Abstract: In one implementation, a method of monitoring film thickness on a substrate, comprises: generating light from a light source; collimating the light from the light source to form a collimated beam; reflecting the collimated beam off of a surface to be measured to produce a reflected beam; splitting the reflected beam with a dichroic mirror, wherein the reflected beam splits into a first beam and a second beam; receiving, by a pyrometer, the first beam from the dichroic mirror; receiving, by a spectrometer, the second beam from the dichroic mirror; and analyzing data derived from the pyrometer and the spectrometer to determine one or more characteristics of the surface to be measured.

Abstract: An apparatus for determining a characteristic of a photoluminescent (PL) layer comprises: a light source that generates an excitation light that includes light from the visible or near-visible spectrum; an optical assembly configured to direct the excitation light onto a PL layer; a detector that is configured to receive a PL emission generated by the PL layer in response to the excitation light interacting with the PL layer and generate a signal based on the PL emission; and a computing device coupled to the detector and configured to receive the signal from the detector and determine a characteristic of the PL layer based on the signal.

Abstract: A method includes receiving, by a processing device, first data generated by a first sensor of a substrate processing system. The first data is generated responsive to the first sensor receiving electromagnetic radiation from a substrate held by a robot arm of a transfer chamber in the substrate processing system. The method further includes processing the first data to obtain second data. The second data includes a first indication of performance of the substrate processing system. The method further includes causing, in view of the second data, performance of a corrective actions associated with the substrate processing system.

Abstract: Methods and systems for monitoring etch or deposition processes using image-based in-situ process monitoring techniques include illuminating a measurement area on a sample disposed in a process chamber. The measurement area is illuminated using an input beam generated remote from the process chamber and transmitted to a first viewing window of the process chamber by a first optical fiber. Portions of the first input beam reflected from the measurement area are transmitted from the first viewing window to an imaging sensor by a second optical fiber. A sequence of images is obtained at the imaging sensor, and a change in reflectance of pixels within each of the images is determined. The etch or deposition process is monitored based on the change in reflectance.

Abstract: A method of processing a substrate according to a PECVD process is described. Temperature profile of the substrate is adjusted to change deposition rate profile across the substrate. Plasma density profile is adjusted to change deposition rate profile across the substrate. Chamber surfaces exposed to the plasma are heated to improve plasma density uniformity and reduce formation of low quality deposits on chamber surfaces. In situ metrology may be used to monitor progress of a deposition process and trigger control actions involving substrate temperature profile, plasma density profile, pressure, temperature, and flow of reactants.

Abstract: Methods and systems for detection of an endpoint of a substrate process are provided. A set of machine learning models are trained to provide a metrology measurement value associated with a particular type of metrology measurement for a substrate based on spectral data collected for the substrate. A respective machine learning model is selected to be applied to future spectral data collected during a future substrate process for a future substrate in view of a performance rating associated with the particular type of metrology measurement. Current spectral data is collected during a current process for a current substrate and provided as input to the respective machine learning model. An indication of a respective metrology measurement value corresponding to the current substrate is extracted from one or more outputs of the trained machine learning model.

Abstract: Embodiments disclosed herein include a method of monitoring a photoresist deposition process. In an embodiment, the method comprises depositing a photoresist layer to a first thickness over a substrate, measuring a property of the photoresist layer with a first electromagnetic (EM) radiation source, depositing the photoresist layer to a second thickness over the substrate, and measuring the property of the photoresist layer with the first EM radiation source.

Abstract: An apparatus for determining a characteristic of a photoluminescent (PL) layer comprises: a light source that generates an excitation light that includes light from the visible or near-visible spectrum; an optical assembly configured to direct the excitation light onto a PL layer; a detector that is configured to receive a PL emission generated by the PL layer in response to the excitation light interacting with the PL layer and generate a signal based on the PL emission; and a computing device coupled to the detector and configured to receive the signal from the detector and determine a characteristic of the PL layer based on the signal.

Abstract: A method of processing a substrate according to a PECVD process is described. Temperature profile of the substrate is adjusted to change deposition rate profile across the substrate. Plasma density profile is adjusted to change deposition rate profile across the substrate. Chamber surfaces exposed to the plasma are heated to improve plasma density uniformity and reduce formation of low quality deposits on chamber surfaces. In situ metrology may be used to monitor progress of a deposition process and trigger control actions involving substrate temperature profile, plasma density profile, pressure, temperature, and flow of reactants.

Abstract: An apparatus for determining a characteristic of a photoluminescent (PL) layer comprises: a light source that generates an excitation light that includes light from the visible or near-visible spectrum; an optical assembly configured to direct the excitation light onto a PL layer; a detector that is configured to receive a PL emission generated by the PL layer in response to the excitation light interacting with the PL layer and generate a signal based on the PL emission; and a computing device coupled to the detector and configured to receive the signal from the detector and determine a characteristic of the PL layer based on the signal.

