Automatic window generation for process trace

1. A method for automatic window definition for analysis of semiconductor equipment trace data, comprising:

receiving a plurality of sensor traces from a plurality of equipment sensors during a plurality of process steps of a semiconductor process;

for each respective step of the plurality of process steps:

aligning each sensor trace to a start of the respective step;

calculating a first rate of change for each sensor trace aligned to the start of the respective step;

aligning each sensor trace to an end of the respective step; and

calculating a second rate of change for each sensor trace aligned to the end of the respective step; and

defining a plurality of windows for bounding and analyzing the sensor traces in each window based primarily on the first and second rates of change calculated for each of the plurality of sensor traces.

2. The method of claim 1 , the defining step further comprising:

defining a first type of window to bound sensor traces in at least a first region where the first rate of change or the second rate of change is increasing or decreasing more than a threshold; and

defining a second type of window to bound sensor traces in at least a second region where the first rate of change or the second rate of change do not exceed the threshold.

3. The method of claim 1 , further comprising:

for each of the calculating steps:

performing a first set of other statistical calculations for each sensor trace aligned to the start of the respective step; and

performing a second set of other statistical calculations for each sensor trace aligned to the end of the respective step; and

defining the plurality of windows based on the first and second rates of change calculated and the first and second sets of other statistical calculations for each of the plurality of sensor traces.

4. The method of claim 1 , further comprising:

scaling each of the plurality of sensor traces prior to the aligning steps.

5. The method of claim 3 , the defining step further comprising:

identifying a plurality of transition periods at the start and the end of each process step;

in between the plurality of transition periods, clustering the input data to group together sensor traces having similar rates of change;

extending the plurality of transition periods; and

merging similar windows in the transition periods and in between the transition periods.

6. The method of claim 5 , the step of identifying transition periods further comprising:

identifying regions where values of the plurality of sensor traces are changing rapidly.

7. The method of claim 5 , the step of extending transition periods further comprising:

incorporating small portions of adjacent regions where values of the plurality of sensor traces are changing less rapidly into transition periods.

8. A method for automatic window definition in the analysis of semiconductor equipment trace data, comprising:

receiving trace data obtained from a plurality of semiconductor equipment sensors, the trace data associated with a plurality of process steps in a semiconductor process;

aligning the trace data for each process step, in a first instance, to a start of the process step, and in a second instance, to an end for the process step;

calculating statistics including rate of change for the first instances and second instances of aligned trace data for each process step; and

generating a plurality of windows for the trace data from analysis of the calculated statistics for the aligned trace data.

9. The method of claim 8 , the generating step further comprising:

identifying a plurality of transition periods at the start and at the end of each process step;

in between the plurality of transition periods, clustering trace data to group together trace data having similar rates of change;

extending the plurality of transition periods; and

merging similar windows.

10. The method of claim 9 , the step of identifying transition periods further comprising:

identifying regions where the plurality of sensor traces are changing rapidly.

11. The method of claim 10 , the step of extending transition periods further comprising:

incorporating small portions of adjacent regions where the sensor traces are changing less rapidly into transition periods.

12. The method of claim 8 , further comprising:

scaling the trace data to the aligning steps.

13. A method for automatic window definition in the analysis of semiconductor equipment trace data, comprising:

receiving trace data obtained from a plurality of semiconductor equipment sensors, the trace data associated with a plurality of steps in a semiconductor process;

for each step of the semiconductor process:

align the trace data associated with the step to the start of the step;

calculate statistics including rate of change for the trace data aligned at the start of the step;

align the trace data associated with the step to the end of the step;

calculate statistics including rate of change for the trace data aligned at the end of the step;

analyzing the calculated statistics for the trace data aligned at the start of the step and the trace data aligned at the end of the step; and

computing windows for the trace data based on the analysis of the calculated statistics.

14. The method of claim 13 , the step of computing windows further comprising:

identifying a plurality of transition periods at the start and at the end of each process step;

in between the plurality of transition periods, clustering trace data to group together trace data having similar rates of change;

extending the plurality of transition periods; and

merging similar windows.

15. The method of claim 14 , the step of identifying transition periods further comprising:

identifying regions where the plurality of sensor traces are changing rapidly.

16. The method of claim 15 , the step of extending transition periods further comprising:

incorporating small portions of adjacent regions where the sensor traces are changing less rapidly into transition periods.

17. The method of claim 13 , further comprising:

scaling the trace data prior to the aligning steps.