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Intro to burn in & ess quantification

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When scientifically planned and conducted, Burn-In and Environmental Stress Screening (ESS) provide one of the most effective methods of reliability screening at the component, sub-assembly, assembly, and system levels. Burn-in and ESS have been practiced in industry for many years, yet they are often conducted without scientific understanding, design, planning, quantification, and optimization. Based on his two co-authored books in the subject, the author of this talk presents a high-level introduction to the quantification of reliability-centered Burn-In and ESS.

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Intro to burn in & ess quantification

  1. 1. An Introduction to Quantification  of Reliability‐Centered Burn‐In and  of Reliabilit Centered B rn In and ESS (以可靠性为中心的老炼和环 境应力筛选定量分析简介) 境应力筛选定量分析简介 Feng‐Bin (Frank) Sun(孙凤斌),  Ph.D. ©2012 ASQ & Presentation Sun Presented live on May 20th, 2012http://reliabilitycalendar.org/The_Reliability_Calendar/Webinars_liability Calendar/Webinars ‐_Chinese/Webinars_‐_Chinese.html
  2. 2. ASQ Reliability Division  ASQ Reliability Division Chinese Webinar Series Chinese Webinar Series One of the monthly webinars  One of the monthly webinars on topics of interest to  reliability engineers. To view recorded webinar (available to ASQ Reliability  Division members only) visit asq.org/reliability ) / To sign up for the free and available to anyone live  webinars visit reliabilitycalendar.org and select English  Webinars to find links to register for upcoming eventshttp://reliabilitycalendar.org/The_Reliability_Calendar/Webinars_liability Calendar/Webinars ‐_Chinese/Webinars_‐_Chinese.html
  3. 3. An Introduction to Quantification ofReliability-Centered Burn-In and ESS(以可靠性为中心的老炼和环境应力筛选定量分析简介) Feng-Bin (Frank) Sun, Ph.D. HDD Reliability Engineering HGST, a Western Digital Company
  4. 4. Overview (综述) Why Stress Screen? Definition of ESS and Burn-in and Their Relationship Phenomenological Observations and the Physical Insight of the Failure Process During Screen Flaw-Stimulus Relationships and Typical Stress Screen Types Burn-in and ESS Quantification  Statistical Modeling √  Physical Modeling  Optimum Screening Time Determination √ References 2
  5. 5. Why Stress Screen?(为什么要进行应力筛选?)1. $500 TO $1,500 WORTH OF ELECTRONICS ARE USED IN EACH VEHICLE BY AUTOMOBILE MANUFACTURERS.2. ABOUT 60% OF A MILITARY AIRCRAFTS COST NOW GOES TO ITS ELECTRONIC SYSTEMS.3. "NEVER BUY A CAR MADE ON MONDAY OR FRIDAY!!!"4. OVER HALF THE EFFORT HAS BEEN REPORTEDLY APPLIED TO REWORK IN THE U.S.5. CORRECTIONS OF DEFECTS AT THE MANUFACTURERS FACILITY IS MORE ECONOMICAL THAN SHIPYARD FAILURE CORRECTIONS AND SHIPYARD FAILURE CORRECTIONS ARE MORE ECONOMICAL THAN POST DELIVERY FAILURE CORRECTIONS DURING FIELD OPERATION. 3
  6. 6. Why Stress Screen? (continued) (为什么要进行应力筛选 - 续) 4
  7. 7. Why Stress Screen? (continued) (为什么要进行应力筛选 - 续) 5
  8. 8. What Is ESS? (什么是环境应力筛选?)  Is a process or series of processes.  Involves the tailored applications of environmental stimuli (such as thermal cycling and random vibration, and/or electrical stresses).  To electronic and electromechanical items (parts, modules, units, and systems).  On an accelerated basis, but within design capability.  Ideally at the most cost-effective point of assembly.  To expose, identify and eliminate latent defects. (such defects can’t be detected by visual inspection, or electrical testing and would in all likelihood, if undetected, manifest themselves in the operational or field environment) 6
