Increasing Fab yields by use of big data power, based on GT data mining. GT strengths in this relevance are its ability to find root-causes, using unsupervised data, and creating simple controls that can be embedded in existing systems.

1. Edith Ohri
Datalert (startup)
edith@datalert.co.il
FabHighQ
- a GT data mining application

2. Team member - Edith Ohri
• Edith Ohri - Industrial & Management Eng. from Israel
Technion and MSc from NY Polytech.
• The developer of GT data mining (big data); GT
received a research grant from SMU Singapore.
• Currently works on applications of GT.
• Previously initiated a project sponsored by Israel Chief
Scientist on “Procedure Systems Diagnostics”.
• Prior to that worked in Israel and the USA as an IE
Eng. And System Analyst.
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3. Fab-High-Q Value Proposition:
HIGHER YIELDS
• Fab-High-Q produces higher yields by finding
the root causes of Quality faults, and guiding
their detection and early stage prevention.
• The solution has a very high net value thanks
to using existing infrastructure, thus saving
the large part of investment, preparations,
integration, training and maintenance costs.
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4. Further Benefits
• Shorter time to market for new products
• Flexible capacity planning
• Faster Learning
• More automation
• Reduced overhead
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5. The product: Fab-High-Q
Fab-High-Q is a data analytics algorithm that
spots Quality risks, sends alerts and suggests
corrections, based on GT – a proprietary
universal solution.
Main strengths:
– Discovery of hidden patterns
– Early detection
– In-depth Machine Learning
– Robustness, very little pre requirements
– Supplied as a turn-key project.
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6. Options of implementation
• Per order project
• Licensing
• Collaboration.
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7. Example – Fab Higher Yields
• The case: a Fab which specializes in small-batch
manufacturing, experienced a steep decrease
in yields.
• GT was called in “to find out which work
stations are responsible for the quality failure”.
• It found that the root cause was rather in the
wafer supply processes. The findings led to
new insights about prevention and
improvement of yields.
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8. Example cont. Overcoming Bad Data
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• The data input was challenging. It had many outliers,
noise, missing values, and disrupted records due to a
computer upgrade that took place in the critical period.
• GT overcame the bad data by applying cluster analysis.

9. Example cont. Finding a clue
Among the clusters there was an irregular tiny
yet significant pattern (marked in red) that
occurred in the beginning of the Quality
deterioration. (cont. next slide)
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10. Example cont. The Root Cause
7Sep-2018
The Quality failure was indeed related to a special
event: a very large production order in the 2 lines under
study (red and blue).
The large order created a shortage in high quality
wafers. The “blue” line started to fail consequently until
brought to a halt.
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11. Example cont. The Solution
• The answer to a raw-material shortage is
increased supply by acquiring additional
quantities from internal or external sources.
• The best way though to increase the supply
would be to improve the quality of the in-
house wafers, by learning with GT their “long
tail” quality distribution.
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12. GT founds in the wafer long tails several sub-
groups. The high end sub-groups could be
expanded thereby improving the total ovens
turn out.
Example cont. Exploiting Long Tails
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13. Example cont. Lessons to draw
• All data sources are equally good; the more
unsupervised the better it is.
• Presuming in big data analysis doesn’t help.
• Good AI creates more answers than asked-for.
• Focus first and most on root causes.
“Defining the source of the problem is half
the way to the solution” (a Hebrew proverb).
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14. Thanks
Edith Ohri
Datalert (startup)
edith@datalert.co.il
* The pictures are from free sources on the internet
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