Machine learning approach to thickness prediction from in situ spectroscopic ellipsometry data for atomic layer deposition processes-Reference-Cited by-同舟云学术

Machine learning approach to thickness prediction from in situ spectroscopic ellipsometry data for atomic layer deposition processes

Author:

Arunachalam Ayush¹,Berriel S. Novia²,Feit Corbin²,Kumar Udit²³,Seal Sudipta²³⁴⁵⁶^ORCID,Basu Kanad¹,Banerjee Parag²⁴⁷⁸^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, Texas 75080

2. Department of Materials Science and Engineering, University of Central Florida, Orlando, Florida 32816

3. Advanced Materials Processing and Analysis Center, University of Central Florida, Orlando, Florida 32816

4. Nano Science and Technology Center, University of Central Florida, Orlando, Florida 32816

5. Biionix Faculty Cluster, College of Medicine, University of Central Florida, Orlando, Florida 32816

6. College of Medicine, University of Central Florida, Orlando, Florida 32827

7. Florida Solar Energy Center, University of Central Florida, Orlando, Florida 32816

8. REACT Faculty Cluster, University of Central Florida, Orlando, Florida 32816

Funder

Semiconductor Research Corporation

Directorate for Engineering

Publisher

American Vacuum Society

Subject

Surfaces, Coatings and Films,Surfaces and Interfaces,Condensed Matter Physics

Link

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