JMP-2 (Cpk Analysis)

© 2014 Finisar Corporation, Confidential

Capability Analysis using JMP

CHONG SOOK FERN14 AUG 2014

© 2014 Finisar Corporation Confidential 2

Discussion Outline


SPC Goals

When special cause variation is present...When special cause variation is present...

Day 1Target Target Target

Day 2 Day 3 TomorrowTarget

Unpredictable???

After achieve a predictable process, compare the distribution with After achieve a predictable process, compare the distribution with specification limits & reduce the product variabilityspecification limits & reduce the product variability

Yesterday

Target

Today

Target

TomorrowTarget Target

LSL USLTarget

LSL USL


General Flow for Cpk Analysis

Phase Exit Analysis Yield Improvement Initiative Test Time Reduction Initiative

Data Collection•Take cumm data that prior to any rework•1 record per module•Include fail data (that require rework in order to pass)

Data Collection•Take 1st pass data (there should be minimal amount of fail data as only stable process should be considered for this option)•1 record per module•Minimum 100 individual data points•Exclude fail data that due to assignable causes (tester wrong reading, typo)

Data Collection•Take 1st data •1 record per module•Include fail data that is product performance related•(just a guideline, focus should be more on failure pareto analysis)

Distribution StudyNormality Assessment:•Assess & locate the best fitted distributionOutlier:•Remove outlier that due to assignable causes

• Typo error, tester wrong reading, defective component that is batch related issue

•Remain outlier that is product performance related• Defective / bad performance component that unable to

be resolved for time being, tuning related issue

Cpk Analysis•Calculate Cpk based on the best fitted distribution•Calculation may be done based on diff channel / temp depends on the objective of study


Some sharing from Phil Kiely on data collection for capability study:Data could be collected using the following options:

1st pass data….this will have one record per module, but the data will include any issues associated with tester capability, etc

Cum data, with rework…..this will have one record per module, but the data is distorted as modules can be reworked many times to make work (but it’s hard to call modules that require lots of rework ‘capable’)

All data….failing modules may have many records, and so will distort this dataset – it will be excessively pessimistic as to the actual capability

Passing data only…..this will have at most one record per module, but on the whole will likely give an excessively optimistic view of the actual capability

Cum data, prior to any rework….this will have one record per module, and will include failing data for any module that required rework to make it ultimately pass…this is the preferred dataset, and is aligned with the ‘Cum yield, no rework’ requirement that is set for phase exits

The attached query is an example of how to get this data (example is for QSFP LR module) The attached data is an example output from that query Note: the drawback of the query as written is that if the module was reworked as part of 2nd

ops (ie:prior to any testing), no data will be shown for that module. This should not be too much of a problem, as it is expected those modules should perform the same in testing as those which did not get reworked in 2nd ops

Example query

Example data


1. Copy data from Excel & paste into JMP data file

2. Click Analyze > Distribution

3. Put the study column into Y box


Click on the red button > continuous fit > Normal to check whether distribution is normally distributed

Parameter for normal distribution


Parameter for normal distribution

Perform Diagnostic Plot & Goodness of Fit test to assess the normality of data


95% confidence interval

Normal: P-value > 0.05


Alternatively, distribution study can be performed by fitting all possible distributions & compare all in 1 view


JMP will display the best fitted distribution by defaultIn this example, the data is normally distributed


1. Click Capability Analysis from the red button

2. Input the LSL & USL* The default sigma used is

Long Term Sigma

3. Choose the best fitted distribution



Eg 1: Non-normal DataThe crossing is tuned just within spec if it fails on

the lower side, not tuned to center of spec range

In this case, the normal fitted distribution (right) can be seen not to be veryrepresentative, the non-normal fit (left) is much more accurate & gives better Ppk.

Cpk computed based on normal distribution Cpk computed based on

non-normal distribution


Eg 2: Non-normal Data with outliersThe ER is tuned based on other params…

basically there are 3 distributions

Cpk computed based on normal distribution

Cpk computed based on non-normal distribution

In this case, the normal fitted distribution (left) can be seen to not be veryrepresentative, the non-normal fit (right) is much more accurate.

Outlier: > Upper Quartile + 1.5*IQR< Lower Quartile – 1.5*IQR


Eg 3: Non-normal Data with outliersThe ER is tuned based on other params…

basically there are 3 distributions

Cpk computed based on normal distribution

Cpk computed based on non-normal distribution

recommend either staying with normal statistics for calculation of Cpk, and/or use a filter to remove outliers – in JMP, use the ‘robust mean and standard deviation’, see the next slide

Outlier: > Upper Quartile + 1.5*IQR< Lower Quartile – 1.5*IQR


Eg 3: Non-normal Data with outliersThis is the same dataset as slide 16but we have now included robust valuesfor mean and standard deviation

Cpk computed based on normal distribution Regular mean & std dev

Cpk computed based on normal distributionRobust mean & std dev

Remark (reason code) should be provided to audience if outliers are removedCpk should always be reported together with distribution plot for audience better understanding


Process Capability Cpk vs Process Performance Ppk


Process Capability Cpk vs Process Performance Ppk


Sigma Level Yield PPM(Part per million)

DPMO(defect per million opportunity)

