SPC英文版教材.ppt

1、Module 3: Statistical Process Control (SPC) Methodology,2,PCS Elements,Create Measurement Plan,Establish Monitor (SPC),Implement Response Flow Checklist (RFC),Element 1,Element 2,Element 3,3,Contents,Introduction 簡介 What is SPC 什麼是SPC? What is Stability 什麼是穩定性? What is a Control Chart 什麼是管制圖 How to

2、Set-up a Control Chart 如何建立管制圖 Type of Control Charts Available 管制圖的種類 How to Calculate the Control Limits 如何計算管制界限 SPC Trend Rules SPC法則 When to Revise Control Limits 何時重新計算管制界限 Process Capability Study 制程能力研討 Spec limits VS Control Limits 規格界限 vs. 管制界限 Stability VS Capability 穩定性 vs. 能力 Control Ch

3、art Reduction/Elimination 減少管制圖 SPC Expectations,4,What is SPC?,Statistical 統計 Anything that deals with the collection, analysis, interpretation some drift gradually, others may have occasional sudden changes in direction between periods of relative stability 傾向隨時間漂移,一般逐漸改變,有的會突然改變方向,Autocorrelation

4、關連性,35,Caution When Using MR Method,If there is autocorrelation, MR(Summary Stat) will underestimate the true process variation & the control limits will be too narrow 如果數據有關連性,MR方法會低估制程變異,界限會太窄. If autocorrelation is evident, use Sigma (Std Dev) Method for control limits computation (Refer to Appen

5、dix B) 如果確定數據有關連性,使用sigma方法計算管制界限.,36,sigma方法計算管制界限, N = the number of lots, = the mean from the ith lot (i = 1,2,.,N), = the mean of the lot means, = the standard deviation of the lot means, where,Compute the control limits using:,37,Control Chart Trend Rules 趨勢規則,Purpose: Improve the responsivenes

6、s of the control chart 增進管制圖的反應 Detect more subtle shifts in the process more quickly 更快地察覺制程中細微的變化 Detect irregularities beyond normal 3 that indicate non-randomness in process 察覺在正常3 界限內的非隨機性變化,38,How to Interpret a Control Chart?,It is based on the Normal Distribution.,39,SPC Trend Rules,Rule# 1:

7、 A single point beyond either control limit 規則1: 單獨點超出管制界限 Uses: Detects very large/sudden shifts 用途: 察覺重大/突然的偏差 False alarm rate: 0.27% 機率: 0.27% Example:,40,SPC Trend Rules,Rule# 2: 9 consecutive points on the same side of the centerline 規則2: 連續9點在中心線同側 Uses: Detects small shifts or trends 用途: 察覺微

8、小/趨勢性的偏差 False alarm rate: 0.39% 機率:0.39% Example:,41,SPC Trend Rules,Rule# 3: 6 consecutive points steadily increasing or Decreasing 規則3: 連續6點持續上升或下降 Uses: Detects strong trends 用途: 察覺強烈趨勢 Example:,42,SPC Trend Rules,Rule# 4: 14 (or more) consecutive points are alternating up and down. 規則4: 相鄰14點規則

9、性上下變動 Uses: Detects systematic effects, such as alternating machines, operators, suppliers, etc. 用途: 察覺系統性的效性. 如交替機台,人員,供應商等. Example:,43,SPC Trend Rules,Rule# 5: 2 out of 3 consecutive points at least 2 std dev beyond the centerline, on the same side 規則5: 連續同側3點中,2點落在同側2標準差之外 Uses: Detects large ch

10、anges 用途: 察覺大改變 False alarm rate: 0.30% 機率: 0.30% Example:,44,SPC Trend Rules,Rule# 6: 4 out of 5 consecutive points on the chart are more than 1 std dev away from the CL 規則6: 連續同側5點中,4點超出1標準差 Uses: Detects moderate-sized changes 用途: 察覺中度改變 False alarm rate: 0.53% 機率: 0.53% Example:,45,SPC Trend Rul

