Mr. Steve A. McGarvey Profile

Steve A. McGarvey

Senior Application Engineer at Hitachi High-Tech America Inc

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Publications (10)

Proceedings Article | 28 March 2017 Paper

Scanning electron microscope automatic defect classification of process induced defects

Scott Wolfe, Steve McGarvey

Proceedings Volume 10145, 1014523 (2017) https://doi.org/10.1117/12.2258122

KEYWORDS: Scanning electron microscopy, Defect inspection, Inspection, Electron microscopes, Image processing, Semiconducting wafers, Process control, Semiconductor manufacturing, High volume manufacturing, Research facilities, Oxides, Silicon, Francium, Defect detection

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With the integration of high speed Scanning Electron Microscope (SEM) based Automated Defect Redetection (ADR) in both high volume semiconductor manufacturing and Research and Development (R and D), the need for reliable SEM Automated Defect Classification (ADC) has grown tremendously in the past few years. In many high volume manufacturing facilities and R and D operations, defect inspection is performed on EBeam (EB), Bright Field (BF) or Dark Field (DF) defect inspection equipment. A comma separated value (CSV) file is created by both the patterned and non-patterned defect inspection tools. The defect inspection result file contains a list of the inspection anomalies detected during the inspection tools’ examination of each structure, or the examination of an entire wafers surface for non-patterned applications. This file is imported into the Defect Review Scanning Electron Microscope (DRSEM). Following the defect inspection result file import, the DRSEM automatically moves the wafer to each defect coordinate and performs ADR. During ADR the DRSEM operates in a reference mode, capturing a SEM image at the exact position of the anomalies coordinates and capturing a SEM image of a reference location in the center of the wafer. A Defect reference image is created based on the Reference image minus the Defect image. The exact coordinates of the defect is calculated based on the calculated defect position and the anomalies stage coordinate calculated when the high magnification SEM defect image is captured. The captured SEM image is processed through either DRSEM ADC binning, exporting to a Yield Analysis System (YAS), or a combination of both. Process Engineers, Yield Analysis Engineers or Failure Analysis Engineers will manually review the captured images to insure that either the YAS defect binning is accurately classifying the defects or that the DRSEM defect binning is accurately classifying the defects. This paper is an exploration of the feasibility of the utilization of a Hitachi RS4000 Defect Review SEM to perform Automatic Defect Classification with the objective of the total automated classification accuracy being greater than human based defect classification binning when the defects do not require multiple process step knowledge for accurate classification. The implementation of DRSEM ADC has the potential to improve the response time between defect detection and defect classification. Faster defect classification will allow for rapid response to yield anomalies that will ultimately reduce the wafer and/or the die yield.

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 | 14 April 2014 Paper

Plasma etched surface scanning inspection recipe creation based on bidirectional reflectance distribution function and polystyrene latex spheres

Tiffany Saldana, Steve McGarvey, Steve Ayres

Proceedings Volume 9050, 905038 (2014) https://doi.org/10.1117/12.2057401

KEYWORDS: Bidirectional reflectance transmission function, Semiconducting wafers, Inspection, Optical spheres, Light scattering, Plasma etching, Silica, Latex, Signal to noise ratio, Plasma

