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Data Analytics

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Description

A proliferation of sensors everywhere — from industry to automotive to smart phones — is generating an enormous amount of data. Much of that data is useless, but within that data also are patterns, or pieces of patterns, that are not always obvious.

Finding those patterns, and using them effectively, is the job of data analytics. In the early part of the millennium, the focus was on data mining to look for specific data, such as financial reports or growth trends. Analytics is the next evolution of that technology, applying AI or machine learning algorithms to to data to detect patterns. In semiconductor manufacturing, for example, these patterns may show why yield was poor at a specific time or why a device failed in the field well short of its expected lifetime.

On a much larger scale, data can be combined with more data from other sources to find patterns in climate shifts, for example, or global trade. Data analytics technology is critical in identifying all of these, as well as helping to explain what in the past were classified as random errors or unexpected events. As such, it can be used as a guide for patching irregularities across a global supply chain or identifying weaknesses in manufacturing that previously had gone unnoticed.

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Variability In Chip Manufacturing

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Variation At 10/7nm

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Inferencing At The Edge