Abstract: Methods and systems for determining information for a specimen are provided. Certain embodiments relate to bump height 3D inspection and metrology using deep learning artificial intelligence. For example, one embodiment includes a deep learning (DL) model configured for predicting height of one or more 3D structures formed on a specimen based on one or more images of the specimen generated by an imaging subsystem. One or more computer systems are configured for determining information for the specimen based on the predicted height. Determining the information may include, for example, determining if any of the 3D structures are defective based on the predicted height. In another example, the information determined for the specimen may include an average height metric for the one or more 3D structures.

Abstract: Methods and systems for determining information for a specimen are provided. Certain embodiments relate to bump height 3D inspection and metrology using deep learning artificial intelligence. For example, one embodiment includes a deep learning (DL) model configured for predicting height of one or more 3D structures formed on a specimen based on one or more images of the specimen generated by an imaging subsystem. One or more computer systems are configured for determining information for the specimen based on the predicted height. Determining the information may include, for example, determining if any of the 3D structures are defective based on the predicted height. In another example, the information determined for the specimen may include an average height metric for the one or more 3D structures.

Abstract: Methods and systems for training a neural network for defect detection in low resolution images are provided. One system includes an inspection tool that includes high and low resolution imaging subsystems and one or more components that include a high resolution neural network and a low resolution neural network. Computer subsystem(s) of the system are configured for generating a training set of defect images. At least one of the defect images is generated synthetically by the high resolution neural network using an image generated by the high resolution imaging subsystem. The computer subsystem(s) are also configured for training the low resolution neural network using the training set of defect images as input. In addition, the computer subsystem(s) are configured for detecting defects on another specimen by inputting the images generated for the other specimen by the low resolution imaging subsystem into the trained low resolution neural network.

Abstract: A system is configured to perform metrology on a front surface, a back surface opposite the front surface, and/or an edge between the front surface and the back surface of a wafer. This can provide all wafer metrology and/or metrology of thin films on the back surface of the wafer. In an example, the thickness and/or optical properties of a thin film on a back surface of a wafer can be determined using a ratio of a greyscale image of a bright field light emerging from the back surface of the wafer under test to that of a reference wafer.

Abstract: Disclosed are methods and apparatus for detecting defects or reviewing defects in a semiconductor sample. The system has a brightfield (BF) module for directing a BF illumination beam onto a sample and detecting an output beam reflected from the sample in response to the BF illumination beam. The system has a modulated optical reflectance (MOR) module for directing a pump and probe beam to the sample and detecting a MOR output beam from the probe spot in response to the pump beam and the probe beam. The system includes a processor for analyzing the BF output beam from a plurality of BF spots to detect defects on a surface or near the surface of the sample and analyzing the MOR output beam from a plurality of probe spots to detect defects that are below the surface of the sample.

Abstract: Methods and systems for training a neural network for defect detection in low resolution images are provided. One system includes an inspection tool that includes high and low resolution imaging subsystems and one or more components that include a high resolution neural network and a low resolution neural network. Computer subsystem(s) of the system are configured for generating a training set of defect images. At least one of the defect images is generated synthetically by the high resolution neural network using an image generated by the high resolution imaging subsystem. The computer subsystem(s) are also configured for training the low resolution neural network using the training set of defect images as input. In addition, the computer subsystem(s) are configured for detecting defects on another specimen by inputting the images generated for the other specimen by the low resolution imaging subsystem into the trained low resolution neural network.

Abstract: Disclosed are methods and apparatus for detecting defects or reviewing defects in a semiconductor sample. The system has a brightfield (BF) module for directing a BF illumination beam onto a sample and detecting an output beam reflected from the sample in response to the BF illumination beam. The system has a modulated optical reflectance (MOR) module for directing a pump and probe beam to the sample and detecting a MOR output beam from the probe spot in response to the pump beam and the probe beam. The system includes a processor for analyzing the BF output beam from a plurality of BF spots to detect defects on a surface or near the surface of the sample and analyzing the MOR output beam from a plurality of probe spots to detect defects that are below the surface of the sample.

Abstract: A system is configured to perform metrology on a front surface, a back surface opposite the front surface, and/or an edge between the front surface and the back surface of a wafer. This can provide all wafer metrology and/or metrology of thin films on the back surface of the wafer. In an example, the thickness and/or optical properties of a thin film on a back surface of a wafer can be determined using a ratio of a greyscale image of a bright field light emerging from the back surface of the wafer under test to that of a reference wafer.

Abstract: Disclosed are methods and apparatus for detecting defects or reviewing defects in a semiconductor sample. The system has a brightfield (BF) module for directing a BF illumination beam onto a sample and detecting an output beam reflected from the sample in response to the BF illumination beam. The system has a modulated optical reflectance (MOR) module for directing a pump and probe beam to the sample and detecting a MOR output beam from the probe spot in response to the pump beam and the probe beam. The system includes a processor for analyzing the BF output beam from a plurality of BF spots to detect defects on a surface or near the surface of the sample and analyzing the MOR output beam from a plurality of probe spots to detect defects that are below the surface of the sample.

Abstract: Systems and methods for enhancing inspection sensitivity to detect defects in wafers using an inspection tool are disclosed. A plurality of light emitting diodes illuminate at least a portion of a wafer and capture a set of grayscale images. A residual signal is determined in each image of the grayscale image set and the residual signal is subtracted from each image of the grayscale image set. Defects are identified based on the subtracted grayscale image set. Models of the inspection tool and wafer may be built and refined in some embodiments of the disclosed systems and methods.

