Response Surface Modeling and Optimisation for Reliable Electronic Products

The aim of integrating computational mechanics (FEA and CFD) and optimization tools is to speed up dramatically the design process in different application areas concerning reliability in electronic packaging. Design engineers in the electronics manufacturing sector may use these tools to predict key design parameters and configurations (i.e. material properties, product dimensions, design at PCB level. etc) that will guarantee the required product performance. In this paper a modeling strategy coupling computational mechanics techniques with numerical optimization is presented and demonstrated with two problems. The integrated modeling framework is obtained by coupling the multi-physics analysis tool PHYSICA - with the numerical optimization package - Visua/DOC into a fuJly automated design tool for applications in electronic packaging. Thermo-mechanical simulations of solder creep deformations are presented to predict flip-chip reliability and life-time under thermal cycling. Also a thermal management design based on multi-physics analysis with coupled thermal-flow-stress modeling is discussed. The Response Surface Modeling Approach in conjunction with Design of Experiments statistical tools is demonstrated and used subsequently by the numerical optimization techniques as a part of this modeling framework. Predictions for reliable electronic assemblies are achieved in an efficient and systematic manner.

Predicting the relationship between reliability and geometric parameters of cu column bumped flip-chips

Cu column bumping is a novel flip chip packaging technique that allows Cu columns to be bonded directly with the dies. It has eliminated the under-bump-metallurgy (UBM) fonnation step of the traditional flip chip manufacturing process. This bumping technique has the potential benefits of simplifying the flip chip manufacturing process, increasing productivity and the UO counts. In this paper, a study of reliability of Cu column bumped flip chips will be presented. Computer modelling methods have been used to predict the shape of solder joints and the response of flip chips to cyclic thermal-mechanical loading. The accumulated plastic strain energy at the corner solder joints has been used as an indicator of the solder joint reliability. Models with a wide range of design parameters have been compared for their reliability. The design parameters that have been investigated are the copper column height and radius, PCB pad radius, solder volume and Cu column wetting height. The relative importance ranking of these parameters has been obtained. The Lead-free solder material 96.5Sn3.5Ag has been used in this modelling work.

Areva and EDF: business prospects and risks in nuclear energy

This report examines the financial position of EDF and Areva and analyses what the impact of their nuclear ambitions will be on this.

Artificial neural networks in modelling osmotic dehydration of foods

Artificial neural network (ANN) models for water loss (WL) and solid gain (SG) were evaluated as potential alternative to multiple linear regression (MLR) for osmotic dehydration of apple, banana and potato. The radial basis function (RBF) network with a Gaussian function was used in this study. The RBF employed the orthogonal least square learning method. When predictions of experimental data from MLR and ANN were compared, an agreement was found for ANN models than MLR models for SG than WL. The regression coefficient for determination (R2) for SG in MLR models was 0.31, and for ANN was 0.91. The R2 in MLR for WL was 0.89, whereas ANN was 0.84.Osmotic dehydration experiments found that the amount of WL and SG occurred in the following descending order: Golden Delicious apple > Cox apple > potato > banana. The effect of temperature and concentration of osmotic solution on WL and SG of the plant materials followed a descending order as: 55 > 40 > 32.2C and 70 > 60 > 50 > 40%, respectively.