1. Mark D. Harrisonsixsigmadude@gmail.com

2. Purpose of Presentation Provide guidance on proper implementation of SPC Provide suggestions on improving process performance Provide a method to ensure SPC becomes a part of company culture Provide suggestions for new methods to improve SPC effectiveness Be used a checklist/reference for new SPC system or improvement of current SPC system4/10/2012Author: Mark D. Harrison2 3. What is SPC ? SPC stands for Statistical Process Control SPC is a fundamental approach to quality control and improvement that is based on objective data and analysis Measure the Process Eliminate Variances in the Process Monitor the Process Improve the Process4/10/2012 Author: Mark D. Harrison3 4. Why use SPC ? Provides indications of how healthy the process is Allows objective numerical analysis of a process Make the Most with the Least Possible Maximize Process Yields Minimize Scrap and Rework incidents Increases Efficiency Provides a Voice of the Process4/10/2012 Author: Mark D. Harrison4 5. Where can SPC be used ? SPC can be used anywhere measurements and performance specifications are utilized SPC is mostly associated with Manufacturing But SPC can also be used in: Marketing Medical/Healthcare Service Industries And many other fields4/10/2012Author: Mark D. Harrison5 6. Elements of a Successful SPC Program Management Leadership A Team Approach Education of employees at all levels Emphasis on reducing variability Measuring success in quantitative (economic) terms Communicate successful results throughout organization4/10/2012 Author: Mark D. Harrison6 7. Management Leadership Need Top Down buy-in Communicate Business Justification to Employees Drive Cultural Change Prevent Internal Sabotage Put SPC metrics in Performance Plans4/10/2012Author: Mark D. Harrison7 8. A Team Approach Include all Stakeholders Break down Silos Define Whats in it for me for each Stakeholder Utilize the Strengths of all your Resources4/10/2012 Author: Mark D. Harrison 8 9. Education of Employees at all Levels Everyone should understand SPC and why it is being used New Employee Orientation Manufacturing should get additional training on RCA Manufacturing and Design Engineering should get additional training on DOE, RCA and Gage R&R Management should get additional training on high level metrics (Cp/Cpk, 1st Pass Yield, ROI etc..)4/10/2012Author: Mark D. Harrison9 10. Emphasis on Reducing Variability Reduce Scrap Reduce Rework Improve Process Capabilities Reduced Costs = Higher Profits4/10/2012Author: Mark D. Harrison 10 11. Measure Success in Quantitative Terms Develop Performance Metrics to report regularly How Workers Benefit (Less Work/Time for same output) How Company Benefits (Higher Profits/More Customers/Competitive)4/10/2012Author: Mark D. Harrison11 12. Communicate Successful Results Report Chosen Performance Metrics on a Regular Basis Post Communications in places where easily viewable Bulletin Boards Video Monitors Internal Company Websites Report who, what, when, where to build pride and ownership in peoples work Include people who did the work to present to higher level management and even write papers4/10/2012Author: Mark D. Harrison 12 13. Example Company Newsletter4/10/2012 Author: Mark D. Harrison 13 14. What does SPC look like ? Upper Control LimitProcess Average or Target Lower Control LimitSample Number or Time4/10/2012Author: Mark D. Harrison14 15. Types of SPC Charts There are 2 basic types of SPC Charts Variable Charts Measureable Attribute Charts - Countable Variable Charts Plot Continuous Data Individuals Charts One Point of Data XBAR and Range Sample Size of 2-9 XBAR and SD Sample Size of 10+ Attribute Charts Plot Count or Go/No-Go Data u Chart Changing Sample Size of Occurrences c Chart Constant Sample Size of Occurrences p Chart Changing Sample Size Pieces or Units np Chart Constant Sample Size Pieces or Units4/10/2012 Author: Mark D. Harrison15 16. Selecting Which SPC Chart to Use n = 2 to 9Average, Range Average, Sigma