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The continual increasing demands upon Plasma Etching systems to self-clean and continue Plasma Etching with minimal downtime allows for the examination of SiCN, SiO2 and SiN defectivity based upon Surface Scanning Inspection Systems (SSIS) wafer scan results. Historically all Surface Scanning Inspection System wafer scanning recipes have been based upon Polystyrene Spheres wafer deposition for each film stack and the subsequent creation of light scattering sizing response curves. This paper explores the feasibility of the elimination of Polystyrene Latex Sphere (PSL) and/or process particle deposition on both filmed and bare Silicon wafers prior to Surface Scanning Inspection System recipe creation. The study will explore the theoretical maximal Surface Scanning Inspection System sensitivity based on PSL recipe creation in conjunction with the maximal sensitivity derived from Bidirectional Reflectance Distribution Function (BRDF) maximal sensitivity modeling recipe creation. The surface roughness (Root Mean Square) of plasma etched wafers varies dependent upon the process film stack. Decrease of the root mean square value of the wafer sample surface equates to higher surface scanning inspection system sensitivity. Maximal sensitivity SSIS scan results from bare and filmed wafers inspected with recipes created based upon Polystyrene/Particle Deposition and recipes created based upon BRDF modeling will be overlaid against each other to determine maximal sensitivity and capture rate for each type of recipe that was created with differing recipe creation modes. A statistically valid sample of defects from each Surface Scanning Inspection system recipe creation mode and each bare wafer/filmed substrate will be reviewed post SSIS System processing on a Defect Review Scanning Electron Microscope (DRSEM). Native defects, Polystyrene Latex Spheres will be collected from each statistically valid defect bin category/size. The data collected from the DRSEM will be utilized to determine the maximum sensitivity capture rate for each recipe creation mode. Emphasis will be placed upon the sizing accuracy of PSL versus BRDF modeling results based upon automated DRSEM defect sizing. An examination the scattering response for both Mie and Rayleigh will be explored in relationship to the reported sizing variance of the SSIS to make a determination of the absolute sizing accuracy of the recipes there were generated based upon BRDF modeling. This paper explores both the commercial and technical considerations of the elimination of PSL deposition as a precursor to SSIS recipe creation. Successful integration of BRDF modeling into the technical aspect of SSIS recipe creation process has the potential to dramatically reduce the recipe creation timeline and vetting period. Integration of BRDF modeling has the potential to greatly reduce the overhead operation costs for High Volume Manufacturing sites by eliminating the associated costs of third party PSL deposition.

Proceedings Article | 10 April 2013 Paper

The challenges encountered in the integration of an early test wafer surface scanning inspection system into a 450mm manufacturing line

Jeffrey Lee, Steve McGarvey

Proceedings Volume 8681, 86811V (2013) https://doi.org/10.1117/12.2011646

KEYWORDS: Semiconducting wafers, Silicon, Bidirectional reflectance transmission function, Inspection, Manufacturing, Light scattering, Polarization, Surface roughness, Scattering, Wafer testing

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The introduction of early test wafer (ETW) 450mm Surface Scanning Inspection Systems (SSIS) into Si manufacturing has brought with it numerous technical, commercial, and logistical challenges on the path to rapid recipe development and subsequent qualification of other 450mm wafer processing equipment. This paper will explore the feasibility of eliminating the Polystyrene Latex Sphere deposition process step and the subsequent creation of SSIS recipes based upon the theoretical optical properties of both the SSIS and the process film stack(s). The process of Polystyrene Latex Sphere deposition for SSIS recipe generation and development is generally accepted on the previous technology nodes for 150/200/300mm wafers. PSL is deposited with a commercially available deposition system onto a non-patterned bare Si or non-patterned filmed Si wafer. After deposition of multiple PSL spots, located in different positions on a wafer, the wafer is inspected on a SSIS and a response curve is generated. The response curve is based on the the light scattering intensity of the NIST certified PSL that was deposited on the wafer. As the initial 450mm Si wafer manufacturing began, there were no inspection systems with sub-90nm sensitivities available for defect and haze level verification. The introduction of a 450mm sub-30nm inspection system into the manufacturing line generated instant challenges. Whereas the 450mm wafers were relatively defect free at 90nm, at 40nm the wafers contained several hundred thousand defects. When PSL was deposited onto wafers with these kinds of defect levels, PSL with signals less than the sub-90nm defects were difficult to extract. As the defectivity level of the wafers from the Si suppliers rapidly improves the challenges of SSIS recipe creation with high defectivity decreases while at the same time the cost of PSL deposition increases. The current cost per wafer is fifteen thousand dollars for a 450mm PSL deposition service. When viewed from the standpoint of the generations of hundreds of SSIS recipes for the global member companies of ISMI, it is simply not economically viable to create all recipes based on PSL based light scattering response curves. This paper will explore the challenges/end results encountered with the PSL based SSIS recipe generation and compare those against the challenges/end results of SSIS recipes generated based strictly upon theoretical Bidirectional reflectance distribution function (BRDF) light scattering modeling. The BRDF modeling will allow for the creation of SSIS recipes without PSL deposition, which is greatly appealing for a multitude of both technical and commercial considerations. This paper will also explore the technical challenges of SSIS recipe generation based strictly upon BRDF modeling.

