EE290H F05 Spanos Introduction to Statistical Process Control

The assignable cause The Control Statistical basis of the control chart , false and true alarms and the operating characteristic function

Lecture 10: Introduction to Statistical Process Control 1 EE290H F05 Spanos Managing Variation over Time

• Statistical Process Control often takes the form of a continuous Hypothesis testing. • The idea is to detect, as quickly as possible, a significant departure from the norm. • A significant change is often attributed to what is known as an assignable cause. •An assignable cause is something that can be discovered and corrected at the machine level.

Lecture 10: Introduction to Statistical Process Control 2 EE290H F05 Spanos What is the Assignable Cause?

• An "Assignable Cause" relates to relatively strong changes, outside the random pattern of the process. • It is "Assignable", i.e. it can be discovered and corrected at the machine level. • Although the detection of an assignable cause can be automated, its identification and correction often requires intimate understanding of the manufacturing process. • For example... – Symptom: significant yield drop. – Assignable Cause: leaky etcher load lock door seal. – Symptom: increased e-test rejections – Assignable Cause: probe card worn out.

Lecture 10: Introduction to Statistical Process Control 3 EE290H F05 Spanos Example:

Investigate furnace temp and set up a real-time alarm.

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-3 0 5000 10000 15000 20000 time The pattern is obvious. How can we automate the alarm? Lecture 10: Introduction to Statistical Process Control 4 EE290H F05 Spanos The purpose of SPC

A. Detect the presence of an assignable cause fast. 2. Minimize needles adjustment.

• Like Hypothesis testing – (A) having low of type II error and – (B) means having low probability of type I error.

• SPC needs a probabilistic model in order to describe the process in question.

Lecture 10: Introduction to Statistical Process Control 5 EE290H F05 Spanos Example: Furnace temp differential (cont.)

Group points and use the average in order to plot a known (normal) . Assume that the first 10 groups of 4 are in Statistical Control. Limits are set for type I error at 0.05. 2

UCL 1.2 1

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Lecture 10: Introduction to Statistical Process Control 6 EE290H F05 Spanos Example (cont.)

• The idea is that the average is normally distributed. • Its is estimated at .6333 from the first 10 groups. • The true (μ) is assumed to be 0.00 (furnace temperature in control). • There is only 5% chance that the average will plot outside the μ+/- 1.96 σ limits if the process is in control.

In general: UCL = μ + k σ LCL = μ -k σ where μ and σ relate to the statistic we plot.

Lecture 10: Introduction to Statistical Process Control 7 EE290H F05 Spanos Another Example

Original Averaged Data (n=5) Plot Variable Control

1.150 1.070 UCL=1.0626 1.100 1.050

1.050 1.030 1.010 1.000 µ0=1.0006 0.990 0.950 small shift 0.970

0.900 0.950 Mean of small shift LCL=0.9387 0.850 0.930 0 100 200 300 400 500 25 50 75 100

small shift Mean of small shift

Lecture 10: Introduction to Statistical Process Control 8 EE290H F05 Spanos How the Grouping Helps

Small Group Size, Large Group Size, large β. smaller β for same α.

Bad Good

Lecture 10: Introduction to Statistical Process Control 9 EE290H F05 Spanos Average Run Length

• If the type I error (α) depends on the original (proper) parameter distribution and the control limits, ...

• ... the type II error (β) depends on the position of the shifted (faulty) distribution with respect to the control limits.

• The average run length (ARL) of the chart is defined as the average number of samples between alarms.

• ARL, in general, is 1/α when the process is good and 1/(1-β) when the process is bad.

Lecture 10: Introduction to Statistical Process Control 10 EE290H F05 Spanos The Operating Characteristic Curve The Operating Characteristic of the chart shows the probability of missing an alarm vs. the actual process shift. Its shape depends on the statistic, the subgroup size and the control limits.

β

These curves are drawn Fig. 4-5 from Montgomery,for α =pp. 0.05110

deviation in #σ

Lecture 10: Introduction to Statistical Process Control 11 EE290H F05 Spanos Pattern Analysis

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Other rules exist: Western Electric, curve fitting, , pattern recognition... Lecture 10: Introduction to Statistical Process Control 12 EE290H F05 Spanos Example: Photoresist Coating

• During each shift, five wafers are coated with photoresist and soft-baked. Resist thickness is measured at the center of each wafer. Is the process in control? • Questions that can be asked:

a) Is group "in control"? b) Is group average "in control"? c) Is there any difference between shifts A and B?

• In general, we can group data in many different ways.

Lecture 10: Introduction to Statistical Process Control 13 EE290H F05 Spanos and x chart for all wafer groups. 600 500 UCL 507.09 400 300 200 100 0 LCL 0.0 8000 UCL 7971.32 7900

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7700 LCL 7694.52

7600 0 10 20 30 40 Wafer Groups Lecture 10: Introduction to Statistical Process Control 14 EE290H F05 Spanos Comparing runs A and B Range, Shift A Range, Shift B 600 600 550 500 500 465

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0 0 0 10 20 0 10 20 Mean, Shift A Mean, Shift B 8000 8000 7985 7958

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Lecture 10: Introduction to Statistical Process Control 15 EE290H F05 Spanos Why Use a Control Chart?

• Reduce scrap and re-work by the systematic elimination of assignable causes. • Prevent unnecessary adjustments. • Provide diagnostic from the shape of the non random patterns. • Find out what the process can do. • Provide immediate visual feedback. • Decide whether a process is production worthy.

Lecture 10: Introduction to Statistical Process Control 16 EE290H F05 Spanos The Control Chart for Controlling Dice Production

Lecture 10: Introduction to Statistical Process Control 17 EE290H F05 Spanos The Reference Distribution 7

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23456789101112 Lecture 10: Introduction to Statistical Process Control 18 EE290H F05 Spanos The Actual 7

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23456789101112 Lecture 10: Introduction to Statistical Process Control 19 EE290H F05 Spanos In Summary

• To apply SPC we need: • Something to measure, that relates to product/process . • Samples from a baseline operation. • A statistical “model” of the variation of the process/product. • Some physical understanding of what the process/product is doing.

Lecture 10: Introduction to Statistical Process Control 20