n = 10 or moreMeasurements non-normalRun Chart normalMoving Rangen fixednp Chartpieces orunitsp ChartCountsn variesn fixedc Chart occurrencesn varies u Chart4/10/2012Author: Mark D. Harrison 16 17. Concept of SPC Control SPC Data follows one of several Types of Distributions When only Common Cause Variation is present data is very predictable When Special Cause Variation is present data outside the control limits is statistically rare4/10/2012Author: Mark D. Harrison 17 18. Sources of Process Variability People Every Person is different Material Every piece of material/item/tool is unique Methods No one does something exactly the same way Measurements Individual instruments perform differently Environment - the weather changes! Sound familiar ? (Think Fishbone Diagram)4/10/2012 Author: Mark D. Harrison18 19. Process Variability Two types of Variability Exist in a Process Common Cause Variability Special Cause Variability4/10/2012 Author: Mark D. Harrison 19 20. Common Cause Variability Is characterized as Inherent or Natural Variability Background Noise Cumulative effect of many small, unavoidable causes Stable System of Chance Causes If a Process is operating with only chance cause of variation present it is said to be in Statistical Control4/10/2012Author: Mark D. Harrison20 21. Common Cause Variability Examples Repeatability of Manually Set Controls Change in Temperature from Ventilation System Barometric Pressure and Humidity Repeatability of Computer Set Controls4/10/2012Author: Mark D. Harrison21 22. Special Cause Variability Is seen as Large Variability compared to Background Noise Unacceptable Level of Process Performance Also known as Assignable Cause Sources of Special Cause Variability Improperly Adjusted or Controlled Machines Operator Errors Defective Raw Material A Process that is operating in the presence of Assignable Causes is said to be Out of Control4/10/2012 Author: Mark D. Harrison 22 23. Special Cause Variability Examples Broken Regulator provides too high Air Pressure Shorting Transformer provides too low Voltage Metal being welded in Contaminated Chemicals used for Processing are Expired Operator Loading a Tool Incorrectly4/10/2012Author: Mark D. Harrison23 24. Common and Special Cause Variability Special CauseVariability Common Cause Variability4/10/2012 Author: Mark D. Harrison24 25. Variability Reduction /Process ImprovementSelect a CommonUpdate SPC Charts andCause to ImproveProcess DocumentsValidate and ImplementIndentify MechanismImprovement for Improvement 4/10/2012 Author: Mark D. Harrison 25 26. Variability Reduction Levels Special Cause Variability Common Cause Variability ReactiveProactive4/10/2012 Author: Mark D. Harrison26 27. Six Sigma and SPCWith a +/- 1.5 Sigma Shift there is little change in theamount that goes beyond the USL/LSL of a process4/10/2012Author: Mark D. Harrison27 28. How SPC Chart Hierarchy is Structured SPC Charts should be organized in this hierarchy Process Tool 145V, Welder # 2, etc.. Part Bolt, Nitride Deposition, Cable Type Y, etc.. Characteristic Length, Thickness, Resistance, etc.. This is done to ensure SPC control integrity Data from different process tools is never mixed with other process tools (Part is top of Hierarchy) This combines Special and Common Cause Variability from several sources Root Cause Analysis requires you to fragment and reassemble the data to get a true picture SPC Limits and Run Rules will not function properly4/10/2012 Author: Mark D. Harrison 28 29. Example SPC Chart Hierarchy Tool287BDrill Machine Part Top PlateEnd Plate Side PlateHole Diameter XBAR/RHole Diameter XBAR/RHole Diameter XBAR/RHole Depth XBAR/RHole Depth XBAR/R Hole Depth XBAR/RCharacteristic 4/10/2012Author: Mark D. Harrison 29 30. Where to put SPC / What to Chart ? Key Process Indicators Engineering Specifications Customer Requirements Usually Chart the Output of an Individual Process Can Chart Tool / Process beyond KPIs for even better Process Performance Use a SIPOC and FishBone