Proceedings Article | 6 April 2012 Paper

Surface scanning inspection system particle detection dependence on aluminum film morphology

Walter Prater, Natalie Tran, Steve McGarvey

Proceedings Volume 8324, 832433 (2012) https://doi.org/10.1117/12.916540

KEYWORDS: Aluminum, Light scattering, Optical spheres, Semiconducting wafers, Bidirectional reflectance transmission function, Inspection, Atmospheric modeling, Scanning electron microscopy, Atomic force microscopy, Scattering

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Physical vapor deposition (PVD) aluminum films present unique challenges when detecting particulate defects with a Surface Scanning Inspection System (SSIS). Aluminum (Al) films 4500Å thick were deposited on 300mm particle grade bare Si wafers at two temperatures using a Novellus Systems INOVA® NExT,.. Film surface roughness and morphology measurements were performed using a Veeco Vx310® atomic force microscope (AFM). AFM characterization found the high deposition temperature (TD) Al roughness (Root Mean Square 16.5 nm) to be five-times rougher than the low-TD Al roughness (rms 3.7 nm). High-TD Al had grooves at the grain boundaries that were measured to be 20 to 80 nm deep. Scanning electron microscopy (SEM) examination, with a Hitachi RS6000 defect review SEM, confirmed the presence of pronounced grain grooves. SEM images established that the low-TD filmed wafers have fine grains (0.1 to 0.3 um diameter) and the high-TD film wafers have fifty-times larger equiaxed plateletshape grains (5 to 15 um diameter). Calibrated Poly-Styrene Latex (PSL) spheres ranging in size from 90 nm to 1 μm were deposited in circular patterns on the wafers using an aerosol deposition chamber. PSL sphere depositions at each spot were controlled to yield 2000 to 5000 counts. A Hitachi LS9100® dark field full wafer SSIS was used to experimentally determine the relationship of the PSL sphere scattered light intensity with S-polarized light, a measure of scattering cross-section, with respect to the calibrated PSL sphere diameter. Comparison of the SSIS scattered light versus PSL spot size calibration curves shows two distinct differences. Scattering cross-section (intensity) of the PSL spheres increased on the low-TD Al film with smooth surface roughness and the low-TD Al film defect detection sensitivity was 126 nm compared to 200 nm for the rougher high- TD Al film. This can be explained by the higher signal to noise attributed to the smooth low-TD Al. Dark field defect detection on surface scanning inspection systems is used to rapidly measure defectivity data. The user generates a calibration curve on the SSIS to plot the intensity of the light scattering derived at each National Institute of Standards and Technology (NIST) certified PSL deposition spot that was deposited. It is not uncommon for the end user to embark upon the time consuming process of attempting to "push" the maximal SSIS film specific sensitivity curve beyond the optical performance capability of the SSIS. Bidirectional reflectance distribution function (BRDF) light scattering modeling was utilized as a means of determining the most appropriate polarity prior to the SSIS recipe creation process. The modeling utilized the Al refractive index (n) and extinction coefficient (k) and the SSIS detector angles and laser wavelength. The modeling results allowed predetermination of the maximal sensitivity for each different Al thickness and eliminate unnecessary recipe modification trial-and-error in search of the SSIS maximal sensitivity. The modeling accurately forecasted the optimal polarization and maximal sensitivity of the SSIS recipe, which, by avoiding a trial and error approach, can result in a substantial savings in time and resources.

