Abstract
We propose the use of machine learning based analytics to simplify OPC (Optical Proximity Correction) model building process which demands concurrent optimization of more than 70 parameters as nodes shrink. We first built a deep neural network architecture to predict the RMS error, for a given set of model parameters. The neural network was trained on existing OPC model parameters and corresponding output RMS data of simulations to achieve an accurate prediction of output RMS for given set of OPC model parameters. Later, a sensitivity analysis-based methodology for recursive partitioning of OPC modelling parameters was employed to reduce the total search space of OPC model simulations. This resulted in reduction of the number of OPC model iterations performed during model tuning by orders of magnitude.
Original language | English |
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Title of host publication | Design-Process-Technology Co-optimization XV |
Editors | Chi-Min Yuan, Ryoung-Han Kim |
Publisher | SPIE |
ISBN (Electronic) | 9781510640610 |
DOIs | |
Publication status | Published - 2021 |
Event | Design-Process-Technology Co-optimization XV 2021 - Virtual, Online, United States Duration: 2021 Feb 22 → 2021 Feb 26 |
Publication series
Name | Proceedings of SPIE - The International Society for Optical Engineering |
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Volume | 11614 |
ISSN (Print) | 0277-786X |
ISSN (Electronic) | 1996-756X |
Conference
Conference | Design-Process-Technology Co-optimization XV 2021 |
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Country/Territory | United States |
City | Virtual, Online |
Period | 21/2/22 → 21/2/26 |
Bibliographical note
Publisher Copyright:© 2021 SPIE.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering