Research on Wafer CMP Temperature Online Detection Compensation Algorithm Based on GA-BP Improved Neural Network
In chemical mechanical polishing (CMP), temperature is a primary factor that determines the rate of chemical reactions, which directly influences the polishing efficiency and quality of wafers. Therefore, constructing an online monitoring system for wafer temperature to enable real-time feedback con...
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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/10982261/ |
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| author | Binjie Li Kuan Shen Zhilong Song Binghai Lyu Wenhong Zhao |
| author_facet | Binjie Li Kuan Shen Zhilong Song Binghai Lyu Wenhong Zhao |
| author_sort | Binjie Li |
| collection | DOAJ |
| description | In chemical mechanical polishing (CMP), temperature is a primary factor that determines the rate of chemical reactions, which directly influences the polishing efficiency and quality of wafers. Therefore, constructing an online monitoring system for wafer temperature to enable real-time feedback control of key node temperatures is beneficial for improving the polishing yield of semiconductor wafers. This paper addresses the challenge that non-contact infrared temperature sensors encounter in the CMP process due to their susceptibility to environmental temperature interference. We propose a backpropagation (BP) neural network temperature compensation model optimized by a dynamic adaptive genetic algorithm. The improved genetic algorithm-optimized backpropagation (GA-BP) neural network model incorporates a dynamic nonlinear probability adjustment mechanism and a fitness calibration mechanism. Compared to traditional BP and standard GA-BP models, the compensation accuracy has improved significantly. Experimental results indicate that the mean absolute error (MAE) of the test set has decreased to 0.2381°C, representing reductions of 82% and 68% compared to BP and GA-BP, respectively. Furthermore, the maximum relative measurement error of the temperature sensor within the environmental temperature range of 3.8 to 75.4°C has decreased from 14% to 2.59%. This model effectively mitigates the shortcomings of the traditional BP network, such as slow convergence rate and vulnerability to local optima, thereby providing a novel approach for sensor temperature compensation in ultra-precision wafer polishing applications. |
| format | Article |
| id | doaj-art-e90ca30c96b14eef8e959af484aa1d82 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e90ca30c96b14eef8e959af484aa1d822025-08-20T03:53:51ZengIEEEIEEE Access2169-35362025-01-0113864988650810.1109/ACCESS.2025.356665010982261Research on Wafer CMP Temperature Online Detection Compensation Algorithm Based on GA-BP Improved Neural NetworkBinjie Li0https://orcid.org/0009-0007-0754-3736Kuan Shen1https://orcid.org/0009-0008-2231-1164Zhilong Song2https://orcid.org/0009-0009-4100-5346Binghai Lyu3https://orcid.org/0000-0002-1628-1084Wenhong Zhao4https://orcid.org/0009-0002-8881-166XCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, ChinaIn chemical mechanical polishing (CMP), temperature is a primary factor that determines the rate of chemical reactions, which directly influences the polishing efficiency and quality of wafers. Therefore, constructing an online monitoring system for wafer temperature to enable real-time feedback control of key node temperatures is beneficial for improving the polishing yield of semiconductor wafers. This paper addresses the challenge that non-contact infrared temperature sensors encounter in the CMP process due to their susceptibility to environmental temperature interference. We propose a backpropagation (BP) neural network temperature compensation model optimized by a dynamic adaptive genetic algorithm. The improved genetic algorithm-optimized backpropagation (GA-BP) neural network model incorporates a dynamic nonlinear probability adjustment mechanism and a fitness calibration mechanism. Compared to traditional BP and standard GA-BP models, the compensation accuracy has improved significantly. Experimental results indicate that the mean absolute error (MAE) of the test set has decreased to 0.2381°C, representing reductions of 82% and 68% compared to BP and GA-BP, respectively. Furthermore, the maximum relative measurement error of the temperature sensor within the environmental temperature range of 3.8 to 75.4°C has decreased from 14% to 2.59%. This model effectively mitigates the shortcomings of the traditional BP network, such as slow convergence rate and vulnerability to local optima, thereby providing a novel approach for sensor temperature compensation in ultra-precision wafer polishing applications.https://ieeexplore.ieee.org/document/10982261/Chemical mechanical polishingdynamic adaptive genetic algorithmGA-BP modelinfrared sensorstemperature compensation |
| spellingShingle | Binjie Li Kuan Shen Zhilong Song Binghai Lyu Wenhong Zhao Research on Wafer CMP Temperature Online Detection Compensation Algorithm Based on GA-BP Improved Neural Network IEEE Access Chemical mechanical polishing dynamic adaptive genetic algorithm GA-BP model infrared sensors temperature compensation |
| title | Research on Wafer CMP Temperature Online Detection Compensation Algorithm Based on GA-BP Improved Neural Network |
| title_full | Research on Wafer CMP Temperature Online Detection Compensation Algorithm Based on GA-BP Improved Neural Network |
| title_fullStr | Research on Wafer CMP Temperature Online Detection Compensation Algorithm Based on GA-BP Improved Neural Network |
| title_full_unstemmed | Research on Wafer CMP Temperature Online Detection Compensation Algorithm Based on GA-BP Improved Neural Network |
| title_short | Research on Wafer CMP Temperature Online Detection Compensation Algorithm Based on GA-BP Improved Neural Network |
| title_sort | research on wafer cmp temperature online detection compensation algorithm based on ga bp improved neural network |
| topic | Chemical mechanical polishing dynamic adaptive genetic algorithm GA-BP model infrared sensors temperature compensation |
| url | https://ieeexplore.ieee.org/document/10982261/ |
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