Logistic Regression and Partitioning Techniques in Automotive Semiconductor Industry

1. Implementation of logistic regression and partitioning techniques with JMP PRO, for a wafer issue in automotive semiconductor industry Corinne Bergès, Yves Chandon, Pierre Soufflet Objective Context / Issue 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 • 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 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. 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. Implementation of logistic regression and partitioning techniques with JMP PRO, for a wafer issue in automotive semiconductor industry Corinne Bergès, Yves Chandon, Pierre Soufflet Partition results Logistic regression results 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) Before or after corrective actions: Probing Time at Hot Temperature has the largest impact on peeling Logistic regression Partition before corrective actions Partition after corrective actions

4. 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