FDC-Application with 1 khz sampling rate

Transcription

1 FDC-Application with 1 khz sampling rate Detection of abnormal Plasma Discharge during an Oxide Etching Process 10/19/2010 SEMICON Europa 2010 Author: M. Mündner (LFoundry GmbH)

2 Content Overview Discharge during plasma etching Data Collection and Infrastructure Analyser Data Quality Fault Detection Data Reduction: Requirements Data Smoothing Compression algorithm Forward compression Backward compression Final compression output Reducer: Tuning parameters Comressed trace vs. Representation in SPC-Framework Summary and conclusions Acknowledgements 2

3 Discharge during plasma etching RF upper 27,1 MHz Failure mechanism and observation Plasma Discharge can lead to electrical damage of the wafer and to particle generation in the process chamber. Wafer Discharge Vpp lower Yield-loss and equipment damage Very short breakdown of the plasma due to a discharge during wafer processing. RF lower 400 khz - 2 MHz extremely short dip in the Vpp-Signal 3

4 Data Collection and Infrastructure Sampling digitalized trace data. Polling context information. (process start/stop, recipe, step,...) Merging all data to generate wafer-files. Starting to analyse the wafer-files after finishing the wafer-process. Monitoring the data quality. Fault Detection and triggering OCAPs. Storing a reduced dataset in a database. (reduction-factor of 500 up to 1000) Data base 4

5 Analyser: Data Quality Monitoring the data quality Identification of data gaps Calculation of the difference between the timestamps of subsequent data-points. Shewart-chart used to control the maximum value of these time-differences. Sample exceeding the limits of the dataquality shewart-chart During recipe-step#5 datagaps up to 22msec occured OCAP triggered Between all 6600 measurement-points of recipe-step#1 the time-difference has been 1msec 5

6 Analyser: Fault Detection Recipe-step wise judging the raw-data trace (sampling rate 1kHz) Golden tunnel calculated around the moving average of the actual trace. Fixed absolute limits. Or none of these judgement. hitcount exceeding specified limit OCAP triggered One Shewart-Chart for each recipe-step to control the hitcounts/step peak exceeding the local limit Judgement of the complete trace Additional Shewart-Chart to control the total hitcount golden tunnel around the actual trace blind zone around recipe-step border fixed limits 6

7 Data Reduction: Requirements Example: 2 Parameters@1kHz samples/min. Wafer process time ~3min. xml-file with a size of MByte Zipping: Compressed file with a size of 2.5 MByte still by far too large!! Data Reduction and Storage Smoothing (Least Squares based convolute operation algorithm) Smoothed Data incl. Detections Compression (iterative and variance based) Compressed Data incl. Detections Data base Requirements to an effective data reduction algorithm: Preservation of shape, location and height of peaks or steps. Minimum number of supporting points required for an acceptable curve-image. Factors: Resolution of the data-review tool and performance of the database! 7

8 Data Smoothing For data smoothing an algorithm with convolution operation, as described in a paper 1) by A. Savitzky and M. J. E. Golay is used. The coefficients of the convolute function are computed as a Least-Squares solution of a polynomical fit function. Smoothing Algorithm Index : j : : Raw : 35.7 : : Y j * = Convolute Operation i=m Σ C Y i j+i i=-m N Smoothed : 31.1 : : Tuning parameters Number of points (filter-width). Polynomial order of the fitting function. 1) Analytical Chemistry, Vol. 36, No. 8, July 1964, pp

9 Compression algorithm The reducer removes all subsequent points of the smoothed data-set not exceeding a limit-band around the actualy regarded data point. The first point exceeding the limits is kept as new actual point. Limits=value(actual_Point)±[ var * mean(trace)] 9

10 Forward compression Calculate symmetric limits around the actual point. Remove all preceeding points as long as their values do not exceed the limits. The first point exceeding the limits is taken as new actual point. Calculate new limits and repeat the procedure. 10

11 Backward compression To avoid missing points caused by steep slopes the algorithm is done twice: First forward and then backward in time! 11

12 Final compression output The final reduced data-set is a combination of both runs (forward+backward). While the number of points exceeds a defined limit ( max_num ), the algorithm is repeated with wider limit_ranges: new_limits_range = var_factor * old_limit_range 12

13 Reducer: Tuning parameters The reducer is tunable by 3 parameters: max_num : maximum number of points after reduction. var : initial factor to calculate the symmetric limits. var_factor : factor to increase the limits for the next iteration, while the number of points after reduction still exceeding max_num. To receive a continous curve: The reduction is done process-step wise. The first and the last data-point of the traces of each step are kept. 13

14 Compressed trace vs. Performance of the data reduction algorithm: Conventionally polled data (sampling rate 1Hz): 86 points 1kHz-trace, var =0.14 ( points): compression-factor ~1030 1kHz-trace, var =0.08 ( points): compression-factor ~760 var =

15 Representation in SPC-Framework HITCOUNT upper limit=1, because this peak is expected Total wafer process time: 174 sec Step1...5: total datapoints in file Sampling Rate~950 Hz! Points raw reduced (in DB) Step1: Step2: Step3: Step4: Step5: Overall reduction factor~550! 15

16 Summary and conclusions The algorithm meets all requirements according accuracy and resolution. Compressed data can be stored in a conventional database! The SPC-Framework automatically identifies and sets up new control-charts by using predefinable default-settings. No manual setup of new charts necessary! Very robust against false alarms. No big efforts required to maintain the charts! Sensitive enough to detect even single discharges. Already identified several abnormal wafer-runs! Triggering actions to minimize negative impact on yield (predictive maintenance)! Processing of one waferfile with ~40 MByte can take up to 5 min; most time-consuming component is the file-parser. Implementation of real-time stream-parsing of the xml-file! Further optimization of the compression-algorithm! 16

17 Acknowledgements The realisation of this very specific FDC application has been greatly supported by M. Frick as part of his diploma-thesis. It has been implemented into an existing and productive SPC-framework originally developed and maintained by LFoundry. The application and also the whole SPC-framework is written in Python, using MySQL, QT and R. It is planned to release the whole code as open source. For this task an open-source consortium is in foundation. 17

18 Thank you for your attention 18