Applied Materials, Inc.

Dominic J Benvegnu

Boguslaw A Swedek

Jeffrey Drue David

Harry Q Lee

Jun Qian

Bogdan Swedek

Thomas H Osterheld

Lakshmanan Karuppiah

Wen-Chiang Tu

Ingemar Carlsson

Sivakumar Dhandapani

Benjamin Cherian

Abraham Ravid

David J Lischka

Jimin Zhang

Nicholas Alexander Wiswell

Nojan Motamedi

Zhihong Wang

Kiran Lall Shrestha

Zhize Zhu

Arash Alahgholipouromrani

Manoocher Birang

Feng Quan Liu

Kun Xu

Doyle E Bennett

Yuchun Wang

Nils Johansson

Wei Guang Lu

Hassan G Iravani

Chih Chung Chou

Gabriel Lorimer Miller

Zhefu Wang

Robert D Tolles

Andreas Norbert Wiswesser

Sidney P Huey

Kerry F Hughes

David Datong Huo

Jay Rohde

Xiaoyuan Hu

Martin A Josefowicz

Laksh Karuppiah

Ramiel Oshana

Steven M Zuniga

Sivapackia Ganapathiappan

Nag B Patibandla

Rajeev Bajaj

Jeonghoon Oh

Daniel Redfield

Sen-Hou Ko

Ankit Vora

Allen L D'Ambra

Boyi Fu

Ashwin Chockalingam

You Wang

Mayu Felicia Yamamura

Jagan Rangarajan

David Maxwell Gage

Garrett Ho Yee Sin

Mario Dagio Cornejo

Thomas Li

Paul V Miller

Ningzhuo Cui

Pierre Fontarensky

A method of classification of a film non-uniformity on a substrate includes obtaining a color image of a substrate with the color image comprising a plurality of color channels, obtaining a standard color for the color image of the substrate, for each respective pixel along a path in the color image determining a difference vector between the a color of the respective pixel and the standard color to generate a sequence of difference vectors, and sorting the pixels along the path into a plurality of regions including at least one normal region and at least one abnormal region based on the sequence of difference vectors, including comparing a multiplicity of the difference vectors in the sequence to a threshold.

Pixel classification of film non-uniformity based on processing of substrate images

A method of classification of a film non-uniformity on a substrate includes obtaining a color image of a substrate with the color image comprising a plurality of color channels, obtaining a standard color for the color image of the substrate, for each respective pixel along a path in the color image determining a difference vector between the a color of the respective pixel and the standard color to generate a sequence of difference vectors, sorting the pixels along the path into a plurality of regions including at least one normal region and at least one abnormal region based on the sequence of difference vectors, and classifying the at least one abnormal region as overpolished or underpolished based on at least one difference vector of a pixel at a boundary between the abnormal region and an adjacent normal region.

Region classification of film non-uniformity based on processing of substrate images

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.

Residue measurement from machine learning based processing of substrate images

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

A polishing pad includes a sensor assembly surrounded by a lower portion of the polishing pad, and an upper portion including a pad portion disposed on the assembly and at least a portion of a polishing layer disposed on the lower portion. The sensor assembly includes a lower body having a first pair of electrodes formed thereon, a polymer body having a second pair of electrodes formed thereon and aligned with the first pair of electrodes, and a pair of gaps between the first pair of electrodes and the second pair of electrodes.

Polishing system with capacitive shear sensor

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.

System using film thickness estimation from machine learning based processing of substrate images

A metrology system for obtaining a measurement representative of a thickness of a layer on a substrate includes a camera positioned to capture a color image of at least a portion of the substrate. A controller is configured to receive the color image from the camera, store a predetermined path in a coordinate space of at least two dimension including a first color channel and a second color channel, store a function that provides a value representative of a thickness as a function of a position on the predetermined path, determine a coordinate of a pixel in the coordinate space from color data in the color image for the pixel, determine a position of a point on the predetermined path that is closest to the coordinate of the pixel, and calculate a value representative of a thickness from the function and the position of the point on the predetermined path.

Thickness measurement of substrate using color metrology

A polishing apparatus includes a polishing station to hold a polishing pad, a carrier head to hold a substrate in contact with a polishing pad at the polishing station, a camera positioned to capture an image of a lower surface of a consumable part when the consumable part moves away from the polishing pad, and a controller configured to perform an image processing algorithm on the image to determine whether the consumable part is damaged. The consumable part can be a retaining ring on a carrier head, or a conditioner disk on a conditioner head.

Consumable part monitoring in chemical mechanical polisher

Film thickness estimation from machine learning based processing of substrate images

A layer thickness measurement system includes a support to hold a substrate, an optical sensor to capture a color image of at least a portion of the substrate, and a controller. The controller is configured to receive the color image from the optical sensor, perform a color correction on the color image to generate an adjusted color image having increased color contrast, determine a coordinate of the pixel in a coordinate space of at least two dimensions including a first color channel and a second color channel from color data in the adjusted color image for each of the adjusted color image, and calculate a value representative of a thickness based on the coordinate of the pixel of the adjusted color image in the coordinate space.

A metrology technique for analyzing a substrate includes storing data indicating a boundary of an area in a 2-dimensional color space having a pair of color channels including a first color channel and a second color channel as axes of the color space, receiving color data of a substrate from a camera, generating a color image of the substrate from the color data, performing a comparison of a pair of color values for the pair of color channels for the pixel to the boundary of the area in the 2-dimensional color space for each pixel of a plurality of pixels of the color image to determine whether the pair of color values meet thresholds provided by the boundary, and generating a signal to an operator based on results of the comparison for the plurality of pixels.

Color imaging for CMP monitoring

A method of detecting a polishing endpoint includes storing a plurality of library spectra, measuring a sequence of spectra from the substrate in-situ during polishing, and for each measured spectrum of the sequence of spectra, finding a best matching library spectrum from the plurality of library spectra to generate a sequence of best matching library spectra. Each library spectrum has a stored associated value representing a degree of progress through a polishing process, and the stored associated value for the best matching library spectrum is determined for each best matching library spectrum to generate a sequence of values representing a progression of polishing of the substrate. The sequence of values is compared to a target value, and a polishing endpoint is triggered when the sequence of values reaches the target value.

Endpoint detection for chemical mechanical polishing based on spectrometry

A chemical mechanical polishing apparatus includes a platen to support a polishing pad, the platen having a recess, a flexible membrane in the recess, and an in-situ vibration monitoring system to generate a signal. The in-situ acoustic monitoring system includes a vibration sensor supported by the flexible membrane and positioned to couple to an underside of the polishing pad.

Monitoring of vibrations during chemical mechanical polishing

A substrate cleaning system includes a cleaner module to clean a substrate after polishing of the substrate, a drier module to dry the substrate after cleaning by the cleaner module, a substrate support movable along a first axis from a first position in the drier module to a second position outside the drier module, and an in-line metrology station including a line-scan camera positioned to scan the substrate as the substrate is held by the substrate support and the substrate support is between the first position to the second position. The first axis is substantially parallel to a face of the substrate as held in by the substrate support.

Imaging for monitoring thickness in a substrate cleaning system

Growing community of inventors

La Honda, CA, United States of America

Dominic J Benvegnu