Wafer Edge Yield Prediction Using a Combined Long Short-Term Memory and Feed-Forward Neural Network Model for Semiconductor Manufacturing

Dasol Kim, Mintae Kim, Wooju Kim

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

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.

Original languageEnglish
Article number9269970
Pages (from-to)215125-215132
Number of pages8
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Materials Science
  • General Engineering

Fingerprint

Dive into the research topics of 'Wafer Edge Yield Prediction Using a Combined Long Short-Term Memory and Feed-Forward Neural Network Model for Semiconductor Manufacturing'. Together they form a unique fingerprint.

Cite this