Applied Materials, Inc.

Applied Materials Israel Limited

Applied Materials Italia S.r.l.

Todd J Egan

Edward W Budiarto

Mehdi Vaez-Iravani

Wei-Sheng Lei

Brad Eaton

Ajay Kumar

Thomas E Nowak

Abraham Ravid

Terrance Y Lee

Jeongmin Lee

Ganesh Balasubramanian

Madhava Rao Yalamanchili

Sergey Starik

Juan Carlos Rocha-Alvarez

Guoheng Zhao

Bok Hoen Kim

Amit Kumar Bansal

Jianhua Zhou

Xinhai Han

Jian J Chen

Ramprakash Sankarakrishnan

Mohamad A Ayoub

James Matthew Holden

Nagarajan Rajagopalan

Zhijun Jiang

Michael Wenyoung Tsiang

Patrick Reilly

Masaki Ogata

Heung Lak Park

Shahid Shaikh

Dmitriy Panasyuk

Kyle Tantiwong

Michael Robert Rice

Robert Jan Visser

Mohammad Kamruzzaman Chowdhury

Dinesh Kabra

Saravjeet Singh

Samer Banna

Avishek Ghosh

Jeffrey C Hudgens

Nir Merry

Byung-Sung Kwak

Dmitry A Dzilno

Lei Lian

Gregory Kirk

Leon Volfovski

Aparna Iyer

Khokan Chandra Paul

Pengyu Han

Joseph Yudovsky

Majeed Foad

Dale Robert Du Bois

Ralf Hofmann

Yaoming Shen

Kalyanjit Ghosh

Byung Sung Kwak

Leonard Michael Tedeschi

Song-Moon Suh

Priyadarshi Panda

Prashant Aji

Richard Giljum

Venkatakaushik Voleti

Eli Mor

Yeishin Tung

Michael Xiaoxuan Yang

Vijay D Parkhe

Nag B Patibandla

Daniel J Woodruff

Suketu Arun Parikh

Yong Cao

Mingwei Zhu

Kevin Griffin

Seshadri Ramaswami

Damon Keith Cox

Zhepeng Cong

Wendell Glenn Boyd, Jr

Gopalakrishna B Prabhu

Weimin Zeng

Daniel Lee Diehl

David Masayuki Ishikawa

Michael Johnson

Matthew James Busche

Jacob Newman

Izya Kremerman

Robert W Batz, Jr

Zihao Yang

Shay Assaf

Paul J Steffas

Paul Dennis Connors

Tao Sheng

Alex Minkovich

Ingrid B Peterson

Edgar Genio

Roman M Mostovoy

Somesh Khandelwal

Zhengping Yao

Eran Weiss

Mitchell DiSanto

Kommisetti Subrahmanyam

Barry Craver

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.

In-situ reflectometry for real-time process control

Implementations disclosed describe, among other things, a system and a method of scanning a substrate with a beam of light and detecting for each of a set of locations of the substrate, a respective one of a set of intensity values associated with a beam of light reflected from (or transmitted through) the substrate. The detected intensity values are used to determine a profile of a thickness of the substrate.

System and method to map thickness variations of substrates in manufacturing systems

A cool cluster comprises one or more transfer chambers; a plurality of process chambers connected to the one or more transfer chambers; and a computing device of the tool cluster. The computing device is to receive first measurements generated by sensors of a first process chamber during or after a process is performed within the first process chamber; determine that the first process chamber is due for maintenance based on processing the first measurements using a first trained machine learning model; after maintenance has been performed on the first process chamber, receive second measurements generated by the sensors during or after a seasoning process is performed within the first process chamber; and determine that the first process chamber is ready to be brought back into service based on processing the second measurements using a second trained machine learning model.

Autonomous substrate processing system

An organic light-emitting diode (OLED) deposition system includes two deposition chambers, a transfer chamber between the two deposition chambers, a metrology system having one or more sensors to perform measurements of the workpiece within the transfer chamber, and a control system to cause the system to form an organic light-emitting diode layer stack on the workpiece. Vacuum is maintained around the workpiece while the workpiece is transferred between the two deposition chambers and while retaining the workpiece within the transfer chamber. The control system is configured to cause the two deposition chambers to deposit two layers of organic material onto the workpiece, and to receive a first plurality of measurements of the workpiece in the transfer chamber from the metrology system.

In-line monitoring of OLED layer thickness and dopant concentration

A method includes identifying first structure data of a first region of a substrate and receiving optical metrology data of the substrate associated with one or more substrate deposition processes in a processing chamber. The method further includes determining, based on the optical metrology data and the first structure data, a first growth rate of the first region of the substrate associated with the one or more substrate deposition processes. The method further includes predicting, based on the optical metrology data and the first growth rate, thickness data of a second region of the substrate without second structure data of the second region.

Spatial pattern loading measurement with imaging metrology

A system includes a radiation source configured to emit a radiation beam. The system further includes a first optical sensor configured to detect a first intensity of a first portion of the radiation beam reflected from a surface of an object. The system further includes a second optical sensor configured to detect a second intensity of a second portion of the radiation beam scattered by the surface of the object. The system further includes a processing device communicatively coupled to the first optical sensor and the second optical sensor. The processing device is configured to determine at least one of a roughness or an emissivity of the surface of the object based on a comparison of the first intensity and the second intensity.

Surface roughness and emissivity determination

An optical metrology model for in-line thickness measurements of a film overlying non-ideal structures on a substrate is generated by performing pre-measurements prior to deposition of the film and performing post-measurements after the deposition. The pre- and post-measurements are performed at at least one of multiple polarization angles or multiple orientations of the substrate. Differences in reflectance between the pre- and post-measurements are determined at the multiple polarization angles and the multiple orientations. At least one of the multiple polarization angles or the multiple orientations are identified where the differences in reflectance are indicative of a suppressed influence from the non-ideal structures. The optical metrology model is generated using the identified polarization angles and the identified orientations as inputs to a machine-learning algorithm.

Optical metrology models for in-line film thickness measurements

Implementations disclosed describe a system that includes a deposition chamber, a light source to produce an incident beam of light, wherein the incident beam of light is to illuminate a region of the deposition chamber, and a camera to collect a scattered light originating from the illuminated region of the deposition chamber, wherein the scattered light is to be produced upon interaction of the first incident beam of light with particles inside the illuminated region of the deposition chamber. The described system may optionally have a processing device, coupled to the camera, to generate scattering data for a plurality of locations of the illuminated region, wherein the scattering data for each location comprises intensity of the scattered light originating from this location.

Real-time detection of particulate matter during deposition chamber manufacturing

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.

Metrology for OLED manufacturing using photoluminescence spectroscopy

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.

Image-based in-situ process monitoring

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.

PECVD process

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. In response to a determination that the respective metrology measurement satisfies a metrology measurement criterion, an instruction including a command to terminate the current process is generated.

Substrate process endpoint detection using machine learning

A substrate processing system comprises one or more transfer chambers; a plurality of process chambers connected to the one or more transfer chambers; and a computing device connected to each of the plurality of process chambers. The computing device is to receive first measurements generated by sensors of a first process chamber during or after a process is performed within the first process chamber; determine that the first process chamber is due for maintenance based on processing the first measurements using a first trained machine learning model; after maintenance has been performed on the first process chamber, receive second measurements generated by the sensors during or after a seasoning process is performed within the first process chamber; and determine that the first process chamber is ready to be brought back into service based on processing the second measurements using a second trained machine learning model.

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Fremont, CA, United States of America

Todd J Egan