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AN INFORMATION-DRIVEN FEA MODEL GENERATION APPROACH FOR CHIP PACKAGE APPLICATIONS

机译:芯片包装应用的信息驱动FEA模型生成方法

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In the electronic chip package development process. Finite Element Analysis (FEA) modeling is widely used as a virtual prototyping technology to achieve good designs. Due to the complexity and variability in materials, geometric shapes, and connectivity configurations, etc. in a chip package, FEA modeling is a tedious and time-consuming activity. Typically finite element modeling takes hours or even days to complete an analysis for a single chip package design. The Multi-Representation Architecture (MRA) is presented as a framework to facilitate automatic transformations of design models into analysis models through four stepping-stone information representations: (1) analyzable product models (APM), (2) context-based analysis models (CBAM). (3) analysis building blocks (ABBs). and (4) solution method models (SMMs). The ABB models describe theoretical physical systems while SMMs represent the ABB models in solution technique-specific form, such as FEA. In this paper, we present an information-driven FEA modeling approach facilitating the mapping between ABBs and SMMs by first decomposing the geometry into meshable bodies and subsequently generating vendor-specific SMMs. To demonstrate this FEA modeling approach, a chip package thermomechanical analysis example is given. The information-driven FEA modeling approach is shown to be an effective and efficient method for capturing engineering information in chip package products, as well as decreasing FEA modeling time.
机译:在电子芯片封装的开发过程中。有限元分析(FEA)建模被广泛用作实现良好设计的虚拟原型技术。由于芯片封装中材料,几何形状和连接配置等的复杂性和可变性,FEA建模是一项繁琐且耗时的活动。通常,有限元建模需要数小时甚至数天才能完成对单芯片封装设计的分析。多重表示架构(MRA)作为框架提供,通过四个垫脚石信息表示来促进将设计模型自动转换为分析模型:(1)可分析产品模型(APM),(2)基于上下文的分析模型( CBAM)。 (3)分析构建块(ABB)。 (4)解决方法模型(SMM)。 ABB模型描述了理论上的物理系统,而SMM则以解决方案技术特定的形式(例如FEA)代表了ABB模型。在本文中,我们提出了一种信息驱动的FEA建模方法,该方法通过首先将几何分解为可网格化实体并随后生成特定于供应商的SMM来促进ABB和SMM之间的映射。为了演示这种有限元分析建模方法,给出了芯片封装热力学分析的例子。信息驱动的FEA建模方法被证明是一种捕获芯片封装产品中的工程信息以及减少FEA建模时间的有效方法。

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