11、es,Rule# 7: 15 (or more) consecutive points are within 1 std dev of the CL 規則7: 連續15點在1標準差內 Uses: Detects a decrease in process variation 用途: 察覺制程變異的降低 Example:,46,SPC Trend Rules,Rule# 8: 8 (or more) consecutive points are on both sides of the CL, but none are within 1 std dev of it. 規則8: 連續不同側8點,沒

12、有一點在1標準差內. Uses: Detects an increase in process variation 用途: 察覺制程變異的增加 Example:,47,Selection of Trend Rules,Using a large # of trend rules is unwise, since each trend rule has a false alarm rate & the cumulative false alarm rate can be very large 使用大量的判斷規則是不智的,因為每一條都有誤判的機率,累積起來將可能很大 False alarm rat

13、e is the frequency of control chart signals when nothing at all is actually wrong with the process 誤判機率指的是制程一點問題都沒有,但規則確告訴我們有問題. False alarms are undesirable: 誤判警告並不是我們想要的 They reduce productivity & increase costs from unnecessarily shutting down the process 不必要的中斷生產造成產能降低&增加成本 They erode confidence

14、 in control charts as useful tools. Eventually all out-of-control signals are ignored. As a result, quality, productivity, & cost will suffer. 侵蝕對管制圖的信心. 最後, 忽視所有失控的信號, 導致品質,產量&成品損失.,48,Trend Rule Recommendations 建議,All control charts should at least use the 1st rule: Point UCL or Point LCL 至少須使用第 1

15、 條 For an automated SPC system with automated application of SPC trend rules, its highly recommended to add 5th rule to detect large shifts in mean, (i.e. 2 out of 3 rule) 若spc系統是自動的,非常建議增加第 5 條 Add other rules depending upon process knowledge ability to respond criticality of the monitor sensitivit

16、y requirements for the monitor Intel does recommend as a goal to use rules 1, 2, 5, & 6 when appropriate Intel建議使用1, 2, 5, & 6 條,49,Trend Rule Recommendations,Only use the trend rules that signal process instabilities for which you are capable of responding 在你有能力反應處理的不穩定制程,才使用趨勢規則 Justification need

17、ed for not using other SPC trend rules Std dev & range charts may choose not to react to Point LCL, however a LCL on these charts can be valuable for detecting metrology problems or unexpected process improvement 若點LCL可能被認為不須要處理,然而這些點卻可以告訴我們察覺儀器量測問題或非預期性的制程改善,50,Treatment of OOC Points,Every OOC poi

18、nts should have a completed RFC where applicable & document actions taken 每一失控點須完成RFC,它包括執行對策 Do necessary adjustment or correction to the equipment or process as instructed in the RFC where applicable 依RFC指示來對設備或制程作必要的調整或改正,51,Treatment of OOC Points,The corresponding equipment shall be shutdown 相關

19、的設備應停機 if the OOC situations cannot be solved & non-conforming parts are produced 假如失控點情況無法解決&不合格品被產生 per control chart RFC If any individual data values falls outside the process specification limits, then disposition the affected material per the requirements specified in the respective process sp

20、ecification or put the material on-hold for engineering disposition.,52,When to Revise Control Limits?,Control limits should be reviewed periodically (monthly or quarterly) off-line to assess if they need updating. A minimum of 30 data points are needed before re-calculating control limits. 應定期(月,季)

21、審查管制表來確定是否須要更新. 重新計算前須至少收集30數據點 Re-compute control limits when a significant process change is implemented current control limits too wide or too narrow 重新計算管制界限 制程重大改變 管制界限太寬或太窄,53,Change Ratio,A change ratio may be utilized to detect when the current limits may be inappropriate 可以計算change Ratio 來判

22、斷目前管制界限是否合適 LCL Change Ratio = (LCLcurrent - LCLcalc) / srun-run(calc) UCL Change Ratio = (UCLcalc - UCLcurrent) / srun-run(calc) A large negative change ratio suggests tightening a control limit 若負值很大,建議縮小管制界限 A large positive change ratio suggests widening the control limit 若正值很大,建議放寬管制界限 Note: sr

23、un-run(calc) = 2.66MR / 3,54,Guidelines for Interpreting Change Ratios,Potentially a problem : |Change Ratio| 1.0 有潛在問題, 1 Definitely a problem : |Change Ratio| 1.5 確定有問題 1.5,Given Thickness.jmp example: UCLcurrent = 130.0 LCLcurrent = 70.0 Newly collected data resulted the following: UCLcalc = 119.

