通过全自动光刻模拟改进光刻线缺陷处理

R. Mann, E. Goodman, K. Lao, Steven Ha, A. Vacca, P. Fiekowsky, Dan Fiekowsky
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引用次数: 1

摘要

大多数先进的晶圆厂都采用了复杂的图案装饰,这给厂内网线的鉴定带来了许多挑战。这些光学接近校正(OPC)技术创建的辅助特征往往在尺寸和形状上与图1所示的主要图案非常接近。辅助功能上的小缺陷很可能对晶圆图像的保真度几乎没有影响,而主要功能上的相同缺陷可能会显著降低设备的功能。为了正确地处理这些缺陷,光线检测技术人员需要一种有效的方法来自动分离主要特征和辅助特征,并预测由此产生的缺陷对晶圆图像的影响。分析系统(ADAS)缺陷仿真系统[1]。到目前为止,由于需要手动输入以创建准确结果的设置的复杂性,使用ADAS仿真仅限于工程师。输入这些值中的一个错误可能导致错误的结果,因此需要完全自动化。在本研究中,我们提出了一种新的方法,将所有需要的仿真参数自动加载到ADAS中。这分两部分完成。首先,我们创建了一个扫描器参数数据库,该数据库可以从掩膜产品和级别名称中自动识别。其次,我们通过使用包含已知的十字线临界尺寸(CD)测量值的新参考图像(由检测工具提供)自动确定适当的模拟可打印性阈值。这种新方法自动加载正确的扫描条件,设置适当的模拟阈值,并自动测量由缺陷引起的CD变化的百分比。这简化了资格认证,减少了搁置等待工程师审查的线路数量。我们还提供了数据,显示了新方法的一致性和可靠性,以及对厂内网线鉴定效率的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving reticle defect disposition via fully automated lithography simulation
Most advanced wafer fabs have embraced complex pattern decoration, which creates numerous challenges during in-fab reticle qualification. These optical proximity correction (OPC) techniques create assist features that tend to be very close in size and shape to the main patterns as seen in Figure 1. A small defect on an assist feature will most likely have little or no impact on the fidelity of the wafer image, whereas the same defect on a main feature could significantly decrease device functionality. In order to properly disposition these defects, reticle inspection technicians need an efficient method that automatically separates main from assist features and predicts the resulting defect impact on the wafer image. Analysis System (ADAS) defect simulation system[1]. Up until now, using ADAS simulation was limited to engineers due to the complexity of the settings that need to be manually entered in order to create an accurate result. A single error in entering one of these values can cause erroneous results, therefore full automation is necessary. In this study, we propose a new method where all needed simulation parameters are automatically loaded into ADAS. This is accomplished in two parts. First we have created a scanner parameter database that is automatically identified from mask product and level names. Second, we automatically determine the appropriate simulation printability threshold by using a new reference image (provided by the inspection tool) that contains a known measured value of the reticle critical dimension (CD). This new method automatically loads the correct scanner conditions, sets the appropriate simulation threshold, and automatically measures the percentage of CD change caused by the defect. This streamlines qualification and reduces the number of reticles being put on hold, waiting for engineer review. We also present data showing the consistency and reliability of the new method, along with the impact on the efficiency of in-fab reticle qualification.
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