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

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.

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Abstract

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.

Item Type:	Proceeding Paper (Plenary Papers)
Additional Information:	7813/114600
Uncontrolled Keywords:	Die bonding, Long short-term memory recurrent neural networks, Semiconductor manufacturing process
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User:	Dr Ahmad Jazlan Haja Mohideen
Date Deposited:	24 Sep 2024 16:39
Last Modified:	08 Oct 2024 14:49
URI:	http://irep.iium.edu.my/id/eprint/114600

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