Kla Tencor Corporation

Kla Corporation

Kla-tencor Technologies Corporation

Jan A Lauber

Yong Zhang

Mark Armstrong McCord

Lisheng Gao

Hucheng Lee

Jason Kirkwood

Himanshu Vajaria

Allen Park

Gabor D Toth

Ajay Gupta

Paul F Petric

Xing Chu

Frank Y H Fan

Jun Pei

J Rex Runyon

Ross W Thompson

Jason Lim

Jorge P Fernandez

A system has detectors configured to receive a beam of light reflected from a wafer. For example, three detectors may be used. Each of the detectors is a different channel. Images from the detectors are combined into a pseudo-color RGB image. A convolutional neural network unit (CNN) can receive the pseudo-color RGB image and determine a size of a defect in the pseudo-color RGB image. The CNN also can classify the defect into a size category.

Defect size measurement using deep learning methods

The correlation of optical images with SEM images includes acquiring a full optical image of a sample by scanning the sample with an optical inspection sub-system, storing the full optical image, identifying a location of a feature-of-interest present in the full optical image with an additional sources, acquiring an SEM image of a portion of the sample that includes the feature at the identified location with a SEM tool, acquiring an optical image portion at the location identified by the additional source, the image portions including a reference structure, correlating the image portion and the SEM image based on the presence of the feature-of-interest and the reference structure in both the image portions and the SEM image, and transferring a location of the feature-of-interest in the SEM image into the coordinate system of the image portion of the full optical image to form a corrected optical image.

Method and system for correlating optical images with scanning electron microscopy images

The present disclosure describes methods, systems, and articles of manufacture for performing a defect inspection of a die image using adaptive care areas (ACAs). The use of ACAs solve the problem of handling rotations of components that require rotating care areas; handling the situation where each care area requires its own rotation, translation, or affine transformation; and the situation of decoupling intensity differences caused by defects or process variation from intensity differences caused by size variations.

Adaptive care areas for die-die inspection

A system for defect detection and analysis is provided. The system may include an inspection sub-system and a controller including a memory and one or more processors. The inspection sub-system may include an illumination source and one or more detectors configured to acquire control patch images of defects of a control specimen along one or more detector channels. The one or more processors may be configured to train a defect classifier using the control patch images and known parameters associated with the defects of the control specimen. The inspection sub-system may be further configured to acquire patch images of identified defects on an additional specimen. The one or more processors may be configured to determine parameters of the identified defects using the defect classifier.

System and method for characterization of buried defects

A die-die inspection image can be aligned using a method or system configured to receive a reference image and a test image, determine a global offset and rotation angle from local sections on the reference image and test image, and perform a rough alignment de-skew of the test image prior to performing a fine alignment.

Multi-step image alignment method for large offset die-die inspection

Methods and systems for determining parameter(s) of a process to be performed on a specimen are provided. One system includes one or more computer subsystems configured for determining an area of a defect detected on a specimen. The computer subsystem(s) are also configured for correlating the area of the defect with information for a design for the specimen and determining a spatial relationship between the area of the defect and the information for the design based on results of the correlating. In addition, the computer subsystem(s) are configured for automatically generating a region of interest to be measured during a process performed for the specimen with a measurement subsystem based on the spatial relationship.

Self directed metrology and pattern classification

Various embodiments for detecting defects on a wafer are provided. Some embodiments include matching a template image, in which at least some pixels are associated with regions in the device having different characteristics, to output of an electron beam inspection system and applying defect detection parameters to pixels in the output based on the regions that the pixels in the output are located within to thereby detect defects on the wafer.

Detecting defects on a wafer using template image matching

Various embodiments for classifying defects detected on a wafer are provided. One method includes acquiring an electron beam image generated by a defect review tool for a location of a defect detected on a wafer by a wafer inspection tool. The method also includes determining a classification of the defect based on at least the electron beam image and without input from a user. The method may also include feeding back the classification results to the wafer inspection tool and optimizing the parameters of the tool to maximize sensitivity to the defects of interest.

Defect discovery and inspection sensitivity optimization using automated classification of corresponding electron beam images

One embodiment relates to an apparatus for automated inspection of a semiconductor substrate. Processor-executable code is configured to control the stage electronics to move the substrate using a continuous motion in a substrate-translation direction and is configured to control the beam to scan it across the surface of the substrate and collect corresponding image data, scan lines of the scan being along a scan-line direction perpendicular to the substrate-translation direction. Processor-executable code is also configured to select from the image data two cells of the repeating pattern on the surface of the substrate, the two cells being displaced from each other by one or multiple cell heights in the scan-line direction. Finally, processor-executable code is configured to generate a difference image by subtracting image data from said two cells on a pixel-by-pixel basis. Other embodiments, aspects and features are also disclosed.

Automated inspection using cell-cell subtraction perpendicular to stage motion direction

A method for monitoring the stability of an inspection or processing of a substrate, a substrate inspection system and a processor readable medium are disclosed. One or more images of one or more portions of the substrate may be obtained from an inspection tool. Image quality information may be extracted from the one or more images. The image quality information may be analyzed to monitor stability of the inspection tool and/or to determine variation of a process performed on the substrate.

Image quality monitoring for substrate inspection

One embodiment disclosed relates to a method of setting a surface charge of an area on a substrate to a desired level. The substrate is held on a stage, and a stage bias voltage applied to the stage is controlled. A flood of electrons is directed to the area. The stage bias voltage is controlled such that the surface charge of the area reaches an equilibrium at the desired level. Another embodiment disclosed relates to a method of auto-focusing a main electron beam incident upon an imaging area of a substrate. A monitor electron beam is generated and directed towards a monitoring area of the substrate at a non-perpendicular incidence angle. An in-focus band in data collected from the monitor beam is detected. The focal length of an objective lens focusing the main beam is adjusted based upon a position of the in-focus band.

Apparatus and methods of controlling surface charge and focus

Disclosed is a method and apparatus for generating an image from a sample. The apparatus includes a charged particle beam generator arranged to generate and control a charged particle beam substantially towards a portion of the sample and a detector arranged to detect charged particles originating from the sample portion to allow generation of an image from the detected charged particles. The apparatus further includes a measurement device arranged to measure a characteristic of the sample portion to obtain a surface voltage value of the sample portion that is exposed to the charged particle beam. For example, the measurement device is an electrostatic voltmeter positioned to obtain a surface voltage value of the exposed sample portion. A charged particle beam is directed substantially towards a portion of the sample under a first set of operating conditions. A surface charge value of the sample portion is obtained under the first set of operating conditions. It is then determined whether an optimum set of operating conditions associated with a predetermined surface charge value have been found. When the optimum conditions have not been found, the operating conditions are adjusted and the charged particle beam is directed substantially towards the sample portion. When the optimum conditions have been found, the charged particle beam is directed substantially towards the sample portion under the found optimum operating conditions.

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San Jose, CA, United States of America

Jan A Lauber