TECHNICAL FIELD

The described embodiments relate to systems for specimen inspection, and more particularly to semiconductor wafer inspection.

BACKGROUND INFORMATION

Semiconductor devices such as logic and memory devices are typically fabricated by a sequence of processing steps applied to a substrate or wafer. The various features and multiple structural levels of the semiconductor devices are formed by these processing steps. For example, lithography among others is one semiconductor fabrication process that involves generating a pattern on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing, etch, deposition, and ion implantation. Multiple semiconductor devices may be fabricated on a single semiconductor wafer and then separated into individual semiconductor devices.

Inspection processes are used at various steps during a semiconductor manufacturing process to detect defects on both unpatterned and patterned wafers to promote higher yield. As design rules and process windows continue to shrink in size, inspection systems are required to capture a wider range of physical defects with ever smaller minimum defect sizes, while maintaining high throughput.

In addition, memory architectures are transitioning from two dimensional floating-gate architectures to fully three dimensional geometries. In some examples, film stacks and etched structures are very deep (e.g., up to six micrometers in depth, or more). Such high aspect ratio structures create challenges for patterned wafer inspection. The ability to measure defects buried within these structures is critical to achieve desired performance levels and device yield.

Optical inspection techniques have proven effective for high throughput detection of defects associated with two dimensional structures and defects buried within three dimensional structures. Traditionally, defect discovery and inspection recipe optimization are based on the inspection of planar, two-dimensional structures. An optical inspection tool is employed to measure a large number of candidate defects associated with two dimensional structures (e.g., less than one micrometer thick) located at the surface of the wafer. The candidate defects detected by the optical inspection tool are verified by inspecting the identified DOI with a scanning electron microscopy (SEM) tool. This is commonly referred to as SEM review. The SEM tool is able to generate images that allow a user to accurately classify a candidate defect as a nuisance defect (i.e., defects identified by the optical inspection tool that are not really defects) or a defect of interest (DOI). Further, the SEM images allow a user to accurately classify the type of DOI. The result of SEM review is set of labeled defects identifying the location of nuisance defects and DOIs on a wafer and the classification of the DOIs.

In many examples, an inspection recipe for an optical inspection tool is formulated based on the labeled defects identified during SEM review. For inspection tasks that require identifying defects of interest from nuisance events, a successful inspection recipe for a semiconductor layer should maximize the number of detects of interest (DOI) detected while minimizing the number of nuisance events detected. By extension, for inspection tasks that require binning of defects, a successful inspection recipe for a semiconductor layer should maximize the number of correctly binned defects (i.e., correctly classified defects) while minimizing the number of nuisance events detected and the number of incorrectly classified defects.

In one example, nuisance filtering and defect classification based on optical inspection is realized by estimating the values of one or more attributes derived from images of a candidate defect. A trained classification model is employed to identify a DOI from a nuisance defect, classify a DOI, or both, based on the values of the one or more attributes. The classification model is typically trained based on attributes of images of labeled defects collected by the optical inspection system. The classification model is trained using a manual decision tree or a classical machine learning algorithm such as support vector machine (SVM), Random Forest, K-nearest neighbor, etc. In these examples, the one or more attributes derived from the images are manually selected (i.e., conjured by a human). There is no guarantee that the selected attribute or set of attributes optimally captures the desired separation between DOIs and nuisance defects. Further, there is no guarantee that the selected attribute or set of attributes optically classifies the DOIs. Thus, it is highly likely that a classification model trained based on manually selected attributes will underperform. This becomes limiting as inspection applications become more and more challenging.

In another example, nuisance filtering and defect classification based on optical inspection is realized by directly identifying and classifying a defect from images of a candidate defect, rather than attributes of the images. A trained classification model is employed to directly identify a DOI from a nuisance defect, classify a DOI, or both, based on images of the candidate defect. The trained classification model is typically trained based on images of labeled defects collected by the optical inspection system. The classification model is trained using a machine learning algorithm such as a convolutional neural network, deep learning neural network, etc. In these examples, the neural network is able to learn custom features that are optimal for the classification task at hand. However, the computational cost of executing a complex, trained neural network for inline wafer inspection is excessively high. Thus, high throughput at an acceptable cost has not been attained. Furthermore, it has not been possible to integrate human knowledge of defect classification or information beyond the available optical images to enhance the training process. Thus, currently, a classification model trained based on a neural network to operate directly on images cannot benefit from human knowledge or additional signal information that might further improve performance of the trained classifier.

Accordingly, it would be advantageous to develop methods and/or systems for generating defect classification models with improved performance to enhance defect discovery and classification of complex structures and vertical semiconductor devices by optical inspection systems.

SUMMARY

Methods and systems for improved detection and classification of defects of interest (DOI) is realized by estimating the values of one or more automatically generated attributes derived from images of a candidate defect. A statistical classifier is trained to classify defects based on the values of the automatically generated attributes.

In one aspect, automatically generated attributes are determined by iteratively training, reducing, and retraining a deep learning model. The deep learning model relates optical images of candidate defects to a known classification of those defects. The deep learning model is iteratively reduced in complexity. After model reduction, attributes of the reduced model are identified which strongly relate the optical images of candidate defects to the known classification of the defects. The reduced model is subsequently employed to generate values of the identified attributes associated with images of candidate defects having unknown classification. The attribute values are employed by a statistical classifier to classify the candidate defects.

In another aspect, a statistical classifier is trained to relate the values of input nodes to the output nodes of each layer of a trained neural network model. Each statistical classifier is analyzed to guide the reduction of the neural network model. In some examples, each statistical classifier is analyzed to identify whether the corresponding layer adds new information to the neural network model. If the layer does not add substantial new information, the entire layer is eliminated. In another example, each statistical classifier is analyzed to identify whether the nodes of the corresponding layer contribute substantially to the layer. Nodes which make a relatively small contribution to the layer are eliminated. Similarly, filters which make a relatively small contribution to the output of a convolutional layer are eliminated.

The combination of deep learning and independent statistical classifiers to guide the reducing of a deep learning based model provides a direct performance-driven approach to minimize the computational cost of determining attributes without losing performance.

In another further aspect, a statistical classifier is trained based on the values of automatically generated attributes and the values of manually generated attributes of defects having known classification. In this manner, a trained statistical classifier incorporates attributes known to human users to be important indicators of defects, along with attributes determined by mathematical computation to be important indicators of the same defects.

In another further aspect, the training of a statistical classifier is performed in an iterative manner while reducing the number of attributes employed in the training of the defect classifier.

The foregoing is a summary and thus contains, by necessity, simplifications, generalizations and omissions of detail; consequently, those skilled in the art will appreciate that the summary is illustrative only and is not limiting in any way. Other aspects, inventive features, and advantages of the devices and/or processes described herein will become apparent in the non-limiting detailed description set forth herein.

