Envisioning Information: Case Study 3

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ENV 2006 CS3.1 Envisioning Information: Case Study 3 Data Exploration with Parallel Coordinates

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Envisioning Information: Case Study 3. Data Exploration with Parallel Coordinates. Multidimensional Detective. Parallel coordinate plots can initially be intimidating. Excellent worked example provided by the creator: A. Inselberg, Multidimensional Detective, IEEE Visualization 1997. - PowerPoint PPT Presentation

Transcript of Envisioning Information: Case Study 3

Page 1: Envisioning Information:  Case Study 3

ENV 2006 CS3.1

Envisioning Information: Case Study 3

Data Exploration with Parallel Coordinates

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ENV 2006 CS3.2

Multidimensional Detective

• Parallel coordinate plots can initially be intimidating

Excellent worked example provided by the creator:A. Inselberg, Multidimensional Detective, IEEE Visualization 1997

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ENV 2006 CS3.3

Understand the Problem

• What is the data?– 473 batches of a VLSI chip– 16 process parameters: X1,..X16– Yield (% useful in batch): X1– Quality (speed): X2– Defects (zero at top): X3 to X12– Physical parameters: X13 to X16

• What is the objective?– Raise the yield, X1– Maintain the quality, X2

• How achieved?– Minimize the defects

Why are we using visualization?

We seek relationships amongst the variables

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ENV 2006 CS3.4

Brushing

• Brushing can select observations which are high in X1 and X2

• Notice separation into two classes in X15

• Some high X3 are not selected

Principle 1: Do not let the picture intimidate youPrinciple 2: Understand the objectives and use them to obtain ‘visual cues’Principle 3: Carefully scrutinise the picture

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ENV 2006 CS3.5

Look at the other defect categories

• Now look for batches with zero defects in 9 out of the 10 defect categories

• Inselberg calls the result a ‘shocker’! Why?

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ENV 2006 CS3.6

Back to the Drawing Board

• Return to base camp

• X6 is clearly different from the other defect categories

• So try excluding X3 and X6 – leaving 8 defect categories

.. Now we do get the high yield batch

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ENV 2006 CS3.7

Good batches

• The best batch has all zeroes except for X3 and X6

• So.. Are these measurement errors in X3 and X6?

• Look for the top group of batches

• None have zero defects in X3 or X6

Principle 4: Test the assumptions and especially the ‘I am really sure of ..’ s

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ENV 2006 CS3.8

Explore the X15 gap

• High range of X15 gives lowest of group of high yield batches, and mixed quality

• Low range of X15 has uniformly high quality and full range of high yield

Conclusion: small ranges of X3 and X6,plus low ranges of X15 characterize agood batch of chips