Abstract: A method of training a neural network includes obtaining two ground truth thickness profiles a test substrate, obtaining two thickness profiles for the test substrate as measured by an in-situ monitoring system while the test substrate is on polishing pads of different thicknesses, generating an estimated thickness profile for another thickness value that is between the two thickness values by interpolating between the two profiles, and training a neural network using the estimated thickness profile.

Abstract: Monitoring operations of a polishing system includes obtaining a time-based sequence of reference images of a component of the polishing system performing operations during a test operation of the polishing system, receiving from a camera a time-based sequence of monitoring images of an equivalent component of an equivalent polishing system performing operations during polishing of a substrate, determining a difference value for the time-based sequence of monitoring images by comparing the time-based sequence of reference images to the time-based sequence of monitoring image using an image processing algorithm, determining whether the difference value exceeds a threshold, and in response to determining the difference value exceeds the threshold, indicating an excursion.

Abstract: A method of polishing a substrate includes polishing a conductive layer on the substrate at a polishing station, monitoring the layer with an in-situ eddy current monitoring system to generate a plurality of measured signals values for a plurality of different locations on the layer, generating thickness measurements the locations, and detecting a polishing endpoint or modifying a polishing parameter based on the thickness measurements. The conductive layer is formed of a first material having a first conductivity. Generating includes calculating initial thickness values based on the plurality of measured signals values and processing the initial thickness values through a neural network that was trained using training data acquired by measuring calibration substrates having a conductive layer formed of a second material having a second conductivity that is lower than the first conductivity to generated adjusted thickness values.

Abstract: A method of generating training spectra for training of a neural network includes measuring a first plurality of training spectra from one or more sample substrates, measuring a characterizing value for each training spectra of the plurality of training spectra to generate a plurality of characterizing values with each training spectrum having an associated characterizing value, measuring a plurality of dummy spectra during processing of one or more dummy substrates, and generating a second plurality of training spectra by combining the first plurality of training spectra and the plurality of dummy spectra, there being a greater number of spectra in the second plurality of training spectra than in the first plurality of training spectra. Each training spectrum of the second plurality of training spectra having an associated characterizing value.

Abstract: Operating a substrate processing system includes receiving a plurality of sets of training data, storing a plurality of machine learning models, storing a plurality of physical process models, receiving a selection of a machine learning model from the plurality of machine learning models and a selection of a physical process model from the plurality of physical process models, generating an implemented machine learning model according to the selected machine learning model, calculating a characterizing value for each training spectrum in each set of training data thereby generating a plurality of training characterizing values with each training characterizing value associated with one of the plurality of training spectra, training the implemented machine learning model using the plurality of training characterizing values and plurality of training spectra to generate a trained machine learning model, and passing the trained machine learning model to a control system of the substrate processing system.

Abstract: An apparatus for chemical mechanical polishing includes a platen having a surface to support a polishing pad, a carrier head to hold a substrate against a polishing surface of the polishing pad, a pad conditioner to press an abrasive body against the polishing surface, an in-situ polishing pad monitoring system including an imager disposed above the platen to capture an image of the polishing pad, and a controller configured to receive the image from the monitoring system and generate a measure of polishing pad surface roughness based on the image. The controller can use machine-learning based image processing to generate the measure of surface roughness.

Abstract: Monitoring operations of a polishing system includes obtaining a time-based sequence of reference images of a component of the polishing system performing operations during a test operation of the polishing system, receiving from a camera a time-based sequence of monitoring images of an equivalent component of an equivalent polishing system performing operations during polishing of a substrate, determining a difference value for the time-based sequence of monitoring images by comparing the time-based sequence of reference images to the time-based sequence of monitoring image using an image processing algorithm, determining whether the difference value exceeds a threshold, and in response to determining the difference value exceeds the threshold, indicating an excursion.

Abstract: A chemical mechanical polishing apparatus includes a rotatable platen to hold a polishing pad, a rotatable carrier to hold a substrate against a polishing surface of the polishing pad during a polishing process, a polishing liquid supply port to supply a polishing liquid to the polishing surface, a thermal control system including a movable nozzle to spray a medium onto the polishing surface to adjust a temperature of a zone on the polishing surface, an actuator to move the nozzle radially relative to an axis of rotation of the platen, and a controller configured to coordinate dispensing of the medium from the nozzle with motion of the nozzle across the polishing surface.

Abstract: During chemical mechanical polishing of a substrate, a signal value that depends on a thickness of a layer in a measurement spot on a substrate undergoing polishing is determined by a first in-situ monitoring system. An image of at least the measurement spot of the substrate is generated by a second in-situ imaging system. Machine vision processing, e.g., a convolutional neural network, is used to determine a characterizing value for the measurement spot based on the image. Then a measurement value is calculated based on both the characterizing value and the signal value.

