Mark Harrison SPC Implementation

Mark D. Harrison
sixsigmadude@gmail.com

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 system

4/10/2012 Author: Mark D. Harrison 2

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 Process


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 Process”


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 fields


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
organization


Management Leadership
 Need “Top Down” buy-in
 Communicate Business Justification to Employees
 Drive Cultural Change
 Prevent Internal “Sabotage”
 Put SPC metrics in “Performance Plans”


A Team Approach
 Include all Stakeholders
 Break down “Silos”
 Define “What’s in it for me” for each Stakeholder
 Utilize the Strengths of all your Resources


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..)


Emphasis on Reducing Variability
 Reduce Scrap
 Reduce Rework
 Improve Process Capabilities
 Reduced Costs = Higher Profits


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)


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 people’s work
 Include people who did the work to present to higher
level management and even write papers


Example Company Newsletter


What does SPC look like ?
Upper Control Limit

Process Average or Target

Lower Control Limit

Sample Number or Time


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 Units


Selecting Which SPC Chart to Use
n = 2 to 9 Average, Range

Average, Sigma
n = 10 or more
Measurements
non-normal Run Chart

normal Moving Range

n fixed np Chart
pieces or
units p Chart
Counts n varies

n fixed c Chart
occurrences
n varies u Chart

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 rare


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)


Process Variability
 Two types of Variability Exist in a Process
 Common Cause Variability
 Special Cause Variability


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 Control


Common Cause Variability Examples
 Repeatability of Manually Set Controls
 Change in Temperature from Ventilation System
 Barometric Pressure and Humidity
 Repeatability of Computer Set Controls


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 Control


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 Incorrectly


Common and Special Cause Variability

Special Cause
Variability

Common Cause
Variability


Variability Reduction /Process Improvement

Select a Common
Update SPC Charts and
Cause to Improve
Process Documents

Validate and Implement Indentify Mechanism
Improvement for Improvement


Variability Reduction Levels
Special Cause Variability Common Cause Variability
Reactive Proactive


Six Sigma and SPC
With a +/- 1.5 Sigma Shift there is little change in the
amount that goes beyond the USL/LSL of a process


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 properly


Example SPC Chart Hierarchy
Tool 287B
Drill Machine

Part

Top Plate End Plate Side Plate

Hole Diameter XBAR/R Hole Diameter XBAR/R Hole Diameter XBAR/R

Hole Depth XBAR/R Hole Depth XBAR/R Hole Depth XBAR/R

Characteristic


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 Charted


Use a SIPOC Diagram

SPC will look at the Input, Process and Output Variables
Some of the Inputs may have been verified by the previous process


Modify SIPOC format when needed
Key Process
Indicators

Potential Incoming
Special Causes


Fishbone Diagrams help Indentify Variables


Process Specifications
 Process specifications need to be reviewed to ensure
 They have been generated through Design and
Engineering 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?


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 developed


Design of Experiments Tools


Design of Experiments Results

You want to identify the And optimize for desired
important process variables result (Max/Min/Uniformity,
and interactions etc) and use the data to
define your process specs

Rational Subgroups
 Used when taking samples of a Process
 Design to Detect Process Shifts
 Maximize Differences between Subgroups if Assignable
Cause are Present
 Minimize Differences within Subgroups if Assignable
Cause 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 Event


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 Hesrey’s Kiss for weight


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 Tools


Snapshot vs Random Sample

Mean is Sensitive Range is Sensitive
“Process Space” Process Stream


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 first


Management Information Systems Diagram


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 made


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 meeting


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 / improvements


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 Charting


Improve Metrology
 Improve Gauge working environment
 Fixturing for Measurements
 New Gauges or Instruments
 Network / SPC Connections


Metrology Accuracy and Precision
Best

Worst


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 Process


Process Capability Due to Gage R&R

Common Cause
Variability Contributor

10% or better Gage R&R is
accepted standard for performance

Gage R&R Effect on Measurements


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/particles


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 Instructions


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 issues


Measure Pattern Review
 13 sites Biases data towards center of wafer
 Desensitized to Edge variation
 19 sites makes each point represent ~ same area


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 choosing


Connect Metrology to Network/SPC System
Data Sent at a push of a Button
No Delay for OOC Notification
No Data Transcription Errors
Faster Measurements


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 Build


Example of Automated Measurement


Improve Process Technology
 Modify Process / Part to take advantage of New
Technologies (Redesign/DOE)
 Upgrade Current Equipment
 Purchase Newest Technology Equipment


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!