24、41 LCLcalc = 78.72 srun-run(calc) = 2.66 MR / 3 = 20.35 / 3 = 6.78 UCL Change Ratio= (UCLcalc - UCLcurrent) / srun-run(calc) = -1.56 LCL Change Ratio = (LCLcurrent - LCLcalc) / srun-run(calc) = -1.28 = Indicates a need to change the current control limits!,Change Ratio Example,56,Process Capability,

25、Process capability is the ability of a process to meet specifications. A process must be stable before its capability can be computed. Not Capable Capable A capability index is a statistic that quantifies & describes the capability of a process,57,Specification Limits,The region where product is kno

26、wn to function well in terms of performance, yield, reliability, or other desired outcome Acceptable range of values for a product parameter Define what is acceptable/unacceptable product Determined by Design requirements & simulation models Engineering judgement (typically product eng. & integratio

27、n) Customer agreement/requirements Data driven validation: Process window characterization Historical data identifying in-line or EOL problems Used to determine process capability,58,Control Limits,Calculated from data, based on actual process performance Describe the natural range of performance of

28、 a stable process Describe the amount of natural process variation Used to determine process stability,59,Spec Limits vs. Control Limits,Spec Limits Based on performance required of the product What the customer wants - “what we want” Tells us when to disposition the product/material Apply only to i

29、ndividual (raw) data values,Control Limits Based on actual historical process performance What the process delivers - “what we get” Tells us when to take action on the process/equipment Apply to summary statistics (e.g. : X-bar, std dev, range, etc. charts),Never use spec limits on a control chart!,

30、60,Stability vs. Capability,A process is said to be in statistical control when the only source of variation is of natural causes, (i.e. no special causes variation present) A process is said to be capable when variation from natural causes is reduced such that it can meet product specification tole

31、rance when the control limits are well within the specification limits A process is said to be not capable if the control limits are outside the specification limits,61,Exercise 3,0,2,4,6,8,Interpretation : Y/N _Stable _Capable,Interpretation : Y/N _Stable _Capable,62,Interpretation : Y/N _Stable _C

32、apable,Interpretation : Y/N _Stable _Capable,Exercise 3,63,Measuring Process Capability,A minimum of 30 data points are needed before calculating process capability indices Always use a histogram or with specification limits to visually represent the process distribution & capability,64,Measuring Pr

33、ocess Capability: Cpk,Cpk is a common measure of process capability: Cpk = minimum (Cpu, Cpl) USL - XX - LSL Cpu = Cpl = 3sindiv 3sindiv where: USL, LSL - spec limits for individual data values sindiv = std dev of individual (raw) measurements,65,Cpk compares the upper half of the distribution to th

34、e upper spec & lower half of the distribution to the lower spec,Measuring Process Capability: Cpk,66,The value of Cpk is affected by: where the process is centered process variation The higher the Cpk value, the better it is For one-sided specification, Cpk = Cpu if only USL exists Cpk = Cpl if only

35、 LSL exists,Measuring Process Capability: Cpk,67,A simple measure of process capability is Cp USL - LSL Cp = - 6sindiv Cp does not consider where the process is centered, therefore it assumes the process is centered on target. Therefore, Cp Cpk. Cp is often called process potential: How the process

36、could perform if it was on target Cp can only be computed for processes with two-sided spec limits,Measuring Process Capability: Cp,68,Example,Cpk = 1.44, Cp = 1.45 Target & mean are close together: Cpk & Cp are similar LSL = 700, Target = 1000, USL = 1300 Mean = 1003.8, Std dev = 68.6,Cpk = 0.52, C

