Norris–Landzberg Acceleration Factors and Goldmann Constants for SAC305 Lead-Free Electronics

2012 ◽  
Vol 134 (3) ◽  
Author(s):  
Pradeep Lall ◽  
Aniket Shirgaokar ◽  
Dinesh Arunachalam
Keyword(s):  
Regression Models ◽  
Flip Chip ◽  
Reliability Prediction ◽  
Harsh Environments ◽  
Mechanical Reliability ◽  
Accelerated Test ◽  
Thermal Reliability ◽  
Area Array ◽  
Bga Packages ◽  
Part Selection

Goldmann constants and Norris–Landzberg acceleration factors for SAC305 lead-free solders have been developed based on principal component regression models (PCR) for reliability prediction and part selection of area-array packaging architectures under thermo-mechanical loads. Models have been developed in conjunction with stepwise regression methods for identification of the main effects. Package architectures studied include ball-grid array (BGA) packages mounted on copper-core and no-core printed circuit assemblies in harsh environments. The models have been developed based on thermomechanical reliability data acquired on copper-core and no-core assemblies in four different thermal cycling conditions. Packages with Sn3Ag0.5Cu solder alloy interconnects have been examined. The models have been developed based on perturbation of accelerated test thermomechanical failure data. Data have been gathered on nine different thermal cycle conditions with SAC305 alloys. The thermal cycle conditions differ in temperature range, dwell times, maximum temperature, and minimum temperature to enable development of constants needed for the life prediction and assessment of acceleration factors. Goldmann constants and the Norris–Landzberg acceleration factors have been benchmarked against previously published values. In addition, model predictions have been validated against validation datasets which have not been used for model development. Convergence of statistical models with experimental data has been demonstrated using a single factor design of experimental study for individual factors including temperature cycle magnitude, relative coefficient of thermal expansion, and diagonal length of the chip. The predicted and measured acceleration factors have also been computed and correlated. Good correlations have been achieved for parameters examined. Previously, the feasibility of using multiple linear regression models for reliability prediction has been demonstrated for flex-substrate BGA packages (Lall et al., 2004, “Thermal Reliability Considerations for Deployment of Area Array Packages in Harsh Environments,” Proceedings of the ITherm 2004, 9th Intersociety Conference on Thermal and Thermo-mechanical Phenomena, Las Vegas, Nevada, Jun. 1–4, pp. 259–267, Lall et al., 2005, “Thermal Reliability Considerations for Deployment of Area Array Packages in Harsh Environments,” IEEE Trans. Compon. Packag. Technol., 28(3), pp. 457–466., flip-chip packages (Lall et al., 2005, “Decision-Support Models for Thermo-Mechanical Reliability of Leadfree Flip-Chip Electronics in Extreme Environments,” Proceedings of the 55th IEEE Electronic Components and Technology Conference, Orlando, FL, Jun. 1–3, pp. 127–136) and ceramic BGA packages (Lall et al., 2007, “Thermo-Mechanical Reliability Based Part Selection Models for Addressing Part Obsolescence in CBGA, CCGA, FLEXBGA, and Flip-Chip Packages,” ASME InterPACK Conference, Vancouver, British Columbia, Canada, Jul. 8–12, Paper No. IPACK2007-33832, pp. 1–18). The presented methodology is valuable in the development of fatigue damage constants for the application specific accelerated test datasets and provides a method to develop institutional learning based on prior accelerated test data.

Models for Component Selection and Thermo-Mechanical Reliability Trade-Offs to Address Component Obsolescence in Military Electronics

2008 ◽  
Author(s):  
Pradeep Lall ◽  
Aniket Shirgaokar ◽  
Jeffrey Suhling
Keyword(s):  
Regression Models ◽  
Operating Conditions ◽  
System Level ◽  
Reliability Prediction ◽  
Harsh Environments ◽  
Minimal Risk ◽  
Mechanical Reliability ◽  
Trade Offs ◽  
Bga Packages ◽  
Part Selection

