Applied Materials, Inc.

Benjamin Cherian

Jun Qian

Thomas H Osterheld

Harry Q Lee

Boguslaw A Swedek

Kun Xu

Dominic J Benvegnu

Sivakumar Dhandapani

Jeffrey Drue David

Doyle E Bennett

David Maxwell Gage

Kiran Lall Shrestha

Nicholas Alexander Wiswell

Thomas Li

Jianshe Tang

Charles C Garretson

Sidney P Huey

Graham Yennie

Brian J Brown

Shou-Sung Chang

Haosheng Wu

Shih-Haur Shen

Hassan G Iravani

Hari N Soundararajan

Huanbo Zhang

Eric Lau

Zhize Zhu

Denis Ivanov

Steven M Zuniga

Jeonghoon Oh

Hui W Chen

Gregory E Menk

Allen L D'Ambra

David Masayuki Ishikawa

Chih Chung Chou

Jagan Rangarajan

David J Lischka

Hui Chen

Chih Chung

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 sensor that receives acoustic signals from the surface of the substrate, and a controller configured to determine a angular orientation of the substrate based on received acoustic signals from the in-situ acoustic monitoring system.

Determining substrate orientation with acoustic signals

A method of polishing a layer on the substrate at a polishing station includes the actions of monitoring the layer during polishing at the polishing station with an in-situ monitoring system to generate a plurality of measured signals for a plurality of different locations on the layer; generating, for each location of the plurality of different locations, an estimated measure of thickness of the location, the generating including processing the plurality of measured signals through a neural network; and at least one of detecting a polishing endpoint or modifying a polishing parameter based on each estimated measure of thickness.

Polishing apparatus using neural network for monitoring

A method of polishing includes holding a substrate with a carrier head against a polishing surface of a polishing pad, generating relative motion between the substrate and polishing pad, applying a first pressure in a first cyclic waveform having a first frequency to a first region of the substrate, applying a second pressure in a second cyclic waveform having a different second frequency to a different second region of the substrate, during polishing of the substrate, monitoring the substrate with an in-situ motor torque monitoring system to generate a sequence of measured values, and determining a relative contribution to the sequence of measured values from the first region and second region based on distinguishing the first frequency from the second frequency.

Pressure signals with different frequencies during friction monitoring to provide spatial resolution

Controlling a polishing system includes receiving from an in-situ monitoring system, for each region of a plurality of regions on a substrate being processed by the polishing system, a sequence of characterizing values for the region. For each region, a polishing rate is determined for the region, and an adjustment is calculated for at least one processing parameter. Calculation of the adjustment includes minimizing a cost function that includes, for each region, i) a difference between a current characterizing value or an expected characterizing value at an expected endpoint time and a target characterizing value for the region, and ii) a plurality of a projected future pressure changes over time for the region and/or a plurality of differences between projected future pressures over time and a baseline pressure for the region.

Control of processing parameters during substrate polishing using cost function

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 sensor that receives acoustic signals from the surface of the substrate, and a controller configured to determine an angular orientation of the substrate based on received acoustic signals from the in-situ acoustic monitoring system.

Determining substrate precession with acoustic signals

Generating a recipe for a polishing process includes receiving a target removal profile that includes a target thickness to remove for a plurality of locations spaced angularly around a center of a substrate, storing a first function providing substrate orientation relative to the carrier head over time, storing a second function defining a polishing rate below a zone of the zone as a function of one or more pressures of one or more zones from a plurality of pressurizable zones of the carrier head that are spaced angularly around the center of the substrate, and for each particular zone of the plurality of zones, calculating a recipe defining a pressure for the particular zone over time. Calculating the recipe includes calculating an expected thickness profile after polishing from the second function defining the polishing rate and the first function providing substrate orientation relative to the zone over time, and applying a minimizing algorithm to reduce a difference between the expected thickness profile and the target thickness profile.

Control of processing parameters for substrate polishing with substrate precession

A method of polishing includes holding a substrate with a carrier head against a polishing surface of a polishing pad, generating relative motion between the substrate and polishing pad, applying a first pressure modulated by a first modulation function to a first region of the substrate, applying a second pressure modulated by a second modulation function that is orthogonal to the first modulation function to a second region of the substrate, during polishing of the substrate monitoring the substrate with an in-situ friction monitoring system to generate a sequence of measured values, and determining a relative contribution to the sequence of measured value from the first region and second region based on distinguishing the first frequency from the second frequency.

Pressure signals during motor torque monitoring to provide spatial resolution

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.

Machine vision as input to a CMP process control algorithm

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.

Training a machine learning system to detect an excursion of a CMP component using time-based sequence of images

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.

Technique for training neural network for use in in-situ monitoring during polishing and polishing system

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.

Determination of substrate layer thickness with polishing pad wear compensation

Detecting an excursion of a CMP component using time-based sequence of images

Trained neural network in in-situ monitoring during polishing and polishing system

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.

Training spectrum generation for machine learning system for spectrographic monitoring

Controlling a polishing system includes receiving from an in-situ monitoring system, for each region of a plurality of regions on a substrate being processed by the polishing system, a sequence of characterizing values for the region. For each region, a polishing rate is determined for the region, and an adjustment is calculated for at least one processing parameter. For each of a plurality of parameter update times, an adjustment is calculated for at least one processing parameter, wherein calculation of the adjustment for a particular parameter update time from the plurality of parameter update times includes calculation of expected future parameter changes for one or more future parameter update times subsequent to the particular parameter update time.

Control of processing parameters during substrate polishing using expected future parameter changes

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San Jose, CA, United States of America

Benjamin Cherian