As the development of semiconductor devices, manufacturing system leads to improve productivity and efficiency for wafer fabrication. Owing to such improvement, the number of wafers yielded from the fabrication process has been rapidly increasing. However, current software systems for semiconductor wafers are not aimed at processing large number of wafers. To resolve this issue, the BISTel (a world-class provider of manufacturing intelligence solutions and services for manufacturers) tries to build several products for big data such as Trace Analyzer (TA) and Map Analyzer (MA) using Apache Spark. TA is to analyze raw trace data from a manufacturing process. It captures details on all variable changes, big and small and give the traces' statistical summary (i.e.: min, max, slope, average, etc.). Several BISTel's customers, which are the top-tier semiconductor company in the world use the TA to analyze the massive raw trace data from their manufacturing process. Especially, TA is able to manage terabytes of data by applying Apache Spark's APIs. MA is an advanced pattern recognition tool that sorts wafer yield maps and automatically identify common yield loss patterns. Also, some semiconductor companies use MA to identify clustering patterns for more than 100,000 wafers, which can be considered as big data in the semiconductor area. This talk will introduce these two products which are developed based on the Apache Spark and present how to handle the large-scale semiconductor data in the aspects of software techniques. Speakers: Seungchul Lee, Daeyoung Kim

1. Seungchul Lee, sclee@bistel.com
BISTel Inc.
Daeyoung Kim, dykim3@bistel.com
BISTel Inc.
Analyzing 2TB of Raw Trace Data
from a Manufacturing Process:
A First Use Case of Apache Spark for
Semiconductor Wafers from Real Industry
#UnifiedAnalytics #SparkAISummit

2. Contents
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• Introduction to BISTel
– BISTel’s business and solutions
– Big Data for BISTel’s smart manufacturing
• Use cases of Apache Spark in manufacturing industry
– Trace Analyzer (TA)
– Map Analyzer (MA)

3. Introduction to BISTel
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4. BISTel’s business areas
• Providing analytic solutions based on Artificial Intelligence (AI)
and Big Data to the customers for Smart Factory
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5. BISTel’s solution areas
• World-Class Manufacturing Intelligence through innovation
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6. BISTel’s analytic solution: eDataLyzer
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7. BISTel’s analytic solutions (MA)
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• Map Pattern Clustering
– Automatically detect and classify map patterns with/without libraries
– Process thousands of wafers and give results in few minutes
Clustered
Defective
wafers

8. BISTel’s analytic solutions (TA)
• Specialized Application for Trace Raw Data
– Extracts the vital signs out of equipment trace data
– Provide in-depth analysis which traditional methods cannot reach
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Abnormal
Normal

9. BISTel’s big data experiences
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10. BISTel’s big data experiences
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- YMA Test using Spark - - Big data platforms comparison-

11. Trace Analyzer (TA)
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12. Trace Data
• Trace Data is sensor data collected from processing equipment
within a semiconductor fab during a process run.
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- Semiconductor industry -
- Wafer -

13. Logical Hierarchy of the trace data
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Wafer
Lot
Recipe Step
Recipe
Process
Visualization
Whole
process
Process
Recipe 1
wafer

14. An example of the trace data
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Process Recipe Recipe step Lot Wafer Param1 Param2 Time
021_LIT RecipeA 1 1501001 1 32.5 45.4
2015-01-20
09:00:00

15. Data attributes
• Base unit : one process and one parameters
• 1000 wafers
• Each wafer has 1000~2000 data points in a recipe step
• Some factors that make trace data huge volume
• # of parameters
• # of processes
• # of wafers
• # of recipe steps
• duration of the recipe step
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16. An example of the trace data – (2)
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No. Fab
# of
processes
# of
recipe steps
Avg. Recipe
ProcessTime
Data
Frequency
# of
units
Parameter
per unit
(max)
1 Array 109 10 16 mins 1Hz 288 185
2 CF 25 5 1min 1Hz 154 340
3 CELL 12 7 1min 1Hz 213 326
4 MDL 5 12 2mins 1Hz 32 154
• Some calculations
• For one process, one parameter and one wafer
• 16 * 10 * 60 sec * 1Hz = 9600 points
• Multi parameters, multi processes and multi wafers
• 9600 * 288 *185 * 109 * (# of wafers)

