1. Parametric Sensitivity Analysis
Identify in a big mass of data the significant predictors of the Probe wafer yield by using JMP scripting

2. Parametric Sensitivity Analysis
Gianpaolo Polsinelli, Felice Russo
LFoundry s.r.l Italy a Smic Company
Abstract
Objective
In a Silicon-Fab several electrical and functional measurements are collected for each single silicon wafer.
So it is very important to identify in a big mass of data which variables are really modulating the wafer yield the most important key performance indicator.  
Usually a scatter plot with a linear regression fit is used for that.
Anyway this technique works well only if distributions are normal, in absence of outliers and data noisy. All those factors can obscure the true’s relationship between yield loss and in line issues.
To determine how different values of a
predictor variable impact the wafer
yield.
Identify the right candidates by using
Parametric Sensitivity Analysis (PSA)
algorithm.
JMP Script to automate the entire
process.

3. Parametric Sensitivity Analysis
Gianpaolo Polsinelli, Felice Russo
LFoundry s.r.l Italy a Smic Company
Methodology
The PSA technique is used when data are very noisy and contain confounding effects. The response distribution is divided in N different balanced groups and a label is assigned to all database rows. For each group the predictors mean and/or median is calculated.
Gr 1
Gr 2
Gr 3
Gr 4
Gr 5
Gr 6
The linear fit R2 is then calculated using mean and/or median of groups instead of raw data points. Besides R2 value the P-Val is evaluated too.
A table with predictors ranked by a decreasing R2 value is generated.
Grp 6
Grp1
Scatter plot for Yield vs. Predictor1
Scatter plot for Yield vs. Predictor2
Predictor1 by Group
( A correlation is visible)
Predictor2 by Group
(NO correlation is visible )
Response by Group

4. Parametric Sensitivity Analysis
Gianpaolo Polsinelli, Felice Russo
LFoundry s.r.l Italy a Smic Company
Raw Data
Result
Correlations
FAB Data:
Not normal distributions, long tails, fliers…
Density function estimation box: Hyperlink to page 4
Modes filtering box: Hyperlink to page4
Picture Table “Result”: Hyperlink to page 5
…….….
…….….
…….….
Parametric
Sensitivity
Analysis
Output ranked
by R2

5. Parametric Sensitivity Analysis
Gianpaolo Polsinelli, Felice Russo
LFoundry s.r.l Italy a Smic Company
Input GUI
Output Table
Graph Builder
To notice the strong improvement of R2 value.