Diagram to determine if any other Variables need to be Charted4/10/2012Author: Mark D. Harrison 30 31. Use a SIPOC DiagramSPC will look at the Input, Process and Output VariablesSome of the Inputs may have been verified by the previous process4/10/2012 Author: Mark D. Harrison31 32. Modify SIPOC format when neededKey ProcessIndicatorsPotential IncomingSpecial Causes4/10/2012Author: Mark D. Harrison 32 33. Fishbone Diagrams help Indentify Variables4/10/2012 Author: Mark D. Harrison 33 34. Process Specifications Process specifications need to be reviewed to ensure They have been generated through Design andEngineering work The process is capable of meeting the specifications They are optimized for desired characteristics There are no conflicts with other specifications Ask Where did they come from? How were they derived?4/10/2012 Author: Mark D. Harrison 34 35. Design of Experiments (DoE) Should have been used to define Process Specifications Current Specs need to be researched to ensure how they were developed and validated Look for objective evidence on how the specs were developed Engineering Process Documents Engineering/Design Reports Industry Process Practices There should be an information trail leading to the how and why the specs were developed4/10/2012 Author: Mark D. Harrison 35 36. Design of Experiments Tools4/10/2012 Author: Mark D. Harrison 36 37. Design of Experiments ResultsYou want to identify the And optimize for desiredimportant process variablesresult (Max/Min/Uniformity,and interactions etc) and use the data to define your process specs4/10/2012 Author: Mark D. Harrison 37 38. Rational Subgroups Used when taking samples of a Process Design to Detect Process Shifts Maximize Differences between Subgroups if AssignableCause are Present Minimize Differences within Subgroups if AssignableCause is Present Ensure all Sample Data is from the Same Common Cause Sources Samples close in Time together Process Stream Samples close in Space/Location Process Event4/10/2012Author: Mark D. Harrison38 39. Rational Subgroups Examples Snapshot - Process Space Sampling 5 chip features on a wafer Sampling 5 drill holes on a cover plate Random - Process Stream Sampling the last 5 parts from a punch tool Sampling every 10th Hesreys Kiss for weight4/10/2012Author: Mark D. Harrison39 40. Rational Subgroups and Charting Ensure All Common Cause Variability Sources are the Same for each member of the Subgroup DO NOT group units made from Different Process Tools This Drill Press has 4 Different Spindles and would be considered 4 Different Process Tools4/10/2012Author: Mark D. Harrison 40 41. Snapshot vs Random SampleMean is SensitiveRange is SensitiveProcess SpaceProcess Stream4/10/2012 Author: Mark D. Harrison41 42. Integrated Information Systems Management Information Systems (MIS) have become increasingly integrated Data can come from multiple sources and reside in a single database What used to be shuffled with paper is now instantaneous through network connected devices Communications to the outside world (operators/engineering/management) takes place through applications/software algorithms first4/10/2012Author: Mark D. Harrison42 43. Management Information Systems Diagram4/10/2012 Author: Mark D. Harrison 43 44. Integrate SPC Data into Operations Make the Voice of the Process visible to Stakeholders Create procedures that uses SPC data on a regular basis Create a process that ensures problems are solved and improvements are made4/10/2012 Author: Mark D. Harrison 44 45. Example Voice of the Process Data % Exceptions works well as Indicator No need to recalculate Process Capabilities weekly SQL Query updated with process additions/deletions as needed Weekly SQL Database Query run against SPC Data Data is formatted by Department and % Exceptions Each Dept works on the Highest % SPC Exception Charts and reports to their Management Top 5 overall performers are reviewed at the weekly Controls Steering Committee meeting4/10/2012 Author: Mark D. Harrison 