37、p = 0.87 Target & mean are offset significantly: Cpk is much less than Cp LSL = 700, Target = 1000, USL = 1300 Mean = 1196, Std dev = 115.0,69,Exercise 5,For each of the 4 distributions below, check if you think Cpk 1.3,70,Cp & Cpk Assumptions,Process Stability The process must be in statistical con

38、trol Representative Samples The obtained samples are representative of the population. Random sampling is important in this regard. Normality The underlying process distribution is normal Note: If the assumptions are not met, the process capability indices calculated are misleading & do not accurate

39、ly indicate the capability of the process. See your site statistician for advice on calculation methods when assumptions are violated.,71,Test for Cp & Cpk Assumptions,Process Stability (no outliers) Screen out outliers from the database before computing Cpk by using control chart. Any point beyond

40、either control limit is an outlier. Report number of outliers screened. Normality Plot a normal probability plot of the data or overlay a normal curve over the histogram. Normally distributed data will roughly fall on a straight line. Test for normality by using Shapiro-Wilk W test in JMP,72,No Larg

41、e Outliers,The example below illustrates the Cpk results with & without 2 large outliers.,With outliers: Cpk = 0.52,2 outliers points removed: Cpk = 0.88,73,Are Data Normally Distributed?,Processes with large systematic effects or other non-normal signature often result in an underestimated Cpk.,74,

42、Cpk vs. DPM Out-of-Spec,If we assume that the process distribution is Normal, we can relate Cpk to the estimated dpm of product that is out-of-spec.,DPM = Defectives Per Million (i.e. the expected number of defective parts or units of product per million produced).,Examples of Determining the DPM Ou

43、t-of-Spec from Capability Indices,76,Open the dataset Thickness.jmp, which contains 150 thickness measurements taken over 30 lots. The spec limits for thickness are 80 mils to 120 mils. JMP Analysis Select Distribution from the Analyze Menu. Select thickness and click Y, Columns. Click OK. Select Ca

44、pability Analysis from the pop-up menu in the output window of the histogram. Enter the spec limits. Click OK. Note : The target value is only used for the calculation of Cpm, which is not discussed here. Select Fit Distribution from the pop-up menu in the output window of the histogram & select Nor

45、mal Curve. Double Click on the X-axis. Enter 75 for the axis minimum. Enter 125 for the axis maximum. Note: 1. The x-axis scale does not change automatically to show the spec limits. 2. Multiple responses can be analyzed simultaneously in the same output window.,Computing Capability Indices using JM

46、P,77,The graph beside is a histogram of 150 plating thickness measurements taken from several production lots. LSL = 80, USL = 120 In the summary tables, note that the value of Cpk = 1.038 agrees with the histogram - this process is not capable.,% of product out-of-spec = 0.092 + 0.031 = 0.123% = 12

47、30 dpm,Computing Capability Indices using JMP,78,Exercise 6,Adhesive bleedout has an upper spec limit of 500 microns (no lower spec limit). Data collected from 50 lots (250 parts total) is contained in the file Exer6.jmp What is the Cpk of the process with respect to adhesive bleedout? What long-ter

48、m average percentage of parts do you estimate will be out-of-spec for adhesive bleedout?,79,Intels Goal on Process Capability,The goal for a capable process is to have the Cpk 1.33 If the minimum Cpk of 1.33 is not met, the team must generate plans to improve the process capability,80,Control Chart

49、Reduction/Elimination,For processes which are very stable & capable over a period of time, its recommended that the existing SPC monitor sampling frequency be re-evaluated & reduced if appropriate. Sample size can be reduced accordingly if low within-lot variation compared to between-lot variation e

50、ncountered with the condition that the new sampling plan would still be able to detect the assignable causes. Some of the considerations for monitor elimination are: Level of stability, capability, risks, time to end of product life, data linking product characteristics/process parameters No real-time decisions need to be made from the data, such as no control limits or immediate reaction to control limits violation, dat