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

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

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

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 you Principle 2: Understand the objectives and use them to obtain ‘visual cues’ Principle 3: Carefully scrutinise the picture

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?

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

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

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 a good batch of chips