17. Spark : Smart manufacturing
• Spark is a best way to process big data in batch analytics
• Distributing data based on parameter is suitable for using
Apache Spark.
• Easy deployment and scalability when it comes to providing the
solutions to our customers
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Naïve way: applying spark to TA

19. How to apply Spark to TA?
traceDataSet = config.getTraceRDDs().mapToPair(t->{
String recipeStepKey = TAUtil.getRecipeStepKey(t); #use recipe step as key
return new Tuple2<String,String>(recipeStepKey,t);
}).groupByKey();
traceDataSet.flatMap(t->{
Map<String,TraceDataSet> alltraceData = TAUtil.getTraceDataSet(t);
...
TAUtil.seperateFocusNonFocus(alltraceData,focus,nonFocus); #separate data
ta.runTraceAnalytic(focus,nonFocus,config); # calling the TA core
...
});

20. Most cases in manufacturing industry
• In real industry, most parameters have small number of data points.
(Most case : 1Hz)
• In addition, the number of wafers to be analyzed is not massive.
(up to 1,000 wafers)
• Therefore the total number of data points in a process can be easily
processed in a core

21. Issues in manufacturing industry
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• Last year, I have got an email indicating that..

22. Big parameter
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• Tools with high frequency or high recipe time can produce huge
volume for single parameter
• Requirements in industry
• For one parameter
• 400,000 wafers
• 20,000 data points.

23. Limitations of the Naïve TA
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For(Tuple<String,Iterable<String> recipeTrace : allTraceData){
TraceDataSet ftds = new TraceDataSet();
Iterable<String> oneRecipe = recipeTrace._2();
for(String tr : oneRecipe){
TraceData td = TAUtil.convertToTraceData(tr);
ftds.add(td);
}
}
traceDataSet = config.getTraceRDDs().mapToPair(t->{
String recipeStepKey = TAUtil.getRecipeStepKey(t); #use recipe step as key
return new Tuple2<String,String>(recipeStepKey,t);
}).groupByKey();
All the data points based
on the key are pushed
into one core by shuffling
Java object holds too
many data points

24. Needs for new TA spark
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• Naïve TA Spark version cannot process massive data points.
• Nowadays, new technology enhancements enable data capture at
much higher frequencies.
• TA for “big parameter” version is necessary.

25. Our idea is that..
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• Extracting the TA core logic
– Batch mode
– Key-based processing
– Using .collect() to broadcast variables
– Caching the object

26. • Preprocessing trace data
• Key-based processing
• Base unit : process key or recipe step key
Batch
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JavaPairRDD<String, List<String>> traceDataRDD
= TAImpl.generateBatch(traceData)
First element : process, recipe
step, parameter and batch ID
Second element : lot, wafer and
trace values
Summary
statistics
.
.
.
•Param A

27. Collect() : TA Cleaner
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• Filtering out traces that have unusual duration of process time.
• Use the three main Spark APIs
– mapToPair : extract relevant information
– reduceByKey : aggregating values based on the key
– collect : send the data to the driver

28. Collect() : TA Cleaner – (2)
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Worker
wafer value
1 65
2 54
… …
Worker
wafer value
1 83
2 54
… …
Worker
wafer value
1 34
2 77
… …
Worker
wafer value
1 71
2 80
… …
• traceData.mapToPair()
• Return
• key : process
• value : wafer and its length

29. Collect() : TA Cleaner – (3)
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• reduceByKey()
• Aggregating contexts into one based on the process key
wafer value
1 65
2 54
… …
Shuffling
wafer value
1 88
2 92
… …
wafer value
1 153
2 146
… …

30. Collect() : TA Cleaner – (4)
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• Applying filtering method in each worker
mapToPair(t -> {
String pk = t._1();
Double[] values = toArray(t._2());
FilterThresdholds ft = CleanerFilter.filterByLength(values);
return Tuple(pk,ft);
}).collect();

31. Examples 2 : Computing outlier
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• To detect the outlier in a process, median statistics is required.
• To compute the median value, the values need to be sorted.
• Sort(values)

