A run-to-run controller for a chemical mechanical planarization process using least squares generative adversarial networks

Sinyoung Kim, Jaeyeon Jang and Chang Ouk Kim ()
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Sinyoung Kim: Yonsei University
Jaeyeon Jang: Yonsei University
Chang Ouk Kim: Yonsei University

Journal of Intelligent Manufacturing, 2021, vol. 32, issue 8, No 13, 2267-2280

Abstract: Abstract Achieving high processing quality for chemical mechanical planarization (CMP) in semiconductor manufacturing is difficult due to the distinct process variations associated with this method, such as drift and shift. Run-to-run control aims to maintain the targeted process quality by reducing the effect of process variations. The goal of controller learning is to infer an underlying output–input reverse mapping based on input–output samples considering the process variations. Existing controllers learn reverse mapping by minimizing the total mapping error for sample data. However, this approach often fails to generate inputs for unseen target outputs because conditional input distributions on target outputs are not captured in the learning. In this study, we propose a controller based on a least squares generative adversarial network (LSGAN) that can capture the input distributions. GANs are deep-learning architectures composed of two neural nets: a generator and a discriminator. In the proposed model, the generator attempts to produce fake input distributions that are similar to the real input distributions considering the process variation features extracted using convolutional layers, while the discriminator attempts to detect the fake distributions. Competition in this game drives both networks to improve their performance until the generated input distributions are indistinguishable from the real distributions. An experiment using the data obtained from a work-site CMP tool verified that the proposed model outperformed the comparison models in terms of control accuracy and computation time.

Keywords: Chemical mechanical planarization; Run-to-run control; Least squares generative adversarial networks; Convolutional neural network; Bayesian optimization (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10845-020-01639-1

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