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Deep learning-based yield prediction for the die bonding semiconductor manufacturing process

In the semiconductor manufacturing industry, consistently achieving a high yield is the primary target to meet customer demands and ensure continuous profitability. The ability to predict the yield of a particular manufacturing process at either the Front of Line or End of Line facilities is therefo...

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Main Authors: Akbar, Muhammad Ali, Haja Mohideen, Ahmad Jazlan, Mohd Ibrahim, Azhar, Ahmad, Arfah
Format: Proceeding Paper
Language:English
English
Published: Springer 2025
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://irep.iium.edu.my/114600/1/114600_Deep%20learning-based%20yield%20prediction.pdf
http://irep.iium.edu.my/114600/7/114600_Deep%20learning-based%20yield%20prediction_SCOPUS.pdf
http://irep.iium.edu.my/114600/
https://link.springer.com/chapter/10.1007/978-981-97-3851-9_7
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spelling my.iium.irep.1146002024-10-08T06:49:45Z http://irep.iium.edu.my/114600/ Deep learning-based yield prediction for the die bonding semiconductor manufacturing process Akbar, Muhammad Ali Haja Mohideen, Ahmad Jazlan Mohd Ibrahim, Azhar Ahmad, Arfah TK Electrical engineering. Electronics Nuclear engineering In the semiconductor manufacturing industry, consistently achieving a high yield is the primary target to meet customer demands and ensure continuous profitability. The ability to predict the yield of a particular manufacturing process at either the Front of Line or End of Line facilities is therefore essential in order to analyze Return of Investments (ROI), predictive maintenance and condition monitoring. However, achieving high quality predictions with good accuracy is challenging due to the various uncertainties in the manufacturing process such as unexpected machine downtime and stoppage for maintenance. In this paper we propose a method using Deep Learning Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) to perform day ahead forecasting of the yield from the Die Bond process at a particular semiconductor manufacturing facility. The method was implemented using MATLAB software, and the results demonstrate that the proposed approach achieves accurate yield forecasts with less than 8% error. Further improvements can be made by utilizing hourly data instead of daily data. Springer 2025-09-15 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/114600/1/114600_Deep%20learning-based%20yield%20prediction.pdf application/pdf en http://irep.iium.edu.my/114600/7/114600_Deep%20learning-based%20yield%20prediction_SCOPUS.pdf Akbar, Muhammad Ali and Haja Mohideen, Ahmad Jazlan and Mohd Ibrahim, Azhar and Ahmad, Arfah (2025) Deep learning-based yield prediction for the die bonding semiconductor manufacturing process. In: 7th International Conference on Electrical, Control and Computer Engineering (InECCE 2023), 22nd August 2023, Kuala Lumpur, Malaysia. https://link.springer.com/chapter/10.1007/978-981-97-3851-9_7 10.1007/978-981-97-3851-9_7
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Akbar, Muhammad Ali
Haja Mohideen, Ahmad Jazlan
Mohd Ibrahim, Azhar
Ahmad, Arfah
Deep learning-based yield prediction for the die bonding semiconductor manufacturing process
description In the semiconductor manufacturing industry, consistently achieving a high yield is the primary target to meet customer demands and ensure continuous profitability. The ability to predict the yield of a particular manufacturing process at either the Front of Line or End of Line facilities is therefore essential in order to analyze Return of Investments (ROI), predictive maintenance and condition monitoring. However, achieving high quality predictions with good accuracy is challenging due to the various uncertainties in the manufacturing process such as unexpected machine downtime and stoppage for maintenance. In this paper we propose a method using Deep Learning Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) to perform day ahead forecasting of the yield from the Die Bond process at a particular semiconductor manufacturing facility. The method was implemented using MATLAB software, and the results demonstrate that the proposed approach achieves accurate yield forecasts with less than 8% error. Further improvements can be made by utilizing hourly data instead of daily data.
format Proceeding Paper
author Akbar, Muhammad Ali
Haja Mohideen, Ahmad Jazlan
Mohd Ibrahim, Azhar
Ahmad, Arfah
author_facet Akbar, Muhammad Ali
Haja Mohideen, Ahmad Jazlan
Mohd Ibrahim, Azhar
Ahmad, Arfah
author_sort Akbar, Muhammad Ali
title Deep learning-based yield prediction for the die bonding semiconductor manufacturing process
title_short Deep learning-based yield prediction for the die bonding semiconductor manufacturing process
title_full Deep learning-based yield prediction for the die bonding semiconductor manufacturing process
title_fullStr Deep learning-based yield prediction for the die bonding semiconductor manufacturing process
title_full_unstemmed Deep learning-based yield prediction for the die bonding semiconductor manufacturing process
title_sort deep learning-based yield prediction for the die bonding semiconductor manufacturing process
publisher Springer
publishDate 2025
url http://irep.iium.edu.my/114600/1/114600_Deep%20learning-based%20yield%20prediction.pdf
http://irep.iium.edu.my/114600/7/114600_Deep%20learning-based%20yield%20prediction_SCOPUS.pdf
http://irep.iium.edu.my/114600/
https://link.springer.com/chapter/10.1007/978-981-97-3851-9_7
_version_ 1814042697993814016
score 13.211869

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