Industrial Monitoring of Residue Deposition in Semiconductor Process Exhaust Pipelines Using Electrical Capacitance Measurements
In semiconductor manufacturing, the accumulation of byproducts in exhaust pipelines under inadequate temperature control poses significant safety and operational risks. This study introduces an innovative approach employing an electrical capacitance measurement sensor system combined with an artific...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10817552/ |
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| author | Minho Jeon Anil Kumar Khambampati Jong Hyun Song Keun Joong Yoon Kyung Youn Kim |
| author_facet | Minho Jeon Anil Kumar Khambampati Jong Hyun Song Keun Joong Yoon Kyung Youn Kim |
| author_sort | Minho Jeon |
| collection | DOAJ |
| description | In semiconductor manufacturing, the accumulation of byproducts in exhaust pipelines under inadequate temperature control poses significant safety and operational risks. This study introduces an innovative approach employing an electrical capacitance measurement sensor system combined with an artificial neural network (ANN) to monitor residue buildup. The proposed method estimates the free volume index within industrial process exhaust pipes, enabling precise evaluation of residue deposition and gas phase fractions. Numerical simulations and field studies in semiconductor environments validate the model’s effectiveness, demonstrating accurate residue quantification and enhancing safety and operational efficiency. In numerical simulations, the error between true values and estimated values was within 1%, while the values estimated from experimental data showed an error within 5%. These findings underscore the robustness of the model in both controlled and real-world settings. This advancement offers a practical and reliable solution to mitigate hazards and optimize maintenance processes in semiconductor manufacturing. |
| format | Article |
| id | doaj-art-4c1450058c264ba1ad2e09862c5b0afb |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4c1450058c264ba1ad2e09862c5b0afb2025-01-07T00:01:38ZengIEEEIEEE Access2169-35362025-01-01131445145710.1109/ACCESS.2024.352359610817552Industrial Monitoring of Residue Deposition in Semiconductor Process Exhaust Pipelines Using Electrical Capacitance MeasurementsMinho Jeon0Anil Kumar Khambampati1https://orcid.org/0000-0002-0964-8140Jong Hyun Song2Keun Joong Yoon3https://orcid.org/0009-0002-2444-7141Kyung Youn Kim4https://orcid.org/0000-0002-1814-9546Department of Electronics engineering, Jeju National University, Jeju, South KoreaDepartment of Electronics engineering, Jeju National University, Jeju, South KoreaNano Process Technology Department, KAIST Affiliated National Nano Fab Center, Daejeon, South KoreaProsen Company Ltd., Incheon, South KoreaDepartment of Electronics engineering, Jeju National University, Jeju, South KoreaIn semiconductor manufacturing, the accumulation of byproducts in exhaust pipelines under inadequate temperature control poses significant safety and operational risks. This study introduces an innovative approach employing an electrical capacitance measurement sensor system combined with an artificial neural network (ANN) to monitor residue buildup. The proposed method estimates the free volume index within industrial process exhaust pipes, enabling precise evaluation of residue deposition and gas phase fractions. Numerical simulations and field studies in semiconductor environments validate the model’s effectiveness, demonstrating accurate residue quantification and enhancing safety and operational efficiency. In numerical simulations, the error between true values and estimated values was within 1%, while the values estimated from experimental data showed an error within 5%. These findings underscore the robustness of the model in both controlled and real-world settings. This advancement offers a practical and reliable solution to mitigate hazards and optimize maintenance processes in semiconductor manufacturing.https://ieeexplore.ieee.org/document/10817552/Electrical capacitance measurementsemiconductor processpipe deposition monitoringfree volume indexartificial neural network (ANN) |
| spellingShingle | Minho Jeon Anil Kumar Khambampati Jong Hyun Song Keun Joong Yoon Kyung Youn Kim Industrial Monitoring of Residue Deposition in Semiconductor Process Exhaust Pipelines Using Electrical Capacitance Measurements IEEE Access Electrical capacitance measurement semiconductor process pipe deposition monitoring free volume index artificial neural network (ANN) |
| title | Industrial Monitoring of Residue Deposition in Semiconductor Process Exhaust Pipelines Using Electrical Capacitance Measurements |
| title_full | Industrial Monitoring of Residue Deposition in Semiconductor Process Exhaust Pipelines Using Electrical Capacitance Measurements |
| title_fullStr | Industrial Monitoring of Residue Deposition in Semiconductor Process Exhaust Pipelines Using Electrical Capacitance Measurements |
| title_full_unstemmed | Industrial Monitoring of Residue Deposition in Semiconductor Process Exhaust Pipelines Using Electrical Capacitance Measurements |
| title_short | Industrial Monitoring of Residue Deposition in Semiconductor Process Exhaust Pipelines Using Electrical Capacitance Measurements |
| title_sort | industrial monitoring of residue deposition in semiconductor process exhaust pipelines using electrical capacitance measurements |
| topic | Electrical capacitance measurement semiconductor process pipe deposition monitoring free volume index artificial neural network (ANN) |
| url | https://ieeexplore.ieee.org/document/10817552/ |
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