Applied Materials, Inc.

Charles Hardy

Benjamin Schwarz

James P Cruse

Abhinav Kumar

Dermot Cantwell

Kenneth S Collins

Andrew Nguyen

Ming Xu

Evans Yip Lee

Zhifeng Sui

Michael Robert Rice

Michael D Willwerth

Leonard Michael Tedeschi

Roger Alan Lindley

Yaoling Pan

Patrick John Tae

Thorsten Kril

Mikhail Taraboukhine

Kiyki-Shiy N Shang

Sivasankar Nagarajan

Adauto Diaz, Jr

Eu Jin Lim

A method includes determining a plurality of features of a first original image of a first product that are expected to be different for one or more products to be produced via manufacturing parameters of a manufacturing process compared to the first product. The method further includes adjusting one or more of the plurality of features of the first original image to generate a first synthetic image. The method further includes providing a plurality of images including the first original image and the first synthetic image to train a machine learning model to generate a trained machine learning model configured to generate output associated with updating the manufacturing parameters of the manufacturing process.

Adaptive non-rigid targeted deformation of images for synthetic image generation

Capacitive sensors and capacitive sensing data integration for plasma chamber condition monitoring are described. In an example, a plasma chamber monitoring system includes a plurality of capacitive sensors, a capacitance digital converter, and an applied process server coupled to the capacitance digital converter, the applied process server including a system software. The capacitance digital converter includes an isolation interface coupled to the plurality of capacitive sensors, a power supply coupled to the isolation interface, a field-programmable gate-array firmware coupled to the isolation interface, and an application-specific integrated circuit coupled to the field-programmable gate-array firmware.

Capacitive sensing data integration for plasma chamber condition monitoring

A method includes providing attributes of a manufacturing process and an image of a product associated with the manufacturing process to a trained machine learning model. The method further includes obtaining, from the trained machine learning model, predictive data. The method further includes determining, based on the predictive data, image measurements of the image of the product associated with the manufacturing process. Manufacturing parameters of the manufacturing process are to be updated based on the image measurements.

Automated image measurement for process development and optimization

Methods, systems, and non-transitory computer readable medium are described for automated image measurement for process development and optimization. A method includes receiving an image of a product associated with a manufacturing process; determining, using a trained machine learning model, an image classification for the image; selecting, based on the image classification, one or more image processing algorithms for the image; pre-processing the image based on at least one of the one or more image processing algorithms to generate an enhanced image; measuring, using a first image processing algorithm of the one or more image processing algorithms, one or more attributes of the enhanced image to determine image measurements; and reporting the image measurements. The manufacturing parameters of the manufacturing process are to be updated based on the image measurements.

Methods, systems, and non-transitory computer readable medium are described for automated image measurement for process development and optimization. A method includes receiving original images including a first original image. Each original image is of a corresponding product associated with a manufacturing process. The method further includes performing, based on process information about the manufacturing process, feature extraction to identify features of the first original image that are expected to change based on manufacturing parameters of the manufacturing process. The method further includes generating a first synthetic image of synthetic images by performing targeted deformation of one or more of the features of the first original image. The method further includes using the original images and the synthetic images to train a machine learning model to automatically determine an image processing algorithm to use for measuring one or more attributes of images of product associated with the manufacturing process.

Methods for processing substrates in twin chamber processing systems having first and second process chambers and shared processing resources are provided herein. In some embodiments, a method may include providing a substrate to the first process chamber of the twin chamber processing system, wherein the first process chamber has a first processing volume that is independent from a second processing volume of the second process chamber; providing one or more processing resources from the shared processing resources to only the first processing volume of the first process chamber; and performing a process on the substrate in the first process chamber.

Methods for processing substrates in process systems having shared resources

Methods and apparatus for automated validation of semiconductor process steps are provided herein. In some examples, a method for validating a semiconductor process recipe includes: selecting a rule set describing an operating window for a semiconductor process tool; checking parameter values defined by steps in the semiconductor process recipe against limit-checking rules of the rule set to produce first results; determining step types from the steps in the semiconductor process recipe using step definition rules of the rule set to produce second results; checking transitions between the step types against step transition rules of the rule set to produce third results; and generating, using the computer, validation data for use of the semiconductor process recipe with the semiconductor process tool based on the first, the second, and the third results.

Method and apparatus for automated validation of semiconductor process recipes

Methods for processing substrates in twin chamber processing systems having first and second process chambers and shared processing resources are provided herein. In some embodiments, a method may include flowing a process gas from a shared gas panel to a processing volume of the first process chamber and to a processing volume of the second process chamber; forming a first plasma in the first processing volume to process the first substrate and a second plasma to process the second substrate; monitoring the first processing volume and the second processing volume to determine if a process endpoint is reached in either volume; and either terminating the first and second plasma simultaneously when a first endpoint is reached; or terminating the first plasma when a first endpoint is reached in the first processing volume while continuing to provide the second plasma in the second processing volume until a second endpoint is reached.

Methods for monitoring processing equipment are provided herein. In some embodiments, a method for monitoring processing equipment when in an idle state for a period of idle time may include selecting a test from a list of a plurality of tests to perform on the processing equipment when the processing equipment is in the idle state, wherein the test has a total run time; starting the selected test; comparing a remaining idle time of the period of idle time to a remaining run time of the total run time as the selected test is performed; and determining whether to end the selected test prior to completing the total run time in response to the comparison.

Methods for monitoring processing equipment

Methods for processing substrates in dual chamber processing systems comprising first and second process chambers that share resources may include performing a first internal chamber clean in each of the first process chamber and the second process chamber; and subsequently processing a substrate in one of the first process chamber or the second process chamber by: providing a substrate to one of the first process chamber or the second process chamber; providing a process gas to the first process chamber and the second process chamber; forming a plasma in only the one of the first process chamber or the second process chamber having the substrate contained therein; and providing an inert gas to the first process chamber and the second process chamber via one or more channels formed in a surface of respective substrate supports disposed in the first process chamber and the second process chamber while processing the substrate.

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San Jose, CA, United States of America

Charles Hardy