A fast and accurate PEB simulation through recurrent neural network

Gangmin Cho; Taeyoung Kim; Seohyun Kim; Youngsoo Shin

doi:10.1117/12.3010292

25 April 2024 A fast and accurate PEB simulation through recurrent neural network

Gangmin Cho, Taeyoung Kim, Seohyun Kim, Youngsoo Shin

Proceedings Volume 12954, DTCO and Computational Patterning III; 129540G (2024) https://doi.org/10.1117/12.3010292
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Abstract

Post-exposure bake (PEB) consists of neutralization, diffusion, and catalysis steps, and are modeled by partial differential equations (PDEs). Commercial PEB simulation relies on numerical methods to explicitly solve PDEs in both spatial and temporal domains, and is very time consuming. A machine learning model has been applied to quickly predict the final inhibitor distribution with initial acid distribution as a model input. The accuracy, however, is not good enough; for different PEB condition comprising baking time and temperature, the model should be trained again, which is another limitation. A recurrent neural network (RNN) is proposed for fast PEB simulation. The network is constructed around convolutional long short-term memory (convLSTM), which is a popular RNN for spatio-temporal prediction. Key inputs of convLSTM include the encoded values of acid and quencher distributions as well as their multiplication; acid and quencher distributions on next time step are obtained after the outputs of convLSTM pass through decoders. Once acid distribution is derived at time instance of interest, inhibitor distribution is extracted directly from its PDE. To accelerate RNN prediction, operations are skipped and the distribution at the next time step is simply copied from the one at the current time step if PEB reaction does not occur. Experiments have shown that the runtime of PEB simulation is reduced by 88.1% with smaller total PDE loss by 35.3%, compared to commercial tool.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Gangmin Cho, Taeyoung Kim, Seohyun Kim, and Youngsoo Shin "A fast and accurate PEB simulation through recurrent neural network", Proc. SPIE 12954, DTCO and Computational Patterning III, 129540G (25 April 2024); https://doi.org/10.1117/12.3010292

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available