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Envisioning Information: Case Study 3

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.

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Envisioning Information: Case Study 3

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

  2. Multidimensional Detective • Parallel coordinate plots can initially be intimidating Excellent worked example provided by the creator: A. Inselberg, Multidimensional Detective, IEEE Visualization 1997 ENV 2006

  3. 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 Understand the Problem Why are we using visualization? We seek relationships amongst the variables ENV 2006

  4. Brushing can select observations which are high in X1 and X2 Notice separation into two classes in X15 Some high X3 are not selected Brushing Principle 1: Do not let the picture intimidate you Principle 2: Understand the objectives and use them to obtain ‘visual cues’ Principle 3: Carefully scrutinise the picture ENV 2006

  5. Now look for batches with zero defects in 9 out of the 10 defect categories Inselberg calls the result a ‘shocker’! Why? Look at the other defect categories ENV 2006

  6. 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 Back to the Drawing Board ENV 2006

  7. 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 Good batches Principle 4: Test the assumptions and especially the ‘I am really sure of ..’ s ENV 2006

  8. 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 Explore the X15 gap Conclusion: small ranges of X3 and X6, plus low ranges of X15 characterize a good batch of chips ENV 2006

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