Physics-Informed Deep Learning for Plasma Etch Optimization

Modeling the plasma etch process is highly valuable in the field of semiconductor manufacturing. This intricate process relies on the execution of numerous individual processes, numbering in the hundreds to thousands. The application of artificial intelligence (AI) and machine learning (ML) techniques in optimizing these steps presents a revolutionary prospect for the industry. In this thesis, we share our findings for plasma etch recipe optimization using AI and ML. We present a series of plasma etch datasets generated from ViennaPS, as well as a new data augmentation algorithm for generating a uniform spatial and temporal distribution of data points. Our results on this dataset demonstrate improved model generalization using physically-informed neural networks (PINNs) with a level-set based loss. We highlight performance metrics for Soft-Adapt learned physics loss weights, as well as statically chosen weights. Future work includes utilizing the PINN model in a Bayesian framework to facilitate recipe optimization for the desired etch profile.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Dighamber, Mohit

Advisor dc:contributor.advisor

• Boning, Duane S.

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/157256

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/157256