Abstract
In recent times, particulate matter (PM2.5) is one of the most critical air quality contaminants, and the rise of its concentration will intensify the hazard of cleanrooms. The forecasting of the concentration of PM2.5 has great importance to improve the safety of the highly pollutant-sensitive electronic circuits in the factories, especially inside semiconductor industries. In this paper, a Single-Dense Layer Bidirectional Long Short-term Memory (BiLSTM) model is developed to forecast the PM2.5 concentrations in the indoor environment by using the time series data. The real-time data samples of PM2.5 concentrations were obtained by using an industrial-grade sensor based on edge computing. The proposed model provided the best results comparing with the other existing models in terms of mean absolute error, mean square error, root mean square error, and mean absolute percentage error. These results show that the low error of forecasting PM2.5 concentration in a cleanroom in a semiconductor factory using the proposed Single-Dense Layer BiLSTM method is considerably high.
Highlights
In the last few years, many attempts have been made to create the system architecture of a smart factory
In order to minimize the concentration of particulates in the air, special precautions are needed to be undertaken during the processing of semiconductors [6]. This is because of the high potential damage caused by particulate matter in the cleanroom of semiconductor factories, for which the forecasting of PM2.5 concentration is critical for predictive maintenance
The proposed Single-Dense Layer Bidirectional Long Short-term Memory (BiLSTM) retains a low mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) level at various sampling rates, which means that the forecasting accuracy can be assured even if the original sequence is resampled or a more extended period is used during data gathering
Summary
In the last few years, many attempts have been made to create the system architecture of a smart factory. One of the most critical among all the pollutants is PM2.5 (fine inhalable particles, with an aerodynamic diameter that is commonly 2.5 micrometers or smaller) [3], potentially creating a big problem for the smart factory field These particles may cause the deformity of semiconductor chips [4], resulting in wafer defects which would reduce profit and increases the maintenance cost [5], especially in the semiconductor industry. In order to minimize the concentration of particulates in the air, special precautions are needed to be undertaken during the processing of semiconductors [6] This is because of the high potential damage caused by particulate matter in the cleanroom of semiconductor factories, for which the forecasting of PM2.5 concentration is critical for predictive maintenance.
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