Abstract
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.
Highlights
I N recent years, as advanced technologies such as smartphones, deep learning, the Internet of Things, and artificial intelligence have emerged, the demand for semiconductors has increased exponentially
In this paper, we proposed a method for edge yield prediction using a combined long short-term memory (LSTM)–feed-forward neural network (FFNN) model
The metrology, virtual metrology, and equipment output data of step A were connected via time-series to the LSTM model, and the other equipment output data and one-hot encoded equipment name information were used as inputs to the FFNN
Summary
I N recent years, as advanced technologies such as smartphones, deep learning, the Internet of Things, and artificial intelligence have emerged, the demand for semiconductors has increased exponentially. The most important parameter among these, is the key to achieving high product quality in semiconductor manufacturing. D. Kim et al.: Wafer Edge Yield Prediction Using a Combined LSTM–FFNN Model for Semiconductor Manufacturing chemicals should be periodically replaced to maintain high quality. Despite its importance and merits, wafer yield prediction has a significantly challenging goal of being systematic with high quality and accuracy Under these conditions, many engineers in semiconductor manufacturing have attempted to predict the yield constantly in practice. Because process variation at the edge contributes to edge yield loss, the edge yield should receive more attention This motivated us to propose a wafer edge yield prediction model using edge parameters. Because the data in semiconductor manufacturing exhibit the time-series property by R2R control or engineers’ tuning to achieve the target value, this characteristic should be included in the wafer yield prediction model.
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