Abstract: A method of processing a substrate according to a PECVD process is described. Temperature profile of the substrate is adjusted to change deposition rate profile across the substrate. Plasma density profile is adjusted to change deposition rate profile across the substrate. Chamber surfaces exposed to the plasma are heated to improve plasma density uniformity and reduce formation of low quality deposits on chamber surfaces. In situ metrology may be used to monitor progress of a deposition process and trigger control actions involving substrate temperature profile, plasma density profile, pressure, temperature, and flow of reactants.

Abstract: Examples disclosed herein generally relate to systems and methods for detecting the size of a particle in a fluid. In one example, a system for imaging a particle includes a first imaging device. The first imaging device includes a lens and a digital detector. The system further includes a laser source. He laser source is configured to emit a first laser beam and a second laser beam. The digital detector is configured to accumulate a metric of an intensity of an accumulated light that passes through the lens. The accumulated light is scattered from the particle. The accumulated light includes light from the first laser beam and the second laser beam.

Abstract: A machine learning model trained to provide metrology measurements for a substrate is provided. Training data generated for a prior substrate processed according to a prior process is provided to train the model. The training data includes a training input including a subset of historical spectral data extracted from a normalized set of historical spectral data collected for the prior substrate during the prior process. The subset of historical spectral data includes an indication of historical spectral features associated with a particular type of metrology measurement. The training data also includes a training output including a historical metrology measurement obtained for the prior substrate, the historical metrology measurement associated with the particular type of metrology measurement. Spectral data is collected for a current substrate processed according to a current process.

Abstract: Methods and systems for detection of an endpoint of a substrate process are provided. A set of machine learning models are trained to provide a metrology measurement value associated with a particular type of metrology measurement for a substrate based on spectral data collected for the substrate. A respective machine learning model is selected to be applied to future spectral data collected during a future substrate process for a future substrate in view of a performance rating associated with the particular type of metrology measurement. Current spectral data is collected during a current process for a current substrate and provided as input to the respective machine learning model. An indication of a respective metrology measurement value corresponding to the current substrate is extracted from one or more outputs of the trained machine learning model.

Abstract: Examples disclosed herein generally relate to systems and methods for detecting the size of a particle in a fluid. In one example, a system for imaging a particle includes a first imaging device. The first imaging device includes a lens and a digital detector. The system further includes a laser source. He laser source is configured to emit a first laser beam and a second laser beam. The digital detector is configured to accumulate a metric of an intensity of an accumulated light that passes through the lens. The accumulated light is scattered from the particle. The accumulated light includes light from the first laser beam and the second laser beam.

Abstract: Full wafer in-situ metrology chambers and methods of use are described. The metrology chambers include a substrate support and a sensor bar that are rotatable relative to each other. The sensor bar includes a plurality of sensors at different radii from a central axis.

Abstract: Examples disclosed herein generally relate to systems and methods for detecting the size of a particle in a fluid. In one example, a system for imaging a particle includes a first imaging device. The first imaging device includes a lens and a digital detector. The system further includes a laser source. He laser source is configured to emit a first laser beam and a second laser beam. The digital detector is configured to accumulate a metric of an intensity of an accumulated light that passes through the lens. The accumulated light is scattered from the particle. The accumulated light includes light from the first laser beam and the second laser beam.

Abstract: Full wafer in-situ metrology chambers and methods of use are described. The metrology chambers include a substrate support and a sensor bar that are rotatable relative to each other. The sensor bar includes a plurality of sensors at different radii from a central axis.

Abstract: A system for processing a substrate is provided. The system includes a process chamber including one or more sidewalls enclosing a processing region; and a substrate support. The system further includes a passageway connected to the process chamber; and a first particle detector disposed at a first location along the passageway. The first particle detector includes an energy source configured to emit a first beam; one or more optical devices configured to direct the first beam along one or more paths, where the one or more paths extend through at least a portion of the passageway. The first particle detector further includes a first energy detector disposed at a location other than on the one or more paths. The system further includes a controller configured to communicate with the first particle detector, wherein the controller is configured to identify a fault based on signals received from the first particle detector.

Abstract: An apparatus for determining a characteristic of a photoluminescent (PL) layer comprises: a light source that generates an excitation light that includes light from the visible or near-visible spectrum; an optical assembly configured to direct the excitation light onto a PL layer; a detector that is configured to receive a PL emission generated by the PL layer in response to the excitation light interacting with the PL layer and generate a signal based on the PL emission; and a computing device coupled to the detector and configured to receive the signal from the detector and determine a characteristic of the PL layer based on the signal.