Ms. Seohyun Kim Profile

Seohyun Kim

at KAIST

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Proceedings Article | 25 April 2024 Presentation + Paper

A fast and accurate PEB simulation through recurrent neural network

Gangmin Cho, Taeyoung Kim, Seohyun Kim, Youngsoo Shin

Proceedings Volume 12954, 129540G (2024) https://doi.org/10.1117/12.3010292

KEYWORDS: Palladium, Systems modeling, Quenching, Photoresist materials, Neural networks, Fused deposition modeling, Machine learning, Lithography, Education and training, Process modeling

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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.

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