TECHNICAL NOTE
Principal Component Analysis for
Fault Detection on Implanter with FabGuard®
FabGuard Sensor Integration Analysis System is a fully
automated, real-time early fault detection and analysis system
for improving semiconductor equipment and process
productivity and various INFICON in situ diagnostic sensors.
The powerful analysis techniques of FabGuard are capable of"smart diagnostics" by combining sensor and tool data for
fault detection and classification. FabGuard puts in situ
sensors to work to:• Baseline normal process and tool behavior• Analyze process data in real-time to detect problems andpinpoint problem sources• Issue warnings and alarms
One of the benefits of FabGuard is the use of Principal
Component Analysis (PCA) for fault detection using tool data.
The process is ion implantation. PCA is a multivariate
statistical technique that combines data from many variables
based on each variable’s variance and on the correlation
between different variables. In this example, PCA is used todetect a fault rather than to detect endpoint.

Figure 1 shows implant tool data. Data was collected for 24
different tool variables. The lower plot shows the time series
data for 3 of these variables: Filament Current (red),Extraction Current (green), and Tetrode Current (blue). Thisdata is from a typical implant run.
The goal of PCA fault analysis by FabGuard is to use typical
data for training, to create a PCA model of the prototype
process. Then the PCA model is applied to the data fromevery new run as the run proceeds. If a new run issubstantially different from the prototype model, FabGuard
will detect this difference and generate an alarm.
Figure 2 shows how PCA fault analysis is setup in FabGuard.
In this example, the training method is T2. T2 provides ameasure of how close the set of all 24 variables is to theirnormal values. No special knowledge of the meaning of eachvariable is needed. The PCA model was created from three
implant runs that did not have any faults.

Figure 2. PCA training. T2 is used, keeping 3 principal components. The training
set is based on three implants runs that did not have any faults.

Figure 9. Typical implant tool data.

Figure 3 shows how the PCA model is applied, which isreferred to as validation. The validation set includes the three
training runs plus a fourth run that exhibits a fault. The fault in
the fourth run is clearly evident as the large peak just after
5000 seconds in the lower plot. Figure 4 is the same as
Figure 3, except the vertical scale of the lower plot has been
expanded by a factor of 100. For the fault-free training runs,
the largest value of T2 is 20. For the fourth run, the largest
value of T2 is more than 7000. This gives a signal to noise
ratio of 350, which is sufficient for robust fault detection in thisprocess.

Figure 4. PCA validation and fault detection. This is the same as theprevious figure, except the vertical scale of the lower plot has been
expanded by a factor or 100.

Figure 3. PCA validation and fault detection. The validation set includes
the three training runs plus a fourth run that exhibits a fault. The fault in
the fourth run is clearly evident as the large peak just after 5000 seconds

Figure 5 shows the raw data for all four runs from the validation
set. Looking at the raw data for individual variables, it is not
obvious that the fourth run, which starts just after 5000
seconds, contains a fault. Only 3 of the raw variables areplotted in Figure 5, but none of the other 24 variables are any
more useful for predicting a fault. Only PCA, which includes
correlation between all 24 variables, is able to unambiguously
identify the fourth run as substantially different from the firstthree.

Figure 5. Raw tool data for all four runs in the validation set. The runwith the fault starts just after 5000 seconds. The fault is not evident inthe raw data.

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