In semiconductor manufacturing, maintaining a high yield and ensuring accurate yield prediction are considerably important for improving productivity, customer satisfaction, and enhancing profitability. Despite its importance and merits, achieving wafer yield prediction with high quality and accuracy is challenging. In this paper, we propose a method for wafer edge yield prediction using a combined long short-term memory (LSTM) and feed-forward neural network (FFNN) model. Unlike previous research, we focus on the edge yield because of the higher yield loss at the wafer edge. The combined LSTM-FFNN model uses a dataset divided into two types according to data characteristics. Time-series data are used in the case of LSTM, and non-time-series data are fed into the FFNN. When preparing the time-series data, comprising data related to the equipment and chambers, data of different chambers do not overlap, thereby rendering them as independent entities. The proposed model outperforms other models in terms of all evaluation metrics. The coefficient of determination of the proposed combined LSTM-FFNN model is 34.14%, which is almost 13% higher than that of the other compared models on average.
|Number of pages||8|
|Publication status||Published - 2020|
Bibliographical noteFunding Information:
This work was supported in part by the Korea Ministry of Land, Infrastructure and Transport (MOLIT) as [Innovative Talent Education Program for Smart City] and in part by Samsung Electronics Co., Ltd.
© 2020 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Materials Science(all)