Hierarchical Transfer Learning for Cycle Time Forecasting for Semiconductor Wafer Lot under Different Work in Process Levels

Junliang Wang, Pengjie Gao, Zhe Li and Wei Bai
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Junliang Wang: Institute of Artificial Intelligence, Donghua University, Shanghai 201620, China
Pengjie Gao: Institute of Artificial Intelligence, Donghua University, Shanghai 201620, China
Zhe Li: Institute of Artificial Intelligence, Donghua University, Shanghai 201620, China
Wei Bai: State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

Mathematics, 2021, vol. 9, issue 17, 1-11

Abstract: The accurate cycle time (CT) prediction of the wafer fabrication remains a tough task, as the system level of work in process (WIP) is fluctuant. Aiming to construct one unified CT forecasting model under dynamic WIP levels, this paper proposes a transfer learning method for finetuning the predicted neural network hierarchically. First, a two-dimensional (2D) convolutional neural network was constructed to predict the CT under a primary WIP level with the input of spatial-temporal characteristics by reorganizing the input parameters. Then, to predict the CT under another WIP level, a hierarchical optimization transfer learning strategy was designed to finetune the prediction model so as to improve the accuracy of the CT forecasting. The experimental results demonstrated that the hierarchically transfer learning approach outperforms the compared methods in the CT forecasting with the fluctuation of WIP levels.

Keywords: wafer fabrication; cycle time; time series prediction; work in process; convolutional neural network; hierarchical optimization; transfer learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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