The limits and extensibility of optical patterned defect inspection

Silver, Richard M.; Barnes, Brian M.; Sohn, Yeung Joon; Quintanilha, R.; Zhou, Hui; Deeb, Chris; Johnson, Mark A.; Goodwin, Milton

doi:10.1117/12.850935

Cited by 12 publications

(9 citation statements)

References 7 publications

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“…Experimental methods for making full use of this light field have included angle-resolved imaging in a high-magnification platform [5,16-18] as well as the acquisition of focus-resolved images for defect metrology [16,19,20] and dimensional metrology [14,21]. …”

Section: Scatterfield Microscopymentioning

confidence: 99%

Enabling quantitative optical imaging for in-die-capable critical dimension targets

et al. 2016

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Dimensional scaling trends will eventually bring semiconductor critical dimensions (CDs) down to only a few atoms in width. New optical techniques are required to address the measurement and variability for these CDs using sufficiently small in-die metrology targets. Recently, Qin et al. [Light Sci Appl, 5, e16038 (2016)] demonstrated quantitative model-based measurements of finite sets of lines with features as small as 16 nm using 450 nm wavelength light. This paper uses simulation studies, augmented with experiments at 193 nm wavelength, to adapt and optimize the finite sets of features that work as in-die-capable metrology targets with minimal increases in parametric uncertainty. A finite element based solver for time-harmonic Maxwell's equations yields two- and three-dimensional simulations of the electromagnetic scattering for optimizing the design of such targets as functions of reduced line lengths, fewer number of lines, fewer focal positions, smaller critical dimensions, and shorter illumination wavelength. Metrology targets that exceeded performance requirements are as short as 3 μm for 193 nm light, feature as few as eight lines, and are extensible to sub-10 nm CDs. Target areas measured at 193 nm can be fifteen times smaller in area than current state-of-the-art scatterometry targets described in the literature. This new methodology is demonstrated to be a promising alternative for optical model-based in-die CD metrology.

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Section: Scatterfield Microscopymentioning

confidence: 99%

Enabling quantitative optical imaging for in-die-capable critical dimension targets

et al. 2016

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“…[18]. Through-focus optical differential images were also used to study defects [19]. As will be seen in the following sections, TSOM method uses differential TSOM images (i.e differential cross sectional intensity images) as opposed to differential images as used in this defect analysis work.…”

Section: ------------------------------------------------------------mentioning

confidence: 99%

Through-focus scanning optical microscopy

2011

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Through-focus scanning optical microscopy (TSOM) is another 'scanning' based method that provides threedimensional information (i.e. the size, shape and location) about micro-and nanometer-scale structures. TSOM, based on a conventional optical microscope, achieves this by acquiring and analyzing a set of optical images collected at various focus positions going through focus (from above-focus to under-focus). The measurement sensitivity is comparable to what is possible with typical light scatterometry, SEM and AFM. One of the unique characteristics of the TSOM method is its ability to separate different dimensional differences (i.e. ability to distinguish, for example, linewidth difference from line height difference), and hence is expected to reduce measurement uncertainty. TSOM holds the promise of high-throughput, through comparative measurement applications for wide variety of application areas with potentially significant savings and yield improvements.

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“…Defect metrology concentrates on locating and identifying these defects during manufacturing to increase yield. Optical tools, such as scatterometry [10] or imaging techniques [11], are the only way to successfully inspect these defects non-destructively at high speeds over the area of the typical 300 mm diameter wafer. As killer defects decrease in size with shrinking device dimensions the scattered intensity from these defects becomes harder to detect, thus for either approach a large amount of data need to be processed.…”

Section: Introductionmentioning

confidence: 99%

Data-driven approaches to optical patterned defect detection

2019

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Computer vision and classification methods have become increasingly wide-spread in recent years due to ever-increasing access to computation power. Advances in semiconductor devices are the basis for this growth, but few publications have probed the benefits of data-driven methods for improving a critical component of semiconductor manufacturing, the detection and inspection of defects for such devices. As defects become smaller, intensity threshold-based approaches eventually fail to adequately discern differences between faulty and non-faulty structures. To overcome these challenges we present machine learning methods including convolutional neural networks (CNN) applied to image-based defect detection. These images are formed from the simulated scattering of realistic geometries with and without key defects while also taking into account line edge roughness (LER). LER is a known and challenging problem in fabrication as it yields additional scattering that further complicates defect inspection. Simulating images of an intentional defect array, a CNN approach is applied to extend detectability and enhance classification to these defects, even those that are more than 20 times smaller than the inspection wavelength.

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The limits and extensibility of optical patterned defect inspection

Cited by 12 publications

References 7 publications

Enabling quantitative optical imaging for in-die-capable critical dimension targets

Enabling quantitative optical imaging for in-die-capable critical dimension targets

Through-focus scanning optical microscopy

Data-driven approaches to optical patterned defect detection

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