45 46. Controls Steering Committee CSC ensure involvement of Management at higher levels Team usually consists of: Management Engineering Manufacturing Engineering Six Sigma/SPC Top Problem SPC charts are reviewed for Capture of Product Root Cause Analysis Solution Implementation and Results Offer additional suggestions / improvements4/10/2012 Author: Mark D. Harrison 46 47. How to Improve your Processes Improve Process Metrology Improve Processing Technologies Make Charts more Sensitive Utilize Modified Control Limits/Reject Limits Utilize Feed-back/Feed-forward Algorithms Utilize Tool Control Utilize Delta from Expected Charting Utilize Delta from Target Charting4/10/2012 Author: Mark D. Harrison 47 48. Improve Metrology Improve Gauge working environment Fixturing for Measurements New Gauges or Instruments Network / SPC Connections4/10/2012 Author: Mark D. Harrison 48 49. Metrology Accuracy and Precision Best Worst4/10/2012 Author: Mark D. Harrison 49 50. Gage R&R Variability Gage R&R Variability is included when you calculate SPC Limits for a Process Gage R&R Variability Reduces your Process Capability Reducing Gage R&R Variability will provide a more accurate picture of your Process4/10/2012Author: Mark D. Harrison 50 51. Process Capability Due to Gage R&RCommon CauseVariability Contributor10% or better Gage R&R isaccepted standard for performance4/10/2012Author: Mark D. Harrison 51 52. Gage R&R Effect on Measurements4/10/2012 Author: Mark D. Harrison 52 53. Improve Metrology Working Environment Ensure Temperature and Humidity of workspace is held as constant as possible Improve stability of inputs required by the Gage Electrical power conditioning Pneumatic improve flow/pressure regulation Hydraulic improve flow/pressure regulation Optical reduce dust/particles4/10/2012 Author: Mark D. Harrison53 54. Improve / Create Fixturing This ensures more consistent Measurements Reduced Person-to-Person Variation Measurements are always taken at the same Locations using the same Techniques Perform a Gage R&R Study and closely observe how things are done and integrate this information into the Fixture Design and Use Instructions4/10/2012 Author: Mark D. Harrison 54 55. Review Measurement Patterns and Analysis Ensure Data generated makes Statistical sense Ensure Data is not Biased Ensure Data Correctly represents what you are trying to measure Examine Alternative Strategies Example Break up and Chart as two measurements Main area or surface process performance Edge of area alignment or processing issues4/10/2012 Author: Mark D. Harrison55 56. Measure Pattern Review 13 sites Biases data towards center of wafer Desensitized to Edge variation 19 sites makes each point represent ~ same area4/10/2012 Author: Mark D. Harrison 56 57. Metrology Network/SPC Connections Most modern metrology systems come equipped with network connection capability or can easily be adapted for connection Micrometers Volt Meters Analyzers Etc.. Most need a computer to support network operation Once connected data can be sent to databases and applications of your choosing4/10/2012Author: Mark D. Harrison 57 58. Connect Metrology to Network/SPC SystemData Sent at a push of a ButtonNo Delay for OOC NotificationNo Data Transcription ErrorsFaster Measurements4/10/2012 Author: Mark D. Harrison 58 59. Automated Metrology Place Item, Start, and Walk Away Automatic, Pre-Programmed Measurements Connected to SPC/Network Systems Instant Indications of Go/No-Go Parts Measurement Defect Detection/Classification Some Vendors can Custom Build4/10/2012Author: Mark D. Harrison 59 60. Example of Automated Measurement4/10/2012 Author: Mark D. Harrison 60 61. Improve Process Technology Modify Process / Part to take advantage of New Technologies (Redesign/DOE) Upgrade Current Equipment Purchase Newest Technology Equipment4/10/2012Author: Mark D. Harrison 61 62. Make Charts more Sensitive Add +/- 2 Sigma, +/- 1 Sigma and/or +/- Target Limits Start with +/- 2 Sigma Limits Work up to +/- Target