Abstract: Disclosed are methods and apparatus for measuring a characteristics of a through-silicon via (TSV) structure. A beam profile reflectivity (BPR) tool is used to move to a first xy position having a TSV structure. The BPR tool is then used to obtain an optimum focus of at the first xy position by adjusting the z position to a first optimum z position for obtaining measurements at the first xy position. Via the BPR tool, reflectivity measurements for a plurality of angles of incidence are obtained at the first xy position. One or more film thicknesses for the TSV structure are determined based on the reflectivity measurements. A z position can also be recorded and used to determine a height of such TSV structure, as well as one or more adjacent xy positions.

Abstract: Disclosed are methods and apparatus for inspecting and processing semiconductor wafers. The system includes an edge detection system for receiving each wafer that is to undergo a photolithography process. The edge detection system comprises an illumination channel for directing one or more illumination beams towards a side, top, and bottom edge portion that are within a border region of the wafer. The edge detection system also includes a collection module for collecting and sensing output radiation that is scattered or reflected from the edge portion of the wafer and an analyzer module for locating defects in the edge portion and determining whether each wafer is within specification based on the sensed output radiation for such wafer. The photolithography system is configured for receiving from the edge detection system each wafer that has been found to be within specification. The edge detection system is coupled in-line with the photolithography system.

Abstract: Photoreflectance spectroscopy is used to measure strain at or near the edge of a wafer in a production process. The strain measurement is used to anticipate defects and make prospective corrections in later stages of the production process. Strain measurements are used to associate various production steps with defects to enhance later production processes.

Abstract: Systems and methods for enhancing inspection sensitivity to detect defects in wafers using an inspection tool are disclosed. A plurality of light emitting diodes illuminate at least a portion of a wafer and capture a set of grayscale images. A residual signal is determined in each image of the grayscale image set and the residual signal is subtracted from each image of the grayscale image set. Defects are identified based on the subtracted grayscale image set. Models of the inspection tool and wafer may be built and refined in some embodiments of the disclosed systems and methods.

Abstract: Disclosed are methods and apparatus for inspecting and processing semiconductor wafers. The system includes an edge detection system for receiving each wafer that is to undergo a photolithography process. The edge detection system comprises an illumination channel for directing one or more illumination beams towards a side, top, and bottom edge portion that are within a border region of the wafer. The edge detection system also includes a collection module for collecting and sensing output radiation that is scattered or reflected from the edge portion of the wafer and an analyzer module for locating defects in the edge portion and determining whether each wafer is within specification based on the sensed output radiation for such wafer. The photolithography system is configured for receiving from the edge detection system each wafer that has been found to be within specification. The edge detection system is coupled in-line with the photolithography system.

Abstract: Photoreflectance spectroscopy is used to measure strain at or near the edge of a wafer in a production process. The strain measurement is used to anticipate defects and make prospective corrections in later stages of the production process. Strain measurements are used to associate various production steps with defects to enhance later production processes.

Abstract: Disclosed are methods and apparatus for detecting defects or reviewing defects in a semiconductor sample. The system has a brightfield (BF) module for directing a BF illumination beam onto a sample and detecting an output beam reflected from the sample in response to the BF illumination beam. The system has a modulated optical reflectance (MOR) module for directing a pump and probe beam to the sample and detecting a MOR output beam from the probe spot in response to the pump beam and the probe beam. The system includes a processor for analyzing the BF output beam from a plurality of BF spots to detect defects on a surface or near the surface of the sample and analyzing the MOR output beam from a plurality of probe spots to detect defects that are below the surface of the sample.

Abstract: The present invention may include a first dopant metrology system configured to measure a first plurality of values of at least one parameter of a wafer, an ion implanter configured to implant a plurality of ions into the wafer, a second dopant metrology system configured to measure a second plurality of values of at least one parameter of the wafer following ion implantation of the wafer by the implanter, wherein the first dopant metrology system and the second dopant metrology system are communicatively coupled, an annealer configured to anneal the wafer following ion implantation, and a third dopant metrology system configured to measure a third plurality of values of at least one parameter of the wafer following annealing of the wafer by the annealer, wherein the second dopant metrology system and the third dopant metrology system are communicatively coupled.

Abstract: A modulated reflectance measurement system includes lasers for generating an intensity modulated pump beam and a UV probe beam. The pump and probe beams are focused on a measurement site within a sample. The pump beam periodically excites the measurement site and the modulation is imparted to the probe beam. For one embodiment, the wavelength of the probe beam is selected to correspond to a local maxima of the temperature reflectance coefficient of the sample. For a second embodiment, the probe laser is tuned to either minimize the thermal wave contribution to the probe beam modulation or to equalize the thermal and plasma wave contributions to the probe beam modulation.

Abstract: The present invention may include a first dopant metrology system configured to measure a first plurality of values of at least one parameter of a wafer, an ion implanter configured to implant a plurality of ions into the wafer, a second dopant metrology system configured to measure a second plurality of values of at least one parameter of the wafer following ion implantation of the wafer by the implanter, wherein the first dopant metrology system and the second dopant metrology system are communicatively coupled, an annealer configured to anneal the wafer following ion implantation, and a third dopant metrology system configured to measure a third plurality of values of at least one parameter of the wafer following annealing of the wafer by the annealer, wherein the second dopant metrology system and the third dopant metrology system are communicatively coupled.