount of manufacturing test data collected during production will be used to identify potential golden units under test. Let’s call these units ‘Potential Golden Unit’.
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