Limits Apply Western Electric Rules (Standard in Current SPC Packages) Remember! As you make charts more sensitive you may be uncovering low lying Special Cause Variation Be prepared for additional Root Cause Analysis activities!4/10/2012 Author: Mark D. Harrison 62 63. Western Electric Chart and Rules4/10/2012 Author: Mark D. Harrison 63 64. Modified Control / Reject Limits Modified Control Limits or Reject Limits Protect the Specification Limits when using an XBAR/R or XBAR/SD set of SPC Charts Indicate when the Spec is being Violated Used Alone or in conjunction with Regular Control Limits Used in Critical Process Applications Used in Metrology Systems with Manufacturer Defined Specs4/10/2012Author: Mark D. Harrison 64 65. MCL / Reject Limits Protect the Spec Why Specs are not Out of Spec shown on Mean ChartsIn Spec4/10/2012Author: Mark D. Harrison 65 66. MCL / Reject Limit Calculations4/10/2012 Author: Mark D. Harrison 66 67. MCL / Reject Limits and CapabilityCp = ~2+ Cp = 1 Cp = Less than 1 Upper SpecMCL Calculated Upper CLfrom Spec Upper MCLTargetLower MCLCL Calculatedfrom TargetLower CLLower SpecLots of room between No room betweenOverlap betweenCL s and MCLsCL s and MCLsCL s and MCLs*Most Likely will Violate4/10/2012Author: Mark D. Harrison Spec BEFORE CL Flags 67 68. Feedback and Feed-forward Mechanisms Feedback provides information about the process outcome that is used to modify the that particular process for the next unit or event Feed-forward provides information about the current process that is used by the next process for settings or initial conditions4/10/2012 Author: Mark D. Harrison68 69. Feedback Mechanisms Algorithms triggered to run from an SPC Exception Usually Process Settings are recalculated based on a known characteristic of the process Time Temperature Pressure Other Actions can also be taken Put Product on Hold Change a Process Route Send emails/pages/phone messages4/10/2012 Author: Mark D. Harrison69 70. Example Feedback Mechanism4/10/2012 Author: Mark D. Harrison 70 71. Feed-forward Mechanisms Algorithms triggered to run when SPC data is transmitted to the data base whether there was an SPC exception or not Usually Process Data is sent to a Database from the 1st process and accessed for during processing by the next process4/10/2012 Author: Mark D. Harrison 71 72. Example Feed-forward MechanismDeposition Tool Chem-Mech Polish ToolThickness of Deposition isThickness ofread from the database andDeposition is sent toProcess Database the Polish tool uses the infothe Process Databaseto calculate Rough Polishtime before changing toFine Polish time4/10/2012 Author: Mark D. Harrison72 73. Tool Control Many more process variables can be charted Usually found on expensive high use equipment Simple Type Older Equipment Data is sent to SPC through a network/computerconnection and provides a summary or data file that canbe used for SPC control Complex Type Newer Equipment SPC is imbedded in the process tool software Monitors and flags SPC exceptions at end of process Not the same as the derided APC or Automated Process Control4/10/2012Author: Mark D. Harrison 73 74. Delta from Expected - Simple A Process may change with a known characteristic and may not be controllable using normal SPC charts Oxide Standard gaining Native Oxide thickness Deposition Rate changes due to number of runs If the process is repeatable and consistent it might be characterized by a formula Least Squares Fit Line Polynomial Other Line Fitting Formulas This formula can be used to predict where the next point should be and calculate a Delta from Expected4/10/2012 Author: Mark D. Harrison 74 75. Generate the Least Squares Fit Line Least squaredGoodness of Fit needs to be fit line formula high for good performance Least squared fit line4/10/2012 Author: Mark D. Harrison 75 76. Calculate the Delta from Expected Values Once enough data is collected calculate SPC Limits for the chart Least Squares Fit Line4/10/2012 Author: Mark D. Harrison 76 77. Oxide