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Semiconductor industry is experiencing a rising demand for environmentally friendly processes with the emphasis on green policies and worldwide environmental sustainability. Nitrogen trifluoride (NF3), the most common plasma chamber cleaning agent gas, poses a significant concern as a potent greenhouse gas since it has global warming potential (GWP), 740 times and 6 times higher than that CO2 and N2O. This study investigated the exhaust gas using quadrupole mass spectroscopy (QMS) and analyzed the change in cleaning speed and the type of exhaust gas through plasma monitoring using optical mass spectroscopy (OES). The objective is to lower the use of the amount of NF3 gas in chamber cleaning process to partially contribute the environmental sustainability in the point of semiconductor manufacturing. When a small amount of N2 was added to NF3 whose ratio of 7:23, the cleaning efficiency reached to 90% compared to NF3 gas alone. Addition of N2 positively affected electron density and temperature to increase the F-radical in remote plasma system. In conclusion, 18% of NF3 usage amount was reduced during the Sio2 deposition chamber cleaning process. [ABSTRACT FROM AUTHOR]

Climate change’s impact on human health poses unprecedented and diverse challenges. Unless proactive measures based on solid evidence are implemented, these threats will likely escalate and continue to endanger human well-being. The escalating advancements in information and communication technologies have facilitated the widespread availability and utilization of social media platforms. Individuals utilize platforms such as Twitter and Facebook to express their opinions, thoughts, and critiques on diverse subjects, encompassing the pressing issue of climate change. The proliferation of climate change-related content on social media necessitates comprehensive analysis to glean meaningful insights. This paper employs natural language processing (NLP) techniques to analyze climate change discourse and quantify the sentiment of climate change-related tweets. We collected a total number of 5506 tweets for the period of January 2022 and February 2023 and manually labeled them to make the dataset for this experiment. ClimateBERT, a pre-trained model fine-tuned specifically on the climate change domain was used to generate the context vectors. Several machine learning algorithms with different feature encoding techniques, such as TF-IDF and BERT, have been implemented to classify user sentiments. When comparing the performance of the classifiers using different evaluation metrics such as precision, recall, accuracy, and f-measure, the ClimateBERT + Random Forest model is found to be outperforming all the other baselines with an accuracy of 90.22%, recall of 85.22%, and an f-measure of 85.47%. The findings from this experiment unearth valuable insights into public sentiment and the entities associated with climate change discourse. Policymakers, researchers, and organizations can leverage such analyses to understand public perceptions, identify influential actors, and devise informed strategies to address climate change challenges.

Welcome to this March 2024 Special Issue on Advancing Sustainable Development Goals with AI This issue features research on how AI and data technologies can be used to advance the United Nations (UN) sustainable development goals (SDGs). [ABSTRACT FROM AUTHOR]