首页> 外文期刊>Applied Surface Science >Evaluation strategies for multi-layer, multi-material ellipsometric measurements
【24h】

Evaluation strategies for multi-layer, multi-material ellipsometric measurements

机译:多层,多材料椭偏测量的评估策略

获取原文
获取原文并翻译 | 示例
       

摘要

In order to extract the physical properties from an ellipsometric measurement, an optical model of the sample has to be assumed first, because the theory of ellipsometry consists on one-directional computation only (there is no reverse function). Then, the ellipsometric evaluation is an iterative optimising procedure with high time consumption feature and the reliability depends strongly on the a-priori information. The faster the computers are today, the more exactly the physical properties of either the sample or the process can be evaluated. However, the increasing number of the parameters and so, the dimensions of the search space leads to a combinatorial explosion. In the case of larger search space is needed (either less a-priori information is available or more parameters are used), the error surface of the parameter space can be quite "hilly" and may contain even numerous local minima. In the lack of precise a-priori information the Levenberg-Marquardt (LM) gradient search is generally started out of the decreasing area of the global minimum and therefore, it is inappropriate to find the solution. Therefore, there is a hard need of more complex evaluating strategies, which combines the algorithms to make the evaluation more reliable. Different point selection strategies, an extended criteria function and combined algorithms were applied on porous silicon multi-layer and polycrystalline measurements to demonstrate a higher convergence speed (effectiveness) and more reliability. (c) 2006 Elsevier B.V All rights reserved.
机译:为了从椭偏测量中提取物理特性,必须首先假定样品的光学模型,因为椭偏的理论仅基于单向计算(没有反向函数)。然后,椭偏评估是一种具有高时间消耗特征的迭代优化过程,其可靠性在很大程度上取决于先验信息。今天的计算机速度越快,就可以更准确地评估样品或过程的物理性质。但是,参数数量的增加,以及搜索空间的尺寸增加,导致组合爆炸。在需要更大的搜索空间的情况下(可以使用较少的先验信息或使用更多的参数),参数空间的误差面可能会非常“陡峭”,甚至可能包含许多局部最小值。在缺乏精确的先验信息的情况下,Levenberg-Marquardt(LM)梯度搜索通常是从全局最小值的递减区域开始的,因此,寻找解决方案是不合适的。因此,迫切需要更复杂的评估策略,将这些算法结合起来以使评估更加可靠。在多孔硅多层和多晶测量中采用了不同的点选择策略,扩展的标准函数和组合算法,以证明更高的收敛速度(有效性)和更高的可靠性。 (c)2006 Elsevier B.V保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号