Modelling Reliability of Power Electronics Modules Current Status, Future Challenges Chris Bailey University of Greenwich, UK (c) University of Greenwich
University of Greenwich Royal Observatory
Computational Mechanics and Reliability Group (CMRG) Established in 2004 Staff: 2 Profs, 2 Readers, 5 Post Doc s, 7 PhD s Research Mission To develop and apply CAE technologies to predict the physical behaviour, performance, reliability, and maintainability of complex engineering components/systems. 3
Analysis Tools at Greenwich Thermal, Fluids, Chemical FLOTHERM, Phoenics, Fluent PHYSICA (In-House Code) Airflow Temperature Mechanical ANSYS, PHYSICA Electrical/Electromagnetic Tracepro, Spice, PHYSICA Multi-Physics PHYSICA, Comsol, Ansys Other MD (In-house software) Evolver Multi-Physics Analysis Die #3 Die #2 Die #1 Multi-Scale Analysis Stress Optical Analysis Surface Tension & Microstructure Tools
Analysis Tools at Greenwich Optimisation & Risk Analysis Visual Doc, ROMARA Reliability & Prognostics Realsoft, ROMARA, Powerlife 96 AMD SM/DM Cluster Optimisation & Process Capability PXI Hardware in Loop Embedded Systems Parallel Computing Reliability/Prognostics
Modelling Technology - PHYSICA ROMARA Integrated Solution Procedures Coupled algorithms for Physics + Optimisation. Takes advantage of High Performance Comp. systems Optimisation Design of Experiments Sensitivity Analysis Risk Analysis Design for 6-Sigma
Virtual Prototyping Modelling Reliability Future Challenges Content (c) University of Greenwich
Virtual Prototyping Electro-thermal ANSOFF (electromagnetic) PEEC, Maxwell Q3D (Parasitic Extraction) Flotherm (Thermal, CFD) PSPICE, SABER Circuit level models Thermo-Mechanical ANSYS, ABAQUS (Finite Element Codes) Multi-Physics COMSOL, ANSYS Optimisation Optimus, Visual-Doc
Benefits of Virtual Prototyping Main Design Process $1,000,000 Incentive > for virtual prototyping 91.4 tools: MONEY!!! $100,000 The cost of repairing mistakes increases roughly an order of magnitude at each critical phase. $1,000 $10,000 Design Prototype Production Field
Simulation driven DfX Modify Design Formulate Design Intention Computer Aided Drafting and Design Build Prototypes Testing of Prototypes Manufacturin g In-Field Operation and Maintenance Traditional DESIGN BUILD TEST Product Development Cycle Modify Design Formulate Design Intention DESIGN (CAD) MODEL and SIMULATE Fabrication Packaging Assembly Test Reliability/Performance Physical Validation Manufacturing In-Field Operation and Maintenance Numerical Design Optimisation Data Flow Simulation Driven Product Development Cycle 10
Virtual Prototyping Modelling Reliability Modelling Quality Future Challenges Content (c) University of Greenwich
Typical Failures in Power Modules Wirebond Failure Ceramic Substrate Failure Solder Failure
Causes of failure in Electronic Equipment Major Causes of Electronics Failures 20% Vibration 6% Dust 55% Temperature 19% Humidity (Source : US Air Force Avionics Integrity Program)
Why is Temperature a problem? Leads to uneven expansion of different materials Stress; Creep Fracture; Fatigue Expands a lot Doesn t expands a lot Increase in Temperature Stress and Fatigue
Material Properties ALG vs Different materials Thermal performance From Hoffmann Elektrokohle
Physics of Failure based Reliability 16
Data Requirements CFD, FEA, Optimisation Analysis, etc Boundary + Loading conditions Material Behaviour Temperature, Stress Damage Model Failure Criteria Failure definition Validation What is dominant failure mechanism? How long will the product last? 17
Damage Modelling DIE Temp Stress - Damage SUBSTRATE Cohesive Zone / Disturbed State Creep strain energy density
Damage Models Damage Models Wirebond; Solder (SnAg) Creep, Fatigue, Fracture D 1 e 0.05 p N f 178L 1. 023 p No dimple With Dimples N f 1.18 3.492 p
Wirebond (Design for Reliability) Minimise Damage Stress, Plastic Strains Design Changes Impact of wire diameter
Ceramic Substrate - Delamination No dimple With Dimples Maximum absolute stress value decreased by about 20%
coefficient values Solder Joint DOE & Sensitivity Analysis 0.01 0.008 p c0 c1x 1 c2x2 c3x3 c12 x1x 2 0.006 c x x c x x c x c x c x 2 2 2 13 1 3 23 2 3 11 1 22 2 33 3 0.004 Copper Aluminium Copper Solder x 3 x 2 x 1 Copper Baseplate 0.002 0-0.002-0.004-0.006 DOE : Importance of Solder Thickness Alumina Cu Sn3.5Ag cr n Q A sinh ( e)exp( ) RT E(GPa) 270 115 54.05-0.193T 0.22 0.31 0.4 CTE(ppm/ C) 7.4 17.3 21.85+0.02039T A (s -1 ) n α (1/MPa) Q/R SnAg 9.00E+05 5.5 0.06527 8690 Material Properties
PoF to identify cycles to failure Component Lifetime (number of cycles) busbar 946 wirebond 10489 Chip solder 14889 Substrate solder 30000 23