  9. 9. What Is Burn-In? (什么是老练?) Burn-in is a test performed for the purpose of screening or eliminating marginal devices, those with inherent defects or defects resulting from manufacturing aberrations which cause time and stress dependent failures. -- MIL-STD-883C Burn-in can be regarded as a special case of ESS where the appropriate electrical conditions are combined with the appropriate thermal conditions to accelerate the aging of a component or device. 7
  10. 10. Phenomenological Observations and the PhysicalInsight of the Failure Process during Screen (应力筛选过程中的现象表征以及失效过程的物理机制洞察) Conventional Bathtub Curve Concept The “S”-shaped CDF Pattern Roller-Coaster Failure Rate Curve Stress-Strength Interference and Component Failure Patterns 8
  11. 11. Conventional Bathtub Curve Concept (传统失效率浴盆曲线概念) 1 Quality failures 2 Stress-related failures 3 Wearout failures Early-Failure rate failure Wearout period Useful-life period period 1 3 2 0 Cumulative operating time 9
  12. 12. The S-Shaped CDF Pattern (“S”形累积分布图特征) A cdf plot based on the experimental data of CMOS transistors. 10
  13. 13. Roller-Coaster Failure Rate Curve (“过山车”形失效率曲线特征) Latent defects removed in checkout Latent defects removed in process inspections and tests Failure rate Wearout failures Roller-Coaster curve 0 Cumulative operating time 11
  14. 14. Stress-Strength Interference and Bathtub Curve(应力-强度干涉与浴盆曲线的关系) 12
  15. 15. Flaw-Stimulus Relationships (缺陷与激发因子的关系)1. Patent Defect  flaw which has advanced to the point where an anomaly actually exists ,or  out-of-tolerance, or a specification, condition which can be readily detected by an inspection or a test procedure.2. Latent Defect  Irregularity due to manufacturing processes, or  materials which will advance to a patent defect when exposed to environmental or other stimuli. 13
  16. 16. Flaw-Stimulus Relationships (continued) (缺陷与 激发因子的关系 - 续) Examples of Patent Defect1. Parts 2. Interconnections (1.1) Broken or damaged in (2.1) Incorrect wire termination. handling. (2.2) Open wire due to handling (1.2) Wrong part installed. damage. (1.3) Correct part installed (2.3) Wire shorted to ground due to incorrectly. misrouting or insulation damage. (1.4) Failure due to electrical (2.4) Missing wire. overstress or electrostatic (2.5) Open etch on printed wiring discharge. board. (1.5) Missing parts. (2.6) Open plated through-hole. (2.7) Shorted etch. (2.8) Solder bridge. (2.9) Loose wire strand. 14
  17. 17. Flaw-Stimulus Relationships (continued) (缺陷与 激发因子的关系 - 续) Examples of Latent Defect1. Parts 2. Interconnections (1.1) Partial damage through (2.1) Cold solder joint. electrical overstress or (2.2) Inadequate/excessive solder. electrostatic discharge. (2.3) Broken wire strands. (1.2) Partial physical damage during (2.4) Insulation damage. handling. (2.5) Loose screw termination. (1.3) Material or process induced hidden flaws. (2.6) Improper crimp. (1.4) Damage inflicted during (2.7) Unseated connector contact. soldering operations (excessive (2.8) Cracked etch. heat). (2.9) Poor contact termination. (2.10) Inadequate wire stress relief. 15
  18. 18. Flaw-Stimulus Relationships (continued) (缺陷与 激发因子的关系 - 续) Screening processes for IC failure mechanismsScreening Failure Test Mechanism Substrate Bulk Substrate Bonding Particle External mounting silicon surface and contamination Seal Package lead Thermal Electrical defects defects defects wire + extraneous defects defects defects mismatch stability material Internal      visual exam External    visual examStabilization     bake Thermal       cycling Thermal       shock Centrifuge     Shock     Vibration     X ray      Burn-in     Leakage tests  16
