Ultrapointe Corporation

Kla Tencor Corporation

Bruce W Worster

Ken K Lee

Christopher R Fairley

Dale E Crane

Hans J Hansen

Ke Han

M Kent Norton

Lakshman Srinivasan

Kexing Cecilia Du

Louis D Calitz

A method is described for characterizing defects on a test surface of a semiconductor wafer using a confocal-microscope-based automatic defect characterization (ADC) system. The surface to be tested and a reference surface are scanned using a confocal microscope to obtain three-dimensional images of the test and reference surfaces. The test and reference images are converted into sets of geometric constructs, or 'primitives,' that are used to approximate features of the images. Next, the sets of test and reference primitives are compared to determine whether the set of test primitives is different from the set of reference primitives. If such a difference exists, then the difference data is used to generate defect parameters, which are then compared to a knowledge base of defect reference data. Based on this comparison, the ADC system characterizes the defect and estimates a degree of confidence in the characterization.

Method for characterizing defects on semiconductor wafers

A method is described for characterizing defects on a test surface of a semiconductor wafer using a confocal-microscope-based automatic defect characterization (ADC) system. The surface to be tested and a reference surface are scanned using a confocal microscope to obtain three-dimensional images of the test and reference surfaces. The test and reference images are converted into sets of geometric constructs, or &ldquo;primitives,&rdquo; that are used to approximate features of the images. Next, the sets of test and reference primitives are compared to determine whether the set of test primitives is different from the set of reference primitives. If such a difference exists, then the difference data is used to generate defect parameters, which are then compared to a knowledge base of defect reference data. Based on this comparison, the ADC system characterizes the defect and estimates a degree of confidence in the characterization.

A method is described for characterizing defects on a test surface of a semiconductor wafer using a confocal-microscope-based automatic defect characterization (ADC) system. The surface to be tested and a reference surface are scanned using a confocal microscope to obtain three-dimensional images of the test and reference surfaces. The test and reference images are converted into sets of geometric constructs, or &ldquo;primitives,&rdquo; that are used to approximate features of the images. Next, the sets of test and reference primitives are compared to determine whether the set of test primitives is different from the set of reference primitives. If such a difference a exists, then the difference data is used to generate defect parameters, which are then compared to a knowledge base of defect reference data. Based on this comparison, the ADC system characterizes the defect and estimates a degree of confidence in the characterization.

An improved wafer surface inspection system is disclosed. In one embodiment, the object surface inspection system includes a translation stage that generates relative motion between an object viewing device such as an objective lens and the surface of the object being inspected. A translation stage controller controls the relative movement of the object surface and the object viewing device. The translation stage controller determines current coordinates for the object surface and the object viewing device, compares the current coordinates to target coordinates generated by a processor, and generates a trigger signal in response to a match between the current coordinates to the target coordinates. A camera receives an image through the object viewing device and captures the image in response to the trigger signal while the translation stage generates relative motion between the object surface and the object viewing device. In accordance with the present invention, a white light image of an entire wafer surface may be obtained quickly and efficiently. Image processing software may then be used to identify solder bumps on the wafer surface and calculate parameters of the solder bumps. Quality control criteria may be automatically applied to the solder bump parameters to determine the suitability of the wafer for further processing, and to identify problems in wafer processing.

Method and system for inspecting the surface of a wafer

A laser imaging system is used to analyze defects on semiconductor wafers that have been detected by patterned wafer defect detecting systems (wafer scanners). The laser imaging system replaces optical microscope review stations now utilized in the semiconductor fab environment to examine detected optical anomalies that may represent wafer defects. In addition to analyzing defects, the laser imaging system can perform a variety of microscopic inspection functions including defect detection and metrology. The laser imaging system uses confocal laser scanning microscopy techniques, and operates under class 1 cleanroom conditions and without exposure of the wafers to operator contamination or airflow. Unlike scanning electron microscopes (SEMs) that have previously been used for defect analysis, the laser imaging system will not damage samples or slow processing, costs significantly less to implement than an SEM, can produce a three dimensional image which provides quantitative dimensional information, and allows sub-surface viewing of defects lying beneath dielectric layers. The laser imaging system is adaptable to cluster or in-situ applications, where examination of defects or structures during on-line processing can be performed.

Laser imaging system for inspection and analysis of sub-micron particles

10A method is described for detecting and characterizing defects on a test surface of a semiconductor wafer. A three-dimensional image of the test surface is aligned and compared with a three-dimensional image of a defect-free reference surface. Intensity differences between corresponding pixels in the two images that exceed a predefined threshold value are deemed defect pixels. According to the method, the pixels of the reference image are grouped according to their respective z values (elevation) to identify different physical layers of the reference surface. Because different surface layers can have different image properties, such as reflectance and image texture, the groups of pixels are analyzed separately to determine an optimal threshold value for each of the groups, and therefore for each layer of the reference surface.

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Saratoga, CA, United States of America

Bruce W Worster