Machine learning based recursive partitioning for simplifying OPC model building complexity

Apoorva Oak; Soobin Hwang; Ruoxia Chen; Shinill Kang; Ryan Ryoung Han Kim

doi:10.1117/12.2584704

Machine learning based recursive partitioning for simplifying OPC model building complexity

Apoorva Oak, Soobin Hwang, Ruoxia Chen, Shinill Kang, Ryan Ryoung Han Kim

Department of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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
Title of host publication	Design-Process-Technology Co-optimization XV
Editors	Chi-Min Yuan, Ryoung-Han Kim
Publisher	SPIE
ISBN (Electronic)	9781510640610
DOIs	https://doi.org/10.1117/12.2584704
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
Volume	11614
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	Design-Process-Technology Co-optimization XV 2021
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

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10.1117/12.2584704

Cite this

Oak, A., Hwang, S., Chen, R., Kang, S., & Kim, R. R. H. (2021). Machine learning based recursive partitioning for simplifying OPC model building complexity. In C-M. Yuan, & R-H. Kim (Eds.), Design-Process-Technology Co-optimization XV [116140O] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11614). SPIE. https://doi.org/10.1117/12.2584704

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title = "Machine learning based recursive partitioning for simplifying OPC model building complexity",

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. ",

author = "Apoorva Oak and Soobin Hwang and Ruoxia Chen and Shinill Kang and Kim, {Ryan Ryoung Han}",

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year = "2021",

doi = "10.1117/12.2584704",

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Oak, A, Hwang, S, Chen, R, Kang, S & Kim, RRH 2021, Machine learning based recursive partitioning for simplifying OPC model building complexity. in C-M Yuan & R-H Kim (eds), Design-Process-Technology Co-optimization XV., 116140O, Proceedings of SPIE - The International Society for Optical Engineering, vol. 11614, SPIE, Design-Process-Technology Co-optimization XV 2021, Virtual, Online, United States, 21/2/22. https://doi.org/10.1117/12.2584704

Machine learning based recursive partitioning for simplifying OPC model building complexity. / Oak, Apoorva; Hwang, Soobin; Chen, Ruoxia; Kang, Shinill; Kim, Ryan Ryoung Han.

Design-Process-Technology Co-optimization XV. ed. / Chi-Min Yuan; Ryoung-Han Kim. SPIE, 2021. 116140O (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11614).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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.

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Machine learning based recursive partitioning for simplifying OPC model building complexity

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