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Reduced order modelling for reliability optimisation of advanced micro-systems

Reduced order modelling for reliability optimisation of advanced micro-systems

Stoyanov, Stoyan, Rajaguru, Pushpa and Bailey, Chris (2010) Reduced order modelling for reliability optimisation of advanced micro-systems. In: EngOpt2010 2nd International Conference on Engineering Optimization. International Conference on Engineering Optimization (2). EngOpt2010, pp. 1-10.

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This paper discusses the Design for Reliability of advanced electronics Micro-systems based on computational approach that integrates methods for high fidelity analysis, reduced order modelling, numerical risk analysis and optimisation. The methodology is demonstrated for the design of a System-in-Package (SiP) structure. System-in-Package is a technology that is developed on the basis of miniaturised integrated multi-functional electronics modules using 3D stacking of several silicon chips (Integrated Circuits, ICs). System-in-Package aims to provide fully functional electronic systems and sub-systems that integrate several functionally different devices, e.g. optical, MEMS, sensors and other components, into a single package. There is little understanding and knowledge how do the large die sizes in the SiP modules, the lead-free assembly, interfacial de-laminations and the utilisation of new materials affect the reliability of these electronics systems. In particular, the board level reliability of the package related to the thermal fatigue material degradation of solder interconnects is of a great concern. Understanding the performance, reliability and robustness of SiP modules is a key factor for the future development and success of the technology.
The main focus in this study is on the techniques for reduced order modelling and the development of the associated models for fast design evaluation and analysis. The discussion is on methods for approximate response surface modelling based on interpolation techniques using Kriging and radial basis functions. The reduced order modelling approach uses prediction data for the thermo-mechanical behaviour of the SiP design obtained through non-linear transient finite element simulations, in particular for the fatigue life-time of the lead-free solder interconnects and the warpage of the package.
The reduced order models are used for the analysis of the effect of design uncertainties on the reliability of these advanced electronics modules. To aid this assessment, different methods for estimating the variation of reliability related metrics of the electronic package are researched and tested. Sample based methods such as full scale Monte Carlo and Latin Hypercube, and analytical approximate methods such as First Order Second Moment (FOSM) and Point Estimation Method (PEM) are investigated and their accuracy is compared.
The optimisation modelling addresses the probabilistic nature of the reliably problem of the SiP structures under investigation. Optimisation tasks with design uncertainty are formulated and solved using modified Particle Swarm Optimisation algorithms. The probabilistic optimisation deals with two different performance metrics of the design, the thermo-mechanical fatigue reliability of the board level interconnects and the thermally induced warpage of the package. The objective in this analysis is to ensure that the design has the required reliability and meets a number of additional requirements.

Item Type: Conference Proceedings
Title of Proceedings: EngOpt2010 2nd International Conference on Engineering Optimization
Additional Information: [1] EngOpt2010 2nd International Conference on Engineering Optimization, Lisbon - Potugal, 6-9 September 2010 [2] Proceedings published on line by IST – Instituto Superior Técnico, Technical University of Lisbon and IDMEC/IST - Institute of Mechanical Engineering
Uncontrolled Keywords: Reduced order models, system-in-package, risk analysis, probabilistic optimisation, microsystems
Subjects: Q Science > QA Mathematics
T Technology > T Technology (General)
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Department of Mathematical Sciences
Related URLs:
Last Modified: 14 Oct 2016 09:11
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None

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