Abstract: A method of polishing a substrate includes polishing a conductive layer on the substrate at a polishing station, monitoring the layer with an in-situ eddy current monitoring system to generate a plurality of measured signals values for a plurality of different locations on the layer, generating thickness measurements the locations, and detecting a polishing endpoint or modifying a polishing parameter based on the thickness measurements. The conductive layer is formed of a first material having a first conductivity. Generating includes calculating initial thickness values based on the plurality of measured signals values and processing the initial thickness values through a neural network that was trained using training data acquired by measuring calibration substrates having a conductive layer formed of a second material having a second conductivity that is lower than the first conductivity to generated adjusted thickness values.

Abstract: A method of generating training spectra for training of a neural network includes measuring a first plurality of training spectra from one or more sample substrates, measuring a characterizing value for each training spectra of the plurality of training spectra to generate a plurality of characterizing values with each training spectrum having an associated characterizing value, measuring a plurality of dummy spectra during processing of one or more dummy substrates, and generating a second plurality of training spectra by combining the first plurality of training spectra and the plurality of dummy spectra, there being a greater number of spectra in the second plurality of training spectra than in the first plurality of training spectra. Each training spectrum of the second plurality of training spectra having an associated characterizing value.

Abstract: During chemical mechanical polishing of a substrate, a signal value that depends on a thickness of a layer in a measurement spot on a substrate undergoing polishing is determined by a first in-situ monitoring system. An image of at least the measurement spot of the substrate is generated by a second in-situ imaging system. Machine vision processing, e.g., a convolutional neural network, is used to determine a characterizing value for the measurement spot based on the image. Then a measurement value is calculated based on both the characterizing value and the signal value.

Abstract: A chemical mechanical polishing apparatus includes a platen to support a polishing pad, a carrier head to hold a surface of a substrate against the polishing pad, a motor to generate relative motion between the platen and the carrier head so as to polish an overlying layer on the substrate, an in-situ acoustic monitoring system comprising an acoustic window having a top surface to contact the substrate, and a controller configured to detect a polishing endpoint based on received acoustic signals from the in-situ acoustic monitoring system.

Abstract: A polishing pad includes a polishing layer, a backing layer, and an acoustic window extending through the backing layer and the polishing layer, wherein an upper surface of the acoustic window is coplanar with a polishing surface of the polishing layer.

Abstract: A polishing pad includes a polishing layer, a backing layer, and an acoustic window of solid material having an acoustic impedance less than that of the backing layer and extending through the backing layer to contact the bottom surface of the polishing layer.

Abstract: A chemical mechanical polishing apparatus includes a platen, a polishing pad supported on the platen, a carrier head to hold a surface of a substrate against a polishing surface of the polishing pad, and a motor to generate relative motion between the platen and the carrier head so as to polish an overlying layer of the substrate. The polishing pad includes a polishing layer of a solid matrix material with liquid-filled pores, and a backing layer. An in-situ acoustic monitoring system includes an acoustic sensor coupled to the backing layer to receive acoustic signals from the substrate, and a controller is configured to detect a polishing transition point based on received acoustic signals from the in-situ acoustic monitoring system.

Abstract: A chemical mechanical polishing apparatus includes a platen, a polishing pad supported on the platen, a carrier head to hold a surface of a substrate against the polishing pad, a motor to generate relative motion between the platen and the carrier head so as to polish an overlying layer on the substrate, an in-situ acoustic monitoring system comprising an acoustic sensor that receives acoustic signals from the surface of the substrate, and a controller configured to detect planarization of topology on the substrate based on a signal from the in-situ acoustic monitoring system.

Abstract: Determining a thickness of a layer on a wafer during a semiconductor process may include executing the process on the layer on the wafer; monitoring the wafer during the process with an in-situ spectrographic monitoring system to generate spectral data reflected from the wafer; applying a bandpass filter operation to the spectral data to generate filtered spectral data, where the bandpass filter may be configured to pass a frequency range corresponding to the layer on the wafer; and matching the filtered spectral data to a reference filtered spectral data, where the reference filtered spectral data may have been filtered using the bandpass filter operation, and the reference filtered spectral data may be associated with a thickness of the layer.

Abstract: A method of generating training spectra for training of a neural network includes generating a plurality of theoretically generated initial spectra from an optical model, sending the plurality of theoretically generated initial spectra to a feedforward neural network to generate a plurality of modified theoretically generated spectra, sending an output of the feedforward neural network and empirically collected spectra to a discriminatory convolutional neural network, determining that the discriminatory convolutional neural network does not discriminate between the modified theoretically generated spectra and empirically collected spectra, and thereafter, generating a plurality of training spectra from the feedforward neural network.

Abstract: A chemical mechanical polishing apparatus includes a platen having a top surface to hold a polishing pad, a carrier head to hold a substrate against a polishing surface of the polishing pad during a polishing process, a temperature monitoring system including a non-contact thermal imaging camera positioned above the platen to have a field of view of a portion of the polishing pad on the platen, and a controller. The controller is configured to receive the thermal image from the temperature monitoring system, input the thermal image into a machine learning model trained by training examples to determine an indication for one or more of 1) a presence of a process excursion, 2) a substrate state, or 3) a diagnosis for the process excursion, and receive from the machine learning model the indication.