Deep learning-based yield prediction for the die bonding semiconductor manufacturing process - Akbar, Muhammad Ali; Haja Mohideen, Ahmad Jazlan; Mohd Ibrahim, Azhar; Ahmad, Arfah (Springer, 2025)
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.