Can we predict mission life Traction Application: Mass Transit Status T min T max Cycles/day Shed stops -40 C +80 C 1 Station Stops +80 C +100 C 1080 Cruise +80 C +81 C 6E6 Traction Application: High Speed. Status T min T max Cycles/day Shed stops -40 C +80 C 1 Station Stops +80 C +100 C 20 Speed Control +80 C +85 C 3240 Cruise +80 C +81 C 6E6 Failure definition: crack length (L) = 2.8mm, this is equivalent to about 20% area crack. Mass Transit Load profiles Shed stop: over night storage High Speed Station stops: stop at station during service Speed control: automated speed control system Cruise: Power ripples when train is running. 24
Lifetime (years) Traction Control PEM Lifetime prediction solder thickness high speed (years) mass transit (years) 0.1 mm 11.61 0.55 0.2 mm 13.76 0.65 0.3 mm 14.93 0.70 High speed Application > Mass Transit because of the number of station stops. Solder thickness has big impact. 16 15 14 13 12 11 10 y = 3.0306Ln(x) + 18.601 0.1 0.15 0.2 0.25 0.3 solder thickness (mm) 25
Risk and Uncertainty Analysis Reliability is not Deterministic!! Uncertainty: Failure Models & Materials Data Product/Package Quality Probabilistic Design Monte Carlo Moment Methods Need Reduced Order Methods 26
Risk Analysis Example Objective: X3 X2 Die Solder Substrate X1 Cu Cu Minimise Solder Damage under Thermal Cycling Subject to Design Constraints: Probability (Die Stress > 86 MPa) 0.0027 (3-sigma) Probability (X2 + X3 0.225 mm) 0.0027 (3-sigma) Probability (X2 + X3 0.370 mm) 0.0027 (3-sigma) by varying normally distributed design variables within their design limits 4.7 mm mean Die Width (X1) 8.7 mm X1 standard deviation σ = 1.35 mm 0.14 mm mean Substrate Thickness (X2) 0.26 mm X2 standard deviation σ = 0.018 mm 0.07 mm mean Solder Thickness (X3) 0.13 mm X3 standard deviation σ = 0.012 mm 27
Risk Analysis - Example Deterministic optimal solution Die stress at critical limit (86 MPa) Solder damage (objective) 557.8 KJ Probabilistic optimal solution (include uncertainties) Die stress (76.7 MPa ) shifted away from the 86 MPa limit to satisfy 3-sigma reliability requirement Above reliability gained by compromising on the solder damage (615.4 KJ) X3 X2 Die Solder Substrate X1 Cu Cu Solder damage - Visco-plastic energy (kj) Die stress (MPa) Variable X1 (mm) Variable X2 (mm) Variable X3 (mm) Initial Design 585 80.3 6.75 0.2 0.1 Deterministic optimum 557.8 86.0 6.68 0.147 0.13 Probabilistic optimum (mean values) 615.4 76.7 6.69 0.16 0.13 Less improvement of objective 3-sigma reliability with probabilistic optimum Optimal design variable values 28
Accuracy of Reliability Modelling Ref: Syed, SEM 29
Virtual Prototyping Modelling Reliability Future Challenges Content (c) University of Greenwich
Trends In Power Electronics Packaging 1. GreenElectronics technolog y towards 2050 highly electrified society (NEDO Report ID -No Q05018) http://www.nedo.go.jp/database/index.html
Source; ECN, Nov, 2011 (c)company Name Permitted
Design Tools and Modelling Devices Components Convertors Drives Thermal Compact Models Physics of failure Electromigration Dielectric Breakdown Tools Silvaco, Matlab, ANSYS. Thermal & Electrical Compact Thermal and Loss Models Physics of failure Interconnect reliability, optimisation and robustness Tools ANSYS, Flotherm, Powerlife, Visual-Doc.. Passives (Magnets..) EMI, EMC, Thermal Loss Models Trade-off analysis Optimisation Tools Vectorfields. PE & Drive Interactions Thermal, EMC, reliability Multi-physics models for High Speed drives Tools SABER, Flux 3D DTM Vision: Development of new design methodologies, models and tools that will provide the ability to undertake co-design (device to system) and hence optimise a power electronics system in terms of its efficiency, power density, reliability, robustness, EMI, integration and cost.
Design Devices to Systems (c)company Name Permitted
Reliability / Prognostics Design for Reliability Impact of new materials New failure mech/modes Metrology Modelling Accurate failure models Combination testing (temp + vib) Prognostics Data & Model Driven What can we measure Accuracy Standards Future will embed prognostics for monitoring failures in both test and field environments
Challenges UK Power Electronics Manufacturing 6.5% of Global Power Electronics Product >95% being exported, Strong design teams Engineering Grads 33% take non-eng jobs 12% fewer 19 year olds 2008-2018 Reliability Multi-disciplinary Poor coverage at Univ. Need for better training
Contact Professor Chris Bailey University of Greenwich e-mail: c.bailey@gre.ac.uk 37