Abstract
The semiconductor process is designed to meet the requirements of photolithography, thin film, etching, cleaning of 12″ 10 nm advanced commercial semiconductor manufacturing process, and the process integration system obtains the necessary process parameters and product measurement data. The relevant information is transmitted back to the MES system according to the production capacity and the product number of batch. This study first conducted an intelligent review of the 12″ 10 nm furnace process tools. According to the SEMI specification, the production equipment interface standard for the furnace tube equipment should be discussed. The 12″ 10 nm furnace module was selected for MES big data analysis. Due to the large number of process data, only the LPCVD process temperature distribution during the TEOS process was selected for distributed computation discussion. However, the yield results of this study can be maintained at 92%, while the equipment utilization rate can reach 97%. It is obvious that good results have been achieved.
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Chang, KC., Pan, JS., Chu, KC., Horng, DJ., Jing, H. (2019). Study on Information and Integrated of MES Big Data and Semiconductor Process Furnace Automation. In: Pan, JS., Lin, JW., Sui, B., Tseng, SP. (eds) Genetic and Evolutionary Computing. ICGEC 2018. Advances in Intelligent Systems and Computing, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-13-5841-8_70
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DOI: https://doi.org/10.1007/978-981-13-5841-8_70
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