Corinne Bergès, Six Sigma Black Belt, NXP Semiconductors Kurt Neugebauer, Analog Design Engineer, NXP Semiconductors Da Dai, Design Automation Engineer, NXP Semiconductors Martin Kunstmann, R&D-SUP-Working student, NXP Semiconductors Alain Beaudet, Product and Test Engineer, NXP Semiconductors
Structured Problem Solving (SPS) is one of the three pillars of NXP Six Sigma system, with Quality Culture and Continuous Improvement, and demonstrates still more NXP Quality system maturity. Some key approaches in NXP SPS are fitting with the DMAIC/DMADV, 8D or 5-Why frameworks. They widely use statistics to change assumptions into evidences, necessary for a real defect root cause elimination: modeling, DOE, multivariate analysis, …Two specific statistical analysis are described. In design for automotive, about simulation of parametric, hard or soft defects, purpose is to implement the best algorithm to reduce number of simulations, without impacting test coverage or failure rate estimation precision: for this, JMP provides interesting options in clustering. NXP experiments will result in an algorithm and in some recommendations for the new IEEE standard on study about defect coverage accounting method. Now, downstream in manufacturing, when it deals with capability index computation, and with normality test, to bypass high sensitivity of these tests for a slight abnormality, a methodology was designed in JMP to quantify shift from normality, by using the Shash distribution and its Kurtosis and Skewness parameters. A script was implemented to automate it on the more than 3000 tests for an automotive product.
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Level: Intermediate Job Function: Analyst / Scientist / Engineer Corinne Bergès, Six Sigma Black Belt, NXP Statistical studies have shown that a country's economic wealth is largely based on equality among economic actors. In France, INSEE national institute is in charge of male-female salary inequality studies, using Oaxaca and Blinder’s one methods, which are conclusive on very large samples. But what about small samples from one company? This e-poster originates from a study conducted on NXP France within the framework of its human resources equality policy. The study used JMP Pro. In this presentation, for reasons of confidentiality, the same study was run on new data (500 employees). Relying on the framework of the INSEE method, male and female wage data are first modeled by two different models. Then the models are compared. Next, the male model is applied on women's wages. We see that implementation of this method on a small sample has limits in the choice of factors entering the model and in the under-representation of certain levels of these factors. This prompts us to make a statistical study of male and female distributions in the key factors. Regardless of the possible conclusions from this study on wage inequalities within this company, JMP Pro has demonstrated its perfect functionality in social data studies.
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Corinne Bergès Six Sigma Black Belt for Analog & Sensor Business Quality Group, PhD NXP Semiconductor SAS
Jmp PRO degradation analysis platform can be used for different purposes. We would like to present two case studies in the automotive semiconductor test.
In a first example, power mos are tested at customer’s site and electrical measures monitor component behavior in ten points in a very short time period. To help our customer to understand the breakdownoot cause of a part of the mos components, ten-points curve of these measures are compared between the not-failed parts and the failed ones: the jmp tool used for that was the degradation analysis platform.
Another case study for that same platform was a drift analysis on High Temperature Operating Life Test (HTOL) values. The test was initially performed during 2000 hours but a later request was about a 4000 hour test duration. Platform degradation analysis allowed to avoid an additional costly test: it found several families of curve shape among the 1200 tests and succeeded to predict the ones for them the drift at 4000 hours was likely to be superior at 10%, maximal admissible value.
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