Proceedings Article | 20 April 2011 Paper

Surface scanning inspection system defect classification of CMP induced scratches

Steve McGarvey, Anne Miller

Proceedings Volume 7971, 79712P (2011) https://doi.org/10.1117/12.881105

KEYWORDS: Inspection, Copper, Chemical mechanical planarization, Surface finishing, Scanning electron microscopy, Defect inspection, Classification systems, Semiconducting wafers, Particles, Polishing

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Proceedings Article | 16 April 2010 Paper

HVM die yield improvement as a function of DRSEM ADC

Sonu Maheshwary, Terry Haas, Steve McGarvey

Proceedings Volume 7638, 763822 (2010) https://doi.org/10.1117/12.846700

KEYWORDS: Manufacturing, Inspection, Scanning electron microscopy, Semiconducting wafers, Image processing, Image classification, Semiconductor manufacturing, Yield improvement, Classification systems, Electron microscopes

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Proceedings Article | 2 April 2010 Paper

High-resolution defect metrology for silicon BARC analysis

Brian Smith, Steve McGarvey, Zhimin Zhu, Yubao Wang, Dan Sullivan

Proceedings Volume 7638, 763824 (2010) https://doi.org/10.1117/12.846690

KEYWORDS: Coating, Semiconducting wafers, Silicon, System on a chip, Particles, Reflectivity, Air contamination, Metrology, Etching, Surface roughness

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Measuring coating defects on two or more blanket film layers is difficult and can be misleading due to reflectivity changes from the bottom layer, and surface roughness not present when the substrate is only polished silicon. To improve signal-to-noise ratio and establish a lower limit for particle size detection, polystyrene latex (PSL) spheres are deposited on the film stack. Particles as small as 54 nm were detectable on a stack 330-nm thick using a Hitachi LS Series Surface Scanning Inspection System (SSIS) and RS5500 Defect Review Scanning Electron Microscope (DRSEM). These systems have advanced capabilities enabling automated detection, classification, and characterization of defects down to 30 nm or smaller on some substrates and films. Haze wafer maps are related to surface roughness and reflectivity and show unusual asymmetries possibly caused by dispense problems or exhaust flow patterns during baking. These maps can be helpful to find problems in the coating system, even if film thickness is on target. Preliminary testing results are presented for a typical trilayer pattern stack for high-resolution 193-nm patterning consisting of a silicon spinon hardmask (HM) layer on top of a spin-on carbon (SOC) layer. The majority of the defects were caused by bubble formation within the HM that was modulated by process conditions used for these tests. A higher spin speed for the HM coating produced lower defects, most likely due to a thinner film with less trapped solvent during baking, but this effect will require more study, as it could also be due to a faster evaporation rate caused by higher airflow. Pre-wet, spin time, and bake temperature did not produce significant effects within these tests, but showed trends requiring further study. These advanced spin-on HM materials can be applied as thin as 15 to 20 nm due to their high etch selectivity. With the use of such high-resolution defect metrology, very subtle chemical interactions and process effects can be examined to find the ideal process conditions for both the SOC and HM layers.

Proceedings Article | 24 March 2009 Paper

Automated defect review of the wafer bevel with a defect review scanning electron microscope

Steve McGarvey, Masakazu Kanezawa

Proceedings Volume 7272, 72723N (2009) https://doi.org/10.1117/12.814235

KEYWORDS: Semiconducting wafers, Inspection, Scanning electron microscopy, Manufacturing, Statistical analysis, Defect inspection, Inspection equipment, Photoresist processing, Electron microscopes, Integrated circuits

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Proceedings Article | 16 April 2008 Paper

Compensating for SSIS sizing/classification error in a defect review SEM world

David Ruprecht, Steve McGarvey

Proceedings Volume 6922, 692242 (2008) https://doi.org/10.1117/12.796645

KEYWORDS: Scanning electron microscopy, Semiconducting wafers, Manufacturing, Silicon, Inspection, Image classification, Optical spheres, Light scattering, Defect inspection, Classification systems

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