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On The Correlation Between Multiple Hot Blocks And Package Thermal Resistance

机译:关于多热块与封装热阻之间的相关性

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A flip-chip package with square die is considered in this study. Upto four square non-intersecting hot blocks are imposed on the die's otherwise uniform power distribution. Block locations on the die outline are randomly chosen with uniform probability. The power density of a given block is a random parameter, and is permitted to be as high as 10× the baseline uniform bulk power density. Additionally, the size of any block is also treated as a random parameter and is permitted to be as high as 10 % of the die area. A 6000-tuple Monte Carlo study of the packages is conducted, and the package thermal resistance (R{sub}(jc)) noted in each case. A variety of models are fit to the R{sub}(jc) using the block characteristics as key variables, and their quality is characterized using the statistical correlation coefficient as a model metric. The results suggest a 96 % correlation between R{sub}(jc) and the largest product of local power ratio and square of effective local power density ratio among the blocks- providing a simple and useful method to immediately identify blocks with the most impact on R{sub}(jc) in a die floorplan.
机译:在本研究中考虑了具有方形模具的倒装芯片封装。高达四个方形非交叉热块施加在模具上的均匀配电。模具轮廓上的块位置随着均匀概率而随机选择。给定块的功率密度是随机参数,并且被允许高达10倍的基线均匀散装功率密度。另外,任何块的大小也被视为随机参数,并且允许高达芯片区域的10%。进行了6000元组蒙特卡罗研究,并进行了每种情况下注明的封装热阻(R {Sub}(JC))。使用块特性作为键变量的块特性,各种模型适合R {Sub}(JC),并且它们的质量使用统计相关系数作为模型度量。结果表明,R {Sub}(JC)与局部局部局部功率比和有效局部功率密度比的最大产品之间的96%的相关性 - 提供了一种简单而有用的方法,可以立即识别最大的块芯片平面图中的R {sub}(jc)。

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