Cpk

X-Bar ± 0.5σ 38.30% 617,000 841,345 0.27X-Bar ± 1σ 68.27% 317,310 691,462 0.33

X-Bar ± 1.5σ 86.64% 133,612 500,000 0.50X-Bar ± 2σ 95.45% 45,500 308,538 0.67

X-Bar ± 2.5σ 98.76% 12,419 158,655 0.83X-Bar ± 3σ 99.73% 2,700 66,807 1.00

X-Bar ± 3.5σ 99.95% 465 22,750 1.17X-Bar ± 4σ 99.9937% 63 6,210 1.33

X-Bar ± 4.5σ 99.99966% 3.4 1,350 1.50X-Bar ± 5σ 99.999943% 0.57 233 1.67

X-Bar ± 5.5σ 99.999996% 0.038 32 1.83X-Bar ± 6σ 99.9999998% 0.002 3.4 2.00

Theoretical Relationship Between Cpk, Process Yield & PPM

** All values above are estimation based on standard normal distribution** For bilateral tolerances, ppm computed based on Cpk / Ppk tends to overestimate defectives** PPM may or may not = DPMO. If 1 defective part have > 1 defect opportunity, DPMO >>> PPM

Question to ponder: Is the above chart applicable for Ppk??


68.26%95.46%99.73%99.993%99.999943%99.9999998%

Standard Normal Distribution


Theoretical Relationship Between Cpk, Process Yield & PPM


Case Study 1: Mean shift > Cpk improved > Yield improved

Eg1: bad sensitivity on 12RZOL product code In WW20’14the 1st pass yield in Final is 29.17%Major contributor is bad sensitivity failureFailure rate is 9/25 = 36%The average sensitivity = -25.8 dBm

Upon analysis, the best fitted distribution is Johnson SICpk estimated is 0.081

Cpk = 0.081


Eg1: bad sensitivity on 12RZOL product code Upon evaluation, observed apd was over biased. Action taken to optimize apd manually.

In WW43’14the 1st pass yield in Final improved to 52.38%Failure rate drop from 36% to 7/42 = 16.67%The average sensitivity = -26.3 dBm

Upon analysis, the best fitted distribution is Johnson SICpk improved from 0.081 to 0.252

Cpk = 0.081 Cpk = 0.252

eg1

Case Study 1: Mean shift > Cpk improved > Yield improved

Mean shifted


Eg 2: OSNR failure on 12RZOL product code

Original spec = 2e-7Cpk = -0.003

Revised spec = 3e-5(as per OCA)Cpk = 1.101

OSNR condition loosen from 13dB to 14dB

eg2

Case Study 2: Spec Relaxation > Cpk improved > Yield improved


-31.5

-31

-30.5

-30

-29.5

-28.5

-28

-27.5

-26.5

-26

-25.5

-25

USL = -27

LSL=-29

DATA

_VAL

1

final_rx-rm temp_test_rx-cm temp_test_rx-hp

DATASET_NAME

final_rx-rm

temp_test_rx-cm

temp_test_rx-hp-2.33 -1.64-1.28 -0.67 0.0 0.67 1.28 1.64 2.33

0.02

0.04

0.08

0.12

0.18

0.26

0.34

0.44

0.54

0.64

0.74

0.82

0.88

0.92

0.96

0.98

Normal Quantile

Missing Rows 1

Quantiles

Means and Std Deviations

Compare Densities

12RZZT: rx_mca-f:rxpowalmThresh

Case Study 3: Distribution change > Cpk improved > Yield improved

Eg3: Rxpowalrmthresh failure on 12RZZT product code In WW8’14the 1st pass yield in corner is 21.8%Rxpowalrmthresh failure contribute to 10.3% of total test failure

The big drift between diff temperatures contribute to big delta that resulted in poor Cpk

Upon analysis, the best fitted distribution is Johnson SUCpk estimated is 0.236


Case Study 3: Distribution change > Cpk improved > Yield improved

Eg3: Rxpowalrmthresh failure on 12RZZT product code

Cpk = 0.236

Cpk = 0.92

eg3

In WW47’14After cut in improvement action on tuning optimization,the 1st pass yield in corner improved to 70.3%Rxpowalrmthresh failure contribute to 4.5% (improved from previous 10.3%) of total test failure

The big drift between diff temperatures reduced

Upon analysis, the best fitted distribution is Johnson SI

Note that the mean value before & after improvement action remained the same as -28dBm

Cpk estimated improved from 0.236 to 0.92


Upper Control Limit (XUCL)Center Line (X-Bar-Bar)

Subgroup

Center Line (R-Bar)

Upper Control Limit (RUCL)

Lower Control Limit (XLCL)

Lower Control Limit (RLCL)

1st SubgroupR (1) = 5

9th SubgroupX-Bar (9) = 598

Subgroup 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20X1 X2 X3

Mean X-Bar1 X-Bar2 X-Bar3 X-Bar4 X-Bar5 X-Bar6 X-Bar7 X-Bar8 598 X-Bar10 X-Bar11 X-Bar12 X-Bar13 X-Bar14 X-Bar15 X-Bar16 X-Bar17 X-Bar18 X-Bar19 X-Bar20Range 5 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20

Sample size = 3 # of subgroup = 20

Appendix A: Elements of Control Chart


Appendix B: Example of Different Distributions


Q&A Session

Documents

JMP-2 (Cpk Analysis)