  19. 19. Flaw-Stimulus Relationships (continued) (缺陷与激发因子的关系 - 续)* PIND: particle impact noise detector 17
  20. 20. Typical Stress Screen Types (典型应力筛选类型)1. Temperature cycling2. Random vibration3. High temperature burn-in4. Electrical stress5. Thermal shock6. Sine-wave vibration, fixed frequency7. Sine-wave vibration, swept frequency8. Low temperature9. Combined environment 18
  21. 21. Typical Stress Screen Types (continued) (典型应力筛选类型 – 续) An Example of Input Power Spectral DensityAn Example of Input Temperature Profile for for Random Vibration Temperature Cycling 19
  22. 22. Governing Parameters of Stress Profiles (应力筛选激发谱的关键参数)1. High Temperature Burn-in  Temperature Delta  Duration2. Temperature Cycling  Maximum/Minimum Temperature  Temperature Change Rate  Dwell Duration  Number of Cycles3. Random Vibration  Grms  Input Acceleration Profile (Power Spectral Density)  Duration  Axes of Vibration 20
  23. 23. ESS/Burn-In Quantification Classification (老炼及环境应力筛选定量分析分类)1. Statistical Modeling: Mathematical Description of Failure Time Behavior2. Physical Modeling: Life-stress Relationship3. Optimum Screening Time Determination under a Given Stress Profile: • Cost Criteria: Cost/Profit • Reliability Criteria: Failure Rate/Mean Life/Mission Reliability • Screening Efficiency Criteria: Efficiency/Residue/Power/Strength4. Optimum Screening Profile (Parameter) Determination: • Screening Strength • Life-Stress Relation (Accelerated Stress Testing) 21
  24. 24. Mathematical Description Of The FailureProcess During Screen (应力筛选过程的数学描述)1. Mixed Weibull Life Distribution √2. Two-Parameter Bathtub Model3. Three-Parameter Bathtub Model4. Five-Parameter Bathtub Model5. Six-Parameter Bathtub Model 22
  25. 25. Model Selection and Parameter Estimation (模型选择以及参数估计) Model Selection: (1) Bimodal Mixed Weibull Life Distribution – with physical meaning and commercial software available for parameter estimation (2) Two-Parameter Bathtub Model – simple and easy to estimate parameters Parameter Estimation: (1) Analytical Method: MLE – mathematically complicated, but more efficient & accurate 23
  26. 26. Mixed Life Distribution – General (混合寿命分布 –通用模型) R1, 2,...,n (T )  p1 R1 (T )  p2 R2 (T )  p3 R3 (T )  ...  pn Rn (T ) f1, 2,...,n (T )  p1 f1 (T )  p2 f 2 (T )  p3 f 3 (T )  ...  pn f n (T ) p1 f1 (T )  p2 f 2 (T )  p3 f 3 (T )  ...  pn f n (T )1, 2,...,n (T )  p1 R1 (T )  p2 R2 (T )  p3 R3 (T )  ...  pn Rn (T ) where n = total number of subpopulations; fi(T), Ri(T), and λi(T) are failure probability density function, reliability function, and failure rate function of ith subpopulation at age T; pi = proportion of ith subpopulation, and n  pi  1 i 1 24
  27. 27. Mixed Life Distribution – Bimodal Weibull (混合寿命分布 – 双态威布尔模型) 1 2  T  1   T  2            R1, 2 (T )  p1 e  1   p2 e  2  1 2  1 1  T  1   2 1  T  2  1  T   1       2  T   2       f1, 2 (T )  p1     e  1   p2     e  2  1  1   2  2   1 2  1 1  T  1   2 1  T  2  1  T   1       2  T   2       p1     e  1   p2     e  2  1  1   2   1, 2 (T )  1 2 2  T  1   T  2            p1 e  1   p2 e  2  where i, βi, i are Weibull location, shape, and scale parameters of ith subpopulation; pi = proportion of ith subpopulation, and p1  p2  1 25
  28. 28. Two-Parameter Bathtub Curve Model (两参数浴盆曲线模型)     1 T /    (T )     T e , T  0,   0,   0       1 T /   1 e T /   f (T )     T e e   1 e T /   R(T )  e 26