32. Examples 2 : Computing outlier – (2)
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mapToPair reduceByKey
• Computed the approximate median value for big data processing.
• Applied histogram for median
• Collecting the histogram
Collect

33. Caching the trace data
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• Persist the trace data before applying TA algorithm
• Be able to prevent data load when the action is performed
Focus=Focus.persist(StorageLevel.MEMORY() AND DISK())
NonFocus=NonFocus.persist(StorageLevel.MEMORY() AND DISK())

34. RDD vs. DataSet (DataFrame)
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• RDD
– All the data points in a process should be scanned
• Advantage of the DataSet is weakened.
– Hard to manipulate trace data using SQL
– Basic statistics (i.e. Min, Max, Avg, Count…)
– Advanced algorithm (Fast Fourier Transform and
Segmentation)

35. Demo : Running the TA algorithm
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• Analyzed 2TB trace data using TA

36. TA results in eDataLyzer
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37. Results of the Naïve TA
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38. Results of the big parameter TA Spark
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39. • Two different TA Spark versions
Two different TA Spark versions
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Data size
# of
parameter
# of
wafers
# of data
points
Running Time
Naïve TA 2TB 270,000 250 1000 1.1h
Big Param TA 1TB 4 400,000 20,000 54min

40. Map Analyzer (MA)
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41. Map Analytics (MA)
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• Hierarchical clustering is used to find a defect pattern
S.-C. Hsu, C.-F. Chien / Int. J. Production Economics 107 (2007) 88–103

42. MA datasets
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Process Process step Parameter Lot Wafer
Defective
chips
FPP Fall_bin P01 8152767 23
-02,04|-
01,22|+00,25|+08,
33|+04,05
waferDataSetRDD.mapToPair(...).groupBy().mapToPair(...);
Generating
a key value pair
Calling hierarchical
clustering

43. BISTel’s first approach for MA
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• Using the batch mode for clustering massive wafers.

44. Demo : Running the MA algorithm
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• Dataset consists of 26 parameters containing 120,000 wafers

45. Problems in batch for clustering
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• In a manufacturing industry, some issues exist
# of wafers Time Detecting a pattern
DataSet1 15 2017-02-01:09:00 ~ 09:30 Yes
DataSet2 7,000 2017-02-01~2017-02-08 No

46. Spark summit: SHCA algorithm
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• In Spark Summit 2017, chen jin presented a scalable hierarchical
clustering algorithm using Spark.

47. A SHCA algorithm using Spark
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Jin, Chen, et al. "A scalable hierarchical clustering algorithm using
spark." 2015 IEEE First International Conference on Big Data Computing
Service and Applications. IEEE, 2015.

48. Applying SHCA to wafer datasets
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Wafer map ID Coordinates of defective chips
A (13,22), (13,23), (13,24), (13,25)…
B (5,15), (6,12), (6,17), (8,25)…
C (9,29), (16,33), (19,39), (22,25)…
D (19,9), (20,2), (23,21), (25,4)…
E (5,5), (5,8), (5,15), (5,25)…
• Designed the key-value pairs
• Minimum spanning tree (MST)
– Vertex : Wafer
– Edge : distance between wafers
• distance w1, w2

49. Comparison between two versions
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50. Comparison between two versions - (2)
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51. Spark stage results of MA
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• Approximately 100,000 wafers are analyzed for clustering

52. Comparison of the results
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0
500
1000
1500
2000
2500
5,000 50,000 100k 160k 320k
Batch New MA

53. Summary
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• MA using SHCA is accurate than the batch MA.
• However, the running time of the batch MA is faster than that of the
new MA.
• In manufacturing industry, we suggest them to use both of two MAs.

54. Conclusions
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• A first use case of Apache Spark in Semiconductor industry
– Terabytes of trace data is processed
– Achieved hierarchical clustering on distributed machines for
semiconductor wafers

55. Acknowledgements
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• BISTel Korea (BK)
– Andrew An
• BISTel America (BA)
– James Na
– WeiDong Wang
– Rachel Choi
– Taeseok Choi
– Mingyu Lu
* This work was supported by the World Class 300 Project (R&D) (S2641209, "Development of next generation intelligent Smart
manufacturing solution based on AI & Big data to improve manufacturing yield and productivity") of the MOTIE, MSS(Korea).

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