Mr. Alexander Zhivotovsky Profile

Alexander Zhivotovsky

Senior Director

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Publications (2)

Proceedings Article | 25 March 2016 Paper

Recipe creation for automated defect classification with a 450mm surface scanning inspection system based on the bidirectional reflectance distribution function of native defects

Nithin Yathapu, Steve McGarvey, Justin Brown, Alexander Zhivotovsky

Proceedings Volume 9778, 97783J (2016) https://doi.org/10.1117/12.2222306

KEYWORDS: Inspection, Scanning electron microscopy, Classification systems, Bidirectional reflectance transmission function, Latex, Optical spheres, Electron microscopes, Defect inspection, Semiconducting wafers, Optical scanning systems, Wafer inspection, Wafer manufacturing, Wafer testing, Silica, Light scattering, Air contamination, Copper, Optical properties

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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.

Proceedings Article | 17 May 2005 Paper

Automated process control optimization to control low volume products based on high volume products data

Tatiana Levin, Issi Geier, Alex Zhivotovsky, Nilufar Aframiam, Hamutal Friedlander-Klar

Proceedings Volume 5755, (2005) https://doi.org/10.1117/12.598409

KEYWORDS: Process control, Lithium, Data analysis, Data modeling, Statistical analysis, Error analysis, Software development, Time metrology, Computer simulations, Information operations

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