1. Corinne Bergès, Yves Chandon, Pierre Soufflet
Implementation of logistic regression and partitioning techniques
with JMP PRO, for a wafer issue in automotive semiconductor industry
Corinne Bergès, Yves Chandon, Pierre Soufflet
Context / Issue
Objective
Context:
electronic die manufacturing for automotive industry
specific context: fully automated wafer handling at test station
Issue: at extraction of the dies from the wafers, backside metal peeling issue on a significant quantity of wafers
Request from the customer for a Design Of Experiments (DOE)
Objectives: a statistical analysis was started:
to highlight the key parameters involved in the issue from the available data
to check the corrective actions firstly implemented
to find a possible alternative to a costly DOE
First corrective actions
Available data
Available data: all the process and wafer handling parameters already implemented for zero defect strategies
First corrective actions: removing of the pincette time, and choice for a pincette with less contact
Difficulties
Data not always relevant for this defect
Due to measurement difficulties, some potential key parameters not monitored

2. Corinne Bergès, Yves Chandon, Pierre Soufflet
Implementation of logistic regression and partitioning techniques
with JMP PRO, for a wafer issue in automotive semiconductor industry
Corinne Bergès, Yves Chandon, Pierre Soufflet
Method
Preliminary study: data cleaning and correlation of all the measurement values
Logistic regression to see the effect of the parameters individually: Is there one parameter for which a specific adjustment will solve the peeling issue ?
JMP PRO: Analyze  Fit Y by X
Partitioning techniques: decision tree based on a G² likelihood ratio Chi-Square test: Is there a combination of parameters to solve the peeling issue ?
JMP PRO: Analyze  Modeling  Partition
Logistic regression and partition factors:
Pincette type
Pincette time at Room and Hot Temperature
Probing time at Room and Hot Temperature
Response:
With or Without peeling
RT: Room Temperature
HT: Hot Temperature

3. Corinne Bergès, Yves Chandon, Pierre Soufflet
Implementation of logistic regression and partitioning techniques
with JMP PRO, for a wafer issue in automotive semiconductor industry
Corinne Bergès, Yves Chandon, Pierre Soufflet
Logistic regression results
Partition results
Before or after corrective actions: Probing Time at Hot Temperature has the largest impact on peeling
Partition: main parameters to be addressed to decrease peeling rate:
before corrective actions: Probing Time HT and Pincette Time RT/HT
after corrective actions (Pincette Time removal and Pincette change): existence of a safe path for no-peeling incidents with specific actions on Probe Time HT (corrective actions validated)
Logistic regression
Partition before corrective actions
Partition after corrective actions

4. Corinne Bergès, Yves Chandon, Pierre Soufflet
Implementation of logistic regression and partitioning techniques
with JMP PRO, for a wafer issue in automotive semiconductor industry
Corinne Bergès, Yves Chandon, Pierre Soufflet
Conclusions
References
Efficiency of the implemented corrective actions measured
DOE avoided: validity of corrective actions confirmed later on production data
Finally, discrimination of some parameters involved in the issue, and help for their new finer adjustment to definitively solve the problem
Chapter #10 (‘Logistic Regression with Nominal or Ordinal response’), in ‘Fitting Linear Models’ jmp book
Chapter #3 (‘Partition Models’), in ‘Specialized Models’ jmp book