VeritySEM 5i CD-SEM 3D NAND

The newest in the Applied Materials VeritySEM product family, VeritySEM 5i CD-SEM system features first-of-its-kind, in-line, 3D capabilities for high-volume metrology of logic and memory devices at the 1xnm node and beyond. Leveraging market-leading SEMVision® G6 core technology, the new system addresses the unprecedented challenges in measuring physical dimensions posed by leading-edge geometries. Its state-of-the-art, high-resolution SEM column makes possible measurements as small as 6nminnovative image enhancement algorithms aid measurement of fine pattern details. An in-column tilt-beam enables 3D FinFET metrology, while back-scattered electron (BSE) metrology addresses high aspect ratio 3D NAND structures, and BEOL via-in-trench bottom CD and characterization metrology.

FinFETs challenge traditional metrology in such measurements as gate and fin heights, whose uniformity is critical to device performance and yield. Current in-line CD SEM technology can monitor only top-view dimension variations, not those in height and slope. The VeritySEM 5i system’s in-column beam tilt remedies these issues, enabling gate and fin heights to be calculated and controlled.As technology scales down, the aspect ratios of 3D NAND memory structures are increasing to 60:1 and beyond, making accurate measurement of the bottom CD impossible using conventional metrology. High-resolution BSE imaging enhances the signal received from within these structures, allowing the VeritySEM 5i system to “see” deep into vias and trenches for precision measurement. This capability also improves metrology for via-in-trench bottom CD in BEOL processing where the desired connectivity between underlying and overlaying metal layers must be achieved to minimize via resistance.

The VeritySEM 5i system’s exceptional in-line accuracy and process control eliminate more time-consuming and costly off-line wafer cross-sectioning while helping chipmakers to streamline process development, improve device performance and yield, and shorten ramp times to high-volume production.

The VeritySEM 5i system continues to offer hands free recipe creation and full automation. An offline recipe generator (ORG) features recipe editing capabilities via an external server, enabling multiple users to create recipes from computer-aided design offline without the need for wafers. The recipes are automatically stored in the tool database. By eliminating recipe creation time loss, the ORG enables the user to maximize the tool's utilization in production.

The tool's OPC|CheckMax is a proven solution for automating the optical proximity correction (OPC) mask qualification process. As scaling proceeds below 32nm, OPC-enhanced features are commonly incorporated into mask designs for all layers. Hundreds of CD measurements are required to verify that the features printed on the wafer are indeed what the device designers intended to produce. With a suite of proprietary algorithms, OPC|CheckMax receives input from electronic design automation systems, automatically creates CD-SEM measurement recipes, and then directs the VeritySEM 5i system to measure thousands of sites at high throughput without operator assistance.

我曾遇到过和你一样的问题，现在已经解决了。是因为旧显卡无法兼容新的工作模式，你只要下载一个3DANALYZE中文版，打开软件后，1，选定要执行的程序：WE8.EXE2,在强制TNL效果，模拟TNL效果，模拟其他DX8.1效果三个选项前打勾3,执行所选程序.OK!

还不行的话就重下一个吧.

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SEM简单介绍，以下资料来源

因果关系：SEM一般用于建立因果关系模型，但是本身却并不能阐明模型的因果关系。

一般应用于：测量错误、错漏的数据、中介模型（mediation model）、差异分析。

历史：SEM 包括了 回归分析，路径分析（wright, 1921）,验证性因子分析（confirmatory factor analysis）（Joreskog, 1969）.

SEM也被称为 协方差结构模型（covariance structure modelling），协方差结构分析和因果模型。

因果关系：

究竟哪一个是“真的”？ 在被假设的因果变量中其实有一个完整的因果链。

举一个简单的例子： 吃糖果导致蛀牙。这里涉及2个变量，“吃糖果”和“蛀牙”，前者是因，后者是果。 如果上一个因果关系成立，那将会形成一个因果机制，也许会出现这样的结构：

3. 这时还有可能出现更多的潜在变量：

这里我又举另外一个例子，回归模型

在这里，回归模型并不能很好的描述出因果次序，而且也不能轻易的识别因果次序或者未测量的因子。这也是为什么在国外学术界SEM如此流行的原因。

我们在举另外一个例子“路径分析”

路径分析能让我们用于条件模型（conditional relationships），上图中的模型是一种调解型模型或者中介模型，在这里Z 是作为一个中介调节者同时调节X和Y这两个变量的关系。

在这里我们总结一下：

回归分析简单的说就是：X真的影响Y 吗？

路径分析：为什么/如何 X 会影响Y？ 是通过其他潜在变量Z 来达到的吗？例子：刷牙（X）减少蛀牙（Y）通过减少细菌的方法（Z）。------测量和测试中介变量（例如上图中的Z变量）可以帮助评估因果假设。

在这里要提一下因素模型（factor model）

在这个模型当中，各个变量有可能由于受到未被观察到的变量所影响，变得相互有内在的联系，一般来说那些变量都很复杂、混乱，而且很多变量是不能直接被观察到的。

举个例子：“保龄球俱乐部的会员卡”和“本地报纸阅读”，是被观察到的变量，而“社会资产”则是未被观察到的变量。另一个例子：“房屋立法”和“异族通婚”是被观察到的变量，而“种族偏见”是未被观察到的变量。

相互关系并不完全由被观察到的变量的因果关系所导致，而是由于那些潜在的变量而导致。

这些被观察到变量（y1--y4）也有可能由一个潜在的变量（F）所影响。

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