  29. 29. Maximum Likelihood Estimation (MLE) MethodFor Mixed Weibull Distribution (混合威布尔寿命分布的极大似然估计) ReliaSoft Weibull++ 7 - www.ReliaSoft.com Probability - Weibull 99.000 Probability-W eibull 90.000 D ata 1 W eibull-Mixed MLE RRM K-M FM F= 74/ S= 40 50.000 Probability Line p=23% U n r e lia b ilit y , F ( t ) 10.000 5.000 1.000 0.500 Results Summary Distribution: Weibull-Mixed Analysis: MLE 0.100 CB Method: FM 0.050 Ranking: K-M Beta 1.497589756 2.244569659 Eta 7656.365785 519.3829595 0.010 Portion 0.7699722126 0.2300277874 0.005 LK Value -703.6170419 Fail Susp 74 40 0.001 1.000 10.000 100.000 1000.000 10000.000 Time, (t)                                                                                 27
  30. 30. Optimum Screen Time Determination Based OnBimodal Mixed Exponential Life Distribution (基于双态混合指数寿命分布的最佳筛选时间确定)1. Bimodal Mixed Exponential Life Distribution – A Special Case of Bimodal Mixed Weibull2. An Ever Decreasing Failure Rate Function3. Screen Duration for a Post-Screen Mission Reliability4. Screen Duration for a Post-Screen Mean Residual Life5. Screen Duration for a Post-Screen Failure Rate Function6. Screen Duration for a Screen Power Function7. The Number and Cost of Failures During Screen 28
  31. 31. Bimodal Mixed Exponential Life Distribution --A Life Distribution With Ever Decreasing Failure Rate(双态混合指数寿命分布 – 一个失效率永远递减的特殊寿命分布)  Reliability Function:  t  t R(t) =p e b  pg e g b  Probability Density Function (pdf):  t  t f(t) =p  e b  pg g e g b b  Failure Rate Function: λg t  (λ b  λ g ) t f(t) pb λ be  λ b t  p g λ ge p b (λ b  λ g )e λ(t)   λb t λg t  λg   (λ b  λ g ) t R(t) pbe  pge p g  pbe  (t ) where pb>pg, pb+pg  1 and Failure Rate Is Always Decreasing!!! t 0  Initial Failure Rate: λ(0) = pb λb + pg λg  Limiting Final Failure Rate: λ() = λg 29
  32. 32. Failure Rate Function of Mixed Exponential LifeDistribution -- An Ever Decreasing Function (混合指数寿命分布失效率函数– 一个永远递减的函数) Failure Rate Function of Mixed Exponential Life Distribution (Lambda_b=5E-3 fr/hr; p_b=10%; Lambda_g=1E-6 fr/hr; p_g=90%) 5.E-04 Failure Rate, fr/hr 4.E-04 3.E-04 2.E-04 1.E-04 0.E+00 0 500 1,000 1,500 2,000 Operating Time, hr 30
  33. 33. Optimum Screen Duration For A Specified Post-Screen Mission Reliability Goal (满足指定的筛选后工作可靠度目标的最佳筛选时间)     t    p R (t)  e  b      b G        1 T* =   Loge    b          t     b g   g   pg  e   R (t)   G      where RG(t) = specified post-burn-in reliability goal for a mission time of t.Constraints:  t  t  t (p e b  p e g ) < R (t) < e g b g G 31
  34. 34. Optimum Screen Duration For A Specified Post-Screen Mission Reliability Goal – An Example(满足指定的筛选后工作可靠度目标的最佳筛选时间 - 举例) Screen Time Versus Post-screen Mission Reliability (Lambda_b=5E-3 fr/hr; p_b=10%; Lambda_g=1E-6 fr/hr; p_g=90%; t=1000 hr) 700 600 Screen Time, Hours 500 400 300 200 100 0 0.8900 0.9100 0.9300 0.9500 0.9700 0.9900 Desired Post-screen Mission Reliability Goal 32
  35. 35. Optimum Screen Duration For A Specified Post-Screen Mean Residual Life Goal (满足指定的筛选后剩余寿命目标的最佳筛选时间)   1   p  MRL    1    b G λb   TS   * Log e     λb  λg    1     pg    λg  MRLG      where MRLG = specified post-screen mean residual life goal.   p pg  Constraints:  b +  < MRL < 1    g   G g  b  33
  36. 36. Optimum Screen Duration For A Specified Post-Screen Mean Residual Life Goal -- An Example(满足指定的筛选后剩余寿命目标的最佳筛选时间 – 举例) Screen Time Versus MRL (Lambda_b=5E-3 fr/hr; p_b=10%; Lambda_g=1E-6 fr/hr; p_g=90%) 600 Screen Time, Hours 500 400 300 200 100 0 900,000 950,000 1,000,000 Desired MRL Goal, hr 34
  37. 37. Optimum Screen Duration For A Specified Post-Screen Failure Rate Goal (满足指定的筛选后失效率目标的最佳筛选时间)  1   p b λ b - λ G ( t )  *  Log e  t TS  λb  λg      pg λ G (t )  λ g    where G (t) = specified post-screen failure rate goal at the end of t-hr mission.  Constraints:      p g   - g        b g <  (t) <  -      G b  -   - g  t         b    pg + p e    b  35
