Abstract:
A method of controlling polishing includes storing a base spectrum, the base spectrum being a spectrum of light reflected from a substrate after deposition of a deposited dielectric layers overlying a metallic layer or semiconductor wafer and before deposition of a non-metallic layer over the plurality of deposited dielectric layer. After deposition of the non-metallic layer and during polishing of the non-metallic layer on the substrate, measurements of a sequence of raw spectra of light reflected the substrate during polishing are received from an in-situ optical monitoring system. Each raw spectrum is normalized to generate a sequence of normalized spectra using the raw spectrum and the base spectrum. At least one of a polishing endpoint or an adjustment for a polishing rate is determined based on at least one normalized predetermined spectrum from the sequence of normalized spectra.
Abstract:
A method of controlling a polishing operation includes measuring a plurality of spectra at a plurality of different positions on a substrate to provide a plurality of measured spectra. For each measured spectrum of the plurality of measured spectra, a characterizing value is generated based on the measured spectrum. For each characterizing value, a goodness of fit of the measured spectrum to another spectrum used in generating the characterizing value is determined. A wafer-level characterizing value map is generated by applying a regression to the plurality of characterizing values with the plurality of goodnesses of fit used as weighting factors in the regression. A polishing endpoint or a polishing parameter of the polishing apparatus is adjusted based on the wafer-level characterizing map, and the substrate or a subsequent substrate is polished in the polishing apparatus with the adjusted polishing endpoint or polishing parameter.
Abstract:
A method of controlling a polishing operation includes measuring a plurality of spectra at a plurality of different positions on a substrate to provide a plurality of measured spectra. For each measured spectrum of the plurality of measured spectra, a characterizing value is generated based on the measured spectrum. For each characterizing value, a goodness of fit of the measured spectrum to another spectrum used in generating the characterizing value is determined. A wafer-level characterizing value map is generated by applying a regression to the plurality of characterizing values with the plurality of goodnesses of fit used as weighting factors in the regression. A polishing endpoint or a polishing parameter of the polishing apparatus is adjusted based on the wafer-level characterizing map, and the substrate or a subsequent substrate is polished in the polishing apparatus with the adjusted polishing endpoint or polishing parameter.
Abstract:
A substrate having a plurality of zones is polished and spectra are measured. For each zone, a first linear function fits a sequence of index values associated with reference spectra that best match the measured spectra. A projected time at which a reference zone will reach the target index value is determined based on the first linear function, and for at least one adjustable zone, a polishing parameter adjustment is calculated such that the adjustable zone has closer to the target index at the projected time than without such adjustment. The adjustment is calculated based on a feedback error calculated for a previous substrate. The feedback error for a subsequent substrate is calculated based on a second linear function that fits a sequence of index values associated with reference spectra that best match spectra measured after the polishing parameter is adjusted.
Abstract:
A neural network is trained for use in a substrate residue classification system by obtaining ground truth residue level measurements of a top layer of a calibration substrate at a plurality of locations, each location at a defined position for a die being fabricated on the substrate. A plurality of color images of the calibration substrate are obtained, each color image corresponding to a region for a die being fabricated on the substrate. A neural network is trained to convert color images of die regions from an in-line substrate imager to residue level measurements for the top layer in the die region.
Abstract:
Disclosed herein is a chemical mechanical polishing apparatus, comprising 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 including an acoustic sensor that receives acoustic energy from the substrate and the polishing pad; and a controller configured to detect an abnormal acoustic event based on measurements from the in-situ acoustic monitoring system, and determine a type of anomaly based on signals measured by the in-situ acoustic monitoring system during the abnormal acoustic event.
Abstract:
A method of generating a matrix to relate a plurality of controllable parameters of a chemical mechanical polishing system to a polishing rate profile includes polishing a test substrate. The test substrate is polished for a first period of time using baseline parameter values with a first parameter set to a first value, and the test substrate is polished for a second period of time using first modified parameter values with the first parameter set to a modified second value. A thickness of the test substrate is monitored during polishing, and a baseline polishing rate profile is determined for the first period of time and a first modified polishing rate profile is determined for the second period of time. The matrix is calculated based on the baseline parameter values, the first modified parameters, the baseline polishing rate profile and the first modified polishing rate profile.
Abstract:
A neural network is trained for use in a substrate residue classification system by obtaining ground truth residue level measurements of a top layer of a calibration substrate at a plurality of locations, each location at a defined position for a die being fabricated on the substrate. A plurality of color images of the calibration substrate are obtained, each color image corresponding to a region for a die being fabricated on the substrate. A neural network is trained to convert color images of die regions from an in-line substrate imager to residue level measurements for the top layer in the die region.
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 neural network is trained for use in a substrate thickness measurement system by obtaining ground truth thickness measurements of a top layer of a calibration substrate at a plurality of locations, each location at a defined position for a die being fabricated on the substrate. A plurality of color images of the calibration substrate are obtained, each color image corresponding to a region for a die being fabricated on the substrate. A neural network is trained to convert color images of die regions from an in-line substrate imager to thickness measurements for the top layer in the die region. The training is performed using training data that includes the plurality of color images and ground truth thickness measurements with each respective color image paired with a ground truth thickness measurement for the die region associated with the respective color image.