Growth Line Fitting ExampleTime/# of Runs/etc.Time/# of Runs/etc.Original Data does not fit on A Fitted Line/Curve willan SPC chartminimize variability of the Data4/10/2012Author: Mark D. Harrison77 78. SPC Chart - Oxide Growth(2 Charts)Original DataTransformed DataUCL is now a Stop limitTime/# of Runs/etc. Time/# of Runs/etc.Original Data keeps climbing A Fitted Line/Curve willChart set for UCL Exception forminimize variability of the Dataunacceptable levels4/10/2012 Author: Mark D. Harrison78 79. Delta from Expected Complex Example Hot Phosphoric Wet Bench Had scrap issues with Residual Nitride from process Wet bench computer generated Bath Temperature files Able to characterize Etch Rate by Temperature Developed Algorithm to estimate Etch Amount for each Time Interval Summed Total Estimated Etch Amount and sent to SPC chart SPC Exception generated if Estimated Etch Amount is Too Low4/10/2012Author: Mark D. Harrison79 80. Temperature Performance During RunWafers are dropped intotank using a robot armHeaters effect the temperature curve seenwhen wafers are initially submerged4/10/2012 Author: Mark D. Harrison80 81. Hot Phosphoric Etch Performance4/10/2012 Author: Mark D. Harrison 81 82. Wet Bench Temperature Data FileData was access from the tool through the Bench computer and network4/10/2012Author: Mark D. Harrison82 83. Hot Phosphoric SPC Charts - Initial Estimated Etch Amount Bath Temperature Mean Bath Temperature Std Dev4/10/2012 Author: Mark D. Harrison83 84. Delta from Target Used to combine many SPC charts with data entered in irregular intervals with few points Reduces number of SPC Charts required to control all processes on a particular tool (seen 10X in practice) Combines SPC data into longer unbroken strings for better process control Processes are combined that have the same spec limits and general performance levels Real Data is stored in a database for queries and traceability4/10/2012 Author: Mark D. Harrison 84 85. Delta from Target - Photolithography A semiconductor Photolithography tool basically shoots an image onto a resist film The images it prints are usually held to certain specs Block Masks Large, open features Vias/Lines Small, critical dimensions A Photolithography tool can be switched between different products/levels within the product Targets for each feature may differ Specs band (USL-LSL) are the same for many products/levels Delta from Target allows consistent and accurate SPC control with much fewer SPC Charts4/10/2012 Author: Mark D. Harrison85 86. Delta from Target Chart StructurecombinedProcesses A, B and C are 50Process into a single set of SPC Charts 40 A 30Single Chart set keeps product run through 45 tool in correct SPC Date/Time SequenceProcess 35 B 25 40Process 30 C 20100-10 Associated SD Charts are not include to keep diagram as simple as possible 4/10/2012 Author: Mark D. Harrison 86 87. In Conclusion Ensure you have Company/Management Support Ensure your are Charting the Important/Correct Variables Chart/Sample properly Ensure your metrology is capable Use the correct limits for your situation Utilize any Information Systems available Use advanced control techniques where applicable Ensure all areas that use SPC do so on a regular basis and it becomes a part of company culture When Special Cause is down reduce Common Cause Variability4/10/2012Author: Mark D. Harrison87 88. ReferencesIntroduction to Statistical Process Control Douglas C. MontgomeryThe Six Sigma Handbook Thomas PyzdekLean Six Sigma DeMystifiedJay ArthurBasic Statistical Process ControlJack Hunt - IBMIntermediate Statistical Process ControlLenny Dubuque - IBMAdvanced Statistical Process ControlGary Snyder - IBMA Large Scale SPC Implementation using the IBM Multimedia SPC Program Mark Harrisonhttp://www.keyence.com/products/measure/image/im6500/simulation/simulation.phphttp://elonen.iki.fi/articles/centrallimit/index.en.html#demohttp://en.wikipedia.org/wiki/Illustration_of_the_central_limit_theorem4/10/2012Author: Mark D. Harrison 88