Western Electric Chart and Rules


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 Specs


MCL / Reject Limits Protect the Spec
Why Specs are not
Out of Spec shown on Mean Charts

In Spec


MCL / Reject Limit Calculations


MCL / Reject Limits and Capability
Cp = ~2+ Cp = 1 Cp = Less than 1
Upper Spec

MCL Calculated Upper CL
from Spec

Upper MCL

Target
Lower MCL
CL Calculated
from Target
Lower CL

Lower Spec
Lots of room between No room between Overlap between
CL s and MCLs CL s and MCLs CL s and MCLs
*Most Likely will Violate
4/10/2012 Author: Mark D. Harrison Spec BEFORE CL Flags 67

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 conditions


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 messages


Example Feedback Mechanism


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
process


Example Feed-forward Mechanism
Deposition Tool Chem-Mech Polish Tool

Thickness of Deposition is
Thickness of read from the database and
Deposition is sent to Process Database the Polish tool uses the info
the Process Database to calculate Rough Polish
time before changing to
Fine Polish time


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/computer
connection and provides a summary or data file that can
be 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 Control


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 Expected


Generate the Least Squares Fit Line
Least squared “Goodness of Fit” needs to be
fit line formula high for good performance

Least squared
fit line


Calculate the Delta from Expected Values
Once enough data is collected
calculate SPC Limits for the chart

Least Squares
Fit Line


Oxide Growth Line Fitting Example

Time/# of Runs/etc. Time/# of Runs/etc.

Original Data does not fit on A Fitted Line/Curve will
an SPC chart minimize variability of the Data


SPC Chart - Oxide Growth(2 Charts)
Original Data Transformed Data

UCL is now a “Stop” limit

Time/# of Runs/etc. Time/# of Runs/etc.

Original Data keeps climbing A Fitted Line/Curve will
Chart set for UCL Exception for minimize variability of the Data
unacceptable levels

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 Low


Temperature Performance During Run

Wafers are dropped into
tank using a robot arm

Heaters effect the temperature curve seen
when wafers are initially submerged

Hot Phosphoric Etch Performance


Wet Bench Temperature Data File

Data was access from the tool through the Bench computer and network

Hot Phosphoric SPC Charts - Initial
Estimated
Etch
Amount

Bath
Temperature
Mean

Bath
Temperature
Std Dev


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
traceability


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 Charts


Delta from Target Chart Structurecombined
Processes A, B and C are
50
Process into a single set of SPC Charts
40
A
30
Single Chart set keeps product run through
45 tool in correct SPC Date/Time Sequence
Process
35
B
25

40
Process
30
C
20

10
0

-10
Associated SD Charts are not include to keep diagram as simple as possible

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 Variability


References
Introduction to Statistical Process Control Douglas C. Montgomery
The Six Sigma Handbook Thomas Pyzdek
Lean Six Sigma DeMystified Jay Arthur

Basic Statistical Process Control Jack Hunt - IBM

Intermediate Statistical Process Control Lenny Dubuque - IBM

Advanced Statistical Process Control Gary Snyder - IBM

A Large Scale SPC Implementation using the IBM Multimedia SPC Program
Mark Harrison

http://www.keyence.com/products/measure/image/im6500/simulation/simulation.php
http://elonen.iki.fi/articles/centrallimit/index.en.html#demo
http://en.wikipedia.org/wiki/Illustration_of_the_central_limit_theorem

Mark Harrison SPC Implementation

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