基于原生缺陷双向反射分布函数的450mm表面扫描检测系统缺陷自动分类配方创建

N. Yathapu, Steve McGarvey, Justin Brown, Alexander Zhivotovsky
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引用次数: 1

摘要

本研究探讨了表面扫描检测系统(SSIS)自动缺陷分类(ADC)的可行性。缺陷分类基于双向反射分布函数(BRDF)建模生成的散射灵敏度分级曲线。BRDF允许根据薄膜晶圆样品的光学特性和SSIS的光学结构创建SSIS的灵敏度/尺寸曲线。在SSIS配方创建和ADC之前,在薄膜硅片和裸硅片上消除聚苯乙烯乳胶球(PSL)和二氧化硅沉积,对基于光散射表面强度的缺陷分类提出了挑战。本研究结合BRDF建模配方创建的最大灵敏度,探讨了基于本地缺陷配方创建的理论最大SSIS灵敏度。采用基于BRDF模型的配方对单层和叠层晶圆片进行了检测。在SSIS配方创建之后,最初的目标是最大灵敏度,选定的配方被优化,以分类非图像化晶圆上常见的缺陷。结果用于确定原生缺陷的ADC分形精度和评价SSIS配方创建方法。从每个SSIS配方和薄膜衬底的最终检验结果中统计有效的缺陷样本在SSIS ADC处理后在缺陷审查扫描电子显微镜(SEM)上进行了审查。从每个统计上有效的缺陷分类/大小中收集原生缺陷图像用于SEM评审。从缺陷评审SEM中收集的数据用于确定每个SSIS缺陷分类仓的统计纯度和准确性。本文从商业和技术两方面探讨了消除PSL和二氧化硅沉积作为针对ADC的SSIS配方创建的前体的考虑。将SSIS ADC与通过BRDF建模创建的配方成功集成,有可能显著减少缺陷审查SEM的工作量需求,并为450mm SSIS配方创建节省大量资本支出。
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Recipe creation for automated defect classification with a 450mm surface scanning inspection system based on the bidirectional reflectance distribution function of native defects
This study explores the feasibility of Automated Defect Classification (ADC) with a Surface Scanning Inspection System (SSIS). The defect classification was based upon scattering sensitivity sizing curves created via modeling of the Bidirectional Reflectance Distribution Function (BRDF). The BRDF allowed for the creation of SSIS sensitivity/sizing curves based upon the optical properties of both the filmed wafer samples and the optical architecture of the SSIS. The elimination of Polystyrene Latex Sphere (PSL) and Silica deposition on both filmed and bare Silicon wafers prior to SSIS recipe creation and ADC creates a challenge for light scattering surface intensity based defect binning. This study explored the theoretical maximal SSIS sensitivity based on native defect recipe creation in conjunction with the maximal sensitivity derived from BRDF modeling recipe creation. Single film and film stack wafers were inspected with recipes based upon BRDF modeling. Following SSIS recipe creation, initially targeting maximal sensitivity, selected recipes were optimized to classify defects commonly found on non-patterned wafers. The results were utilized to determine the ADC binning accuracy of the native defects and evaluate the SSIS recipe creation methodology. A statistically valid sample of defects from the final inspection results of each SSIS recipe and filmed substrate were reviewed post SSIS ADC processing on a Defect Review Scanning Electron Microscope (SEM). Native defect images were collected from each statistically valid defect bin category/size for SEM Review. The data collected from the Defect Review SEM was utilized to determine the statistical purity and accuracy of each SSIS defect classification bin. This paper explores both, commercial and technical, considerations of the elimination of PSL and Silica deposition as a precursor to SSIS recipe creation targeted towards ADC. Successful integration of SSIS ADC in conjunction with recipes created via BRDF modeling has the potential to dramatically reduce the workload requirements of a Defect Review SEM and save a significant amount of capital expenditure for 450mm SSIS recipe creation.
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