Yield and quality control

When analyzing yield data, which example correctly illustrates that "a correlation between two quantities does not mean one causes the other"?

  1. AConclusions can be drawn without looking at data
  2. BEven if lots processed on a certain tool have low yield, another factor may affect both, such as that tool happening to receive many difficult products
  3. CCorrelation always implies causation
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Answer: B. Even if lots processed on a certain tool have low yield, another factor may affect both, such as that tool happening to receive many difficult products

Correlation only shows that two quantities tend to change together; it does not prove causation. A common hidden factor (a confounder) may influence both. It is important to identify the true cause through stratification and confirmation experiments.

  • A. It is important to verify conclusions with data.
  • B. Correct. Another factor may be affecting both.
  • C. Correlation does not necessarily imply causation.
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More Yield and quality control questions

  1. Which correctly describes efforts to raise yield by analyzing the large amounts of equipment data, metrology data and test results accumulated in a fab with machine learning and similar methods?
  2. Which yield model accounts for defects that occur clustered unevenly on the wafer?
  3. What is the quantity called that, for each type of defect found by inspection, gives the fraction of chips containing that defect that actually fail electrically?
  4. Which correctly states Little's law, relating work in progress (WIP), throughput and cycle time in a factory?
  5. What is the analysis called that statistically identifies, from the manufacturing histories of many lots, the tools, chambers, materials and the like commonly used by low-yield lots?