downloadGroupGroupnoun_press release_995423_000000 copyGroupnoun_Feed_96767_000000Group 19noun_pictures_1817522_000000Group 19Group 19noun_Photo_2085192_000000 Copynoun_presentation_2096081_000000Group 19Group Copy 7noun_webinar_692730_000000Path
Skip to main content

August 18, 2020

SEMI Standards PV and PV Materials China Joint TC Chapter Spring Meeting 2020

This on-demand course, originally recorded on August 18, 2020, describes how machine learning and AI-based approaches to research, development, and production brings advantages to cleanroom processes.  AI-based identification of time-varying equipment performance, and the effects of the previous recipe used on the outcome of the current desired methods, are just some of the ways AI can be used to reduce the process variability.

Time

10:00 am - 12:00 pm

Add to Calendar 2020-08-18 10:00:00 2020-08-18 12:00:00 Flexible Electronics Master Class#2: AI in Mfg This on-demand course, originally recorded on August 18, 2020, describes how machine learning and AI-based approaches to research, development, and production brings advantages to cleanroom processes.  AI-based identification of time-varying equipment performance, and the effects of the previous recipe used on the outcome of the current desired methods, are just some of the ways AI can be used to reduce the process variability. On-Demand, United States SEMI.org contact@semi.org America/Vancouver public
Location

On-Demand,
United States

SEMI Standards PV and PV Materials China Joint TC Chapter Spring Meeting 2020

This course, originally recorded on August 18, 2020, describes how machine learning and AI-based approaches to research, development, and production brings advantages to cleanroom processes.  AI-based identification of time-varying equipment performance, and the effects of the previous recipe used on the outcome of the current desired methods, are just some of the ways AI can be used to reduce the process variability.

Cornell University has applied AI approaches to optimize lithography and etching processes involved in the development of an RF wake-up NEMS (Nano ElectroMechnical system) switch that needs a well-controlled gap between a moving shuttle and a contact. This presentation highlights that, as well as a decision tree based AI model for predicting lithography outcomes.

The AI work used as the basis for the course has been applied to plasma etching and the combined prediction of lithography and thin-film etching, using CD-SEM imagery for feature extraction and modeling process variables. Additional approaches to train process-modeling CAD tools to result in better process development experience have been developed.

The course provides a useful basis upon which to formulate AI strategies for all thin film manufacturing sites.

Featured Speakers

Bob Street PARC
Robert Street
Fellow, Palo Alto Research Center
Chris Ober
Chris Ober
Cornell University
Peter Doerschuk
Peter Doerschuk
Cornell University
Benyamin Davaji
Benyamin Davaji
Cornell University
Barry Bordonaro
Garry Bordonaro
Cornell NanoScale Facility
Jeremy Clark
Jeremy Clark
Cornell NanoScale Facility

Registration Includes: 

  • Access to the recorded session for use by the same person, multiple times
  • Additional attendees must register separately to view

Member Price:  $25

Non-Member Price:  $49

Non-Member Price for Government, Military, and Academia Non-Members:  $35

To receive this price, contact Michelle Fabiano at mfabiano@semi.org for discount code.  Must have a valid .gov, .mil or .edu email address

Students:  $0 

To receive this price, contact Gity Samadi at gsamadi@semi.org for discount code.  Must have a valid student ID card.