  38. 38. Optimum Screen Duration For A Specified Post-Screen Failure Rate Goal -- An Example (满足指定的筛选后失效率目标的最佳筛选时间 – 举例) Screen Time Versus Post-screen Failure Rate (Lambda_b=5E-3 fr/hr; p_b=10%;Lambda_g=1E-6 fr/hr; p_g=90%; t=100 hr) (Lambda_b=5E-3 fr/hr; p_b=10%; Lambda_g=1E-6 fr/hr; p_b=90%; t=100 hr) 700 600 Screen Time, Hours 500 400 300 200 100 0 1.00E-05 6.00E-05 1.10E-04 1.60E-04 2.10E-04 2.60E-04 3.10E-04 Desired Post-screen Failure Rate Goal 36
  39. 39. Optimum Screen Duration for a Desired ScreenPower Goal (满足指定的筛选功效强度目标的最佳筛选时间) Actual failure rate reduction due to screenScreen Power  Maximum potential failure rate reduction due to screen 1 Log 1   PS   * Ts   G  e p (1 PS )  D  g    G where PSG = the screen power goal, D = λb - λgConstraints: 0 < PSG < 1 37
  40. 40. Optimum Screen Duration for a Desired ScreenPower Goal – An Example (满足指定的筛选功效强度目标的最佳筛选时间 – 举例) Screen Power Screen Time, hr 0% 0.00 5% 10.36 Screen Time Versus Screen Power 10% 21.27 (Lambda_b=5E-3 fr/hr; p_b=1%;Lambda_g=1E-7 fr/hr; p_b=99%) (Lambda_b=5E-3 fr/hr; p_b=10%; Lambda_g=1E-6 fr/hr; p_g=90%) 15% 32.81 600.00 20% 45.03 Screen Time, Hours 25% 58.04 500.00 30% 71.94 35% 86.86 400.00 40% 102.97 45% 120.48 300.00 50% 139.64 55% 160.81 200.00 60% 184.47 65% 211.28 100.00 70% 242.21 75% 278.77 0.00 80% 323.50 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 85% 381.14 90% 462.34 Screen Power 95% 601.07 38
  41. 41. The Number and Cost of Failures During Screen(筛选过程中的失效次数和费用模型) 1  A Ts H(Ts )  h Ts  (h  h )(1  e ) f A i f C(Ts )  H(Ts ) C fwhere A  p  g + pg  b b h  pg  g + p  i b b   b g h  f A C  average cost of a single failure. f 39
  42. 42. Optimum Screen Time for the Minimum Cost (使总费用最小的的最佳筛选时间) C (TS )  N [C0  C STS  C fS H S (TS )  C fW HW (TW | TS )]Where TS = screen time, TW = warranty time, N = total # of units to be screened, C0 = fixed cost of screen for each unit, CS = screen cost per hour per unit, CfS = cost of replacing a failed unit during screen, CfW = cost of replacing a failed in the field during warranty, HS(TS) = expected number of renewals of a unit during screen, HW(TW|TS) = expected number of renewals of a screened unit during warranty. 40
  43. 43. References (参考文献) D. Kececioglu and F. Sun, Environmental Stress Screening (ESS) - Its Quantification, Optimization, and Management, 544 pp., 1st Printing by Prentice Hall, June 1995, 2nd Printing by DEStech Inc., 2003. D. Kececioglu and F. Sun, Burn-in Testing - Its Quantification and Optimization, 704 pp., 1st Printing by Prentice Hall, May 1997, 2nd Printing by DEStech, 2003. W. Kuo, W. Chien, T. Kim, Reliability, Yield, and Stress Burn-In: A Unified Approach for Microelectronics Systems Manufacturing and Software Development , Springer; 1st edition January 31, 1998. F. Jensen and N. E. Peterson, Burn-in, John Wiley & Sons, Inc., 167 pp., 1982. D. Kececioglu and F. Sun, "Mixed-Weibull Parameter Estimation for Burn-in Data Using the Bayesian Approach," Microelectronics and Reliability, Vol. 34, No. 10, pp. 1657-1679, 1994. F. Sun and D. Kececioglu, "Determine the Optimum Burn-in Time for the Maximum MRL Directly from the TTT Plot," Proceedings of 5th International Conference of the Decision Sciences Institute, Athens, Greece, July 4-7, 1999. F. Sun and D. Kececioglu, "A Physical Approach for the Determination of the Optimum Random-Vibration Screening Duration,” Proceedings of 1996 Annual Reliability and Maintainability Symposium, Las Vegas, NV, pp. 177-184, January 22-25, 1996. 41
  44. 44. My Contact Information (联络方式) Feng-Bin (Frank) Sun, Ph.D. Email: franksun9999@gmail.comThanks for Your Time! 42

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