Product miniaturization trends in microelectronics industry are driving the need for smaller, faster, more reliable, less expensive IC’s. Area array packages have been increasingly targeted for use in harsh environments such as automotive underhood, military and space applications but system-level decision support and part-selection tools and techniques for thermo-mechanical reliability trade-offs while addressing part obsolescence in extreme environments are scarce. The models presented in this paper provide decision guidance for smart selection and substitution to address component obsolescence by perturbing product designs for minimal risk insertion of new packaging technologies. It is conceivable for commercial off the shelf parts to become unavailable during the production-life of a product. Typical Commercial-of-the-Shelf parts are manufactured for a period of two to four years, and IC manufacturing processes are available for five to six years. It is envisioned that the reliability assessment models will enable turn-key evaluation of geometric architecture, material properties, and operating conditions effects on thermo-mechanical reliability. The presented approach enables the evaluation of qualitative parameter interaction effects, which are often ignored in closed-form modeling, have been incorporated in this work. Previously, the feasibility of using multiple linear regression models for reliability prediction has been demonstrated for flex-substrate BGA packages [1, 2], flip-chip packages [3, 4] and ceramic BGA packages [5]. In this paper, principal component regression models (PCR) have been investigated for reliability prediction and part selection of area package architectures under thermo-mechanical loads in conjunction with stepwise regression methods. Package architectures studied include, BGA packages mounted on CU-CORE and NO-CORE printed circuit assemblies in harsh environments. The models have been developed based on thermo-mechanical reliability data acquired on copper-core and no-core assemblies in four different thermal cycling conditions. Solder alloys examined include SnPb and SAC Alloys.

PCR for Derivation of Parameter Dependencies, Thermo-Mechanical Norris-Landzberg Acceleration Factors, Goldmann Fatigue Constants for Leadfree Electronics

2009 ◽  
Author(s):  
Pradeep Lall ◽  
Aniket Shirgaokar ◽  
Dineshkumar Arunachalam ◽  
Jeff Suhling ◽  
Mark Strickland ◽  
...  
Keyword(s):  
Test Data ◽  
Thermal Cycle ◽  
Regression Models ◽  
Reliability Prediction ◽  
Maximum Temperature ◽  
Temperature Cycle ◽  
Data Sets ◽  
Accelerated Test ◽  
Bga Packages ◽  
Part Selection

Goldmann Constants and Norris-Landzberg acceleration factors for lead-free solders have been developed based on principal component regression models (PCR) for reliability prediction and part selection of area-array packaging architectures under thermo-mechanical loads. Models have been developed in conjunction with Stepwise Regression Methods for identification of the main effects. Package architectures studied include, BGA packages mounted on copper-core and no-core printed circuit assemblies in harsh environments. The models have been developed based on thermo-mechanical reliability data acquired on copper-core and no-core assemblies in four different thermal cycling conditions. Packages with Sn3Ag0.5Cu solder alloy interconnects have been examined. The models have been developed based on perturbation of accelerated test thermo-mechanical failure data. Data has been gathered on nine different thermal cycle conditions with SAC305 alloys. The thermal cycle conditions differ in temperature range, dwell times, maximum temperature and minimum temperature to enable development of constants needed for the life prediction and assessment of acceleration factors. Goldmann Constants and the Norris-Landzberg acceleration factors have been benchmarked against previously published values. In addition, model predictions have been validated against validation data-sets which have not been used for model development. Convergence of statistical models with experimental data has been demonstrated using a single factor design of experiment study for individual factors including temperature cycle magnitude, relative coefficient of thermal expansion, and diagonal length of the chip. The predicted and measured acceleration factors have also been computed and correlated. Good correlations have been achieved for parameters examined. Previously, the feasibility of using multiple linear regression models for reliability prediction has been demonstrated for flex-substrate BGA packages [Lall 2004, 2005], flip-chip packages [Lall 2005] and ceramic BGA packages [Lall 2007]. The presented methodology is valuable in the development of fatigue damage constants for the application specific accelerated test data-sets and provides a method to develop institutional learning based on prior accelerated test data.

