Multidisciplinary design and optimization methodologies in electronics packaging: State-of-the-art review

被引:11
|
作者
Hadim, Hamid [1 ]
Suwa, Tohru [1 ]
机构
[1] Stevens Inst Technol, Dept Mech Engn, Hoboken, NJ 07030 USA
关键词
multidisciplinary design and optimization; electronics packaging design; CAD in electronics packaging; thermal and thermo-mechanical design; electronics component placement; design of experiments; artificial neural network;
D O I
10.1115/1.2957459
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Electronics packaging design is a process that requires optimized solutions based on multidisciplinary design trade-offs, which usually have complex relationships among multiple design variables. Required numerical analyses combining electrical, thermal, and thermomechanical, among others, have made the multidisciplinary design and optimization process more challenging because of their time-intensive modeling and computation. In this paper, a state-of-the-art review of recent multidisciplinary design and optimization methodologies in electronics packaging is presented. The reported methodologies are divided into three groups: (1) integrated multidisciplinary computer aided design (CAD) environment, (2) semi-automated design optimization techniques, and (3) automated component placement techniques. In the first group, multidisciplinary design and optimization are carried out using interactive CAD environment software. The electronics packaging designer inputs data and makes decisions, while the CAD software provides a comprehensive multidisciplinary modeling and simulation environment. In the second group, using semi-automated design optimization methodologies, various objectives are optimized simultaneously mainly based on package configurations (dimensions), material properties, and operating conditions. In the third group, optimal placement of heat generating components is performed automatically based on multiple requirements. In recent years, methodologies using (1) detailed numerical analysis models directly connected to optimization algorithms, (2) design of experiments (DoE), and (3) artificial neural networks (ANNs) have been proposed as new trends in this field. These methodologies have led to significant improvement in design optimization capabilities, while they require intensive computational effort. Advantages as well as disadvantages of these methods are discussed.
引用
收藏
页码:0340011 / 03400110
页数:10
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