Thermo-mechanical reliability tradeoffs for deployment of area array packages in harsh environments

2005 ◽  
Vol 28 (3) ◽  
pp. 457-466 ◽  
Author(s):  
P. Lall ◽  
N. Singh ◽  
J.C. Suhling ◽  
M. Strickland ◽  
J. Blanche
Keyword(s):  
Harsh Environments ◽  
Mechanical Reliability ◽  
Area Array

Risk Management Models for Flip-Chip Electronics in Extreme Environments

2006 ◽  
Author(s):  
Pradeep Lall ◽  
Ganesh Hariharan ◽  
Guoyun Tian ◽  
Jeff Suhling ◽  
Mark Strickland ◽  
...  
Keyword(s):  
Risk Management ◽  
Decision Support ◽  
Closed Form ◽  
Principal Components ◽  
Statistical Models ◽  
Flip Chip ◽  
Harsh Environments ◽  
Accelerated Test ◽  
Modeling Tools ◽  
Failure Mechanics

In this work, risk-management and decision-support models for reliability prediction of flip chip packages in harsh environments have been presented. The models presented in this paper provide decision guidance for smart selection of component packaging technologies and perturbing product designs for minimal risk insertion of new packaging technologies. In addition, qualitative parameter interaction effects, which are often ignored in closed-form modeling, have been incorporated in this work. Previous studies have focused on development of modeling tools at sub-scale or component level. The tools are often available only in an offline manner for decision support and risk assessment of advanced technology programs. There is need for a turn key approach, for making trade-offs between geometry and materials and quantitatively evaluating the impact on reliability. Multivariate linear regression and robust principal components regression methods were used for developing these models. The first approach uses the potentially important variables from stepwise regression, and the second approach uses the principal components obtained from the eigen-values and eigen-vectors, for model building. Principal-component models have been included because if their added ability in addressing multi-collinearity. The statistics models are based on accelerated test data in harsh environments, while failure mechanics models are based on damage mechanics and material constitutive behavior. Statistical models developed in the present work are based on failure data collected from the published literature and extensive accelerated test reliability database in harsh environments, collected by center of advanced vehicle electronics. Sensitivity relations for geometry, materials, and architectures based on statistical models, failure mechanics based closed form models and FEA models have been developed. Convergence of statistical, failure mechanics, and FEA based model sensitivities with experimental data has been demonstrated.

Electrical-Thermal-Reliability Co-Design for TSV-Based 3D-ICs

2015 ◽  
Author(s):  
Tiantao Lu ◽  
Ankur Srivastava
Keyword(s):  
Mechanical Stress ◽  
Electrical Performance ◽  
Mechanical Reliability ◽  
Thermal Reliability ◽  
Conduction Path ◽  
Placement Problem ◽  
Thermal Profile ◽  
3D Ic ◽  
3D Ics ◽  
Simulation Based

This paper presents an electrical-thermal-reliability co-design technique for TSV-based 3D-ICs. Although TSV-based 3D-IC shows significant electrical performance improvement compared to traditional 2D circuit, researchers have reported strong electromigration (EM) in TSVs, which is induced by the thermal mechanical stress and the local temperature hotspot. We argue that rather than addressing 3D-IC’s EM issue after the IC designing phase, the designer should be aware of the circuit’s thermal and EM properties during the IC designing phase. For example, one should be aware that the TSVs establish vertical heat conduction path thus changing the chip’s thermal profile and also produce significant thermal mechanical stress to the nearby TSVs, which deteriorates other TSV’s EM reliability. Therefore, the number and location of TSVs play a crucial role in deciding 3D-IC’s electrical performance, changing its thermal profile, and affecting its EM-reliability. We investigate the TSV placement problem, in order to improve 3D-IC’s electrical performance and enhance its thermal-mechanical reliability. We derive and validate simple but accurate thermal and EM models for 3D-IC, which replace the current employed time-consuming finite-element-method (FEM) based simulation. Based on these models, we propose a systematic optimization flow to solve this TSV placement problem. Results show that compared to conventional performance-centered technique, our design methodology achieves 3.24x longer EM-lifetime, with only 1% performance degradation.

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