Prediction of Dynamic Temperature Rise of Thermocouple Sensors Based on Genetic Algorithm-Back Propagation Neural Network

Wang, Hanyu; Dong, Helei; Zhang, Lei; Niu, Yanyan; Liu, Tao; Li, Xiangpeng; Xiong, Jijun; Tan, Qiulin

doi:10.1109/jsen.2022.3217826

Cited by 8 publications

(2 citation statements)

References 38 publications

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“…For example, the shape of the abrasive particles is treated as a sphere and no deformation of the sphere is considered. In the actual CMP, the abrasive particles are irregular and deformed during grinding [18], [19], [20]. The MRR is affected by many parameters, such as pressure, slurry concentration, polishing pad speed.…”

Section: Figure 1 Cmp Working Principlementioning

confidence: 99%

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Predicting the Material Removal Rate in Chemical Mechanical Planarization Based on Improved Neural Network

Wang,

Shi,

et al. 2024

IEEE Access

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Chemical Mechanical Planarization (CMP) technology in integrated circuit manufacturing plays a critical role in realizing local and global flatness of silicon wafer surface. There are still some problems to be solved in industrial production, such as long period of CMP process debugging and various polishing materials, which lead to a more complicated Material Removal Rate (MRR) calculation during the CMP process. Based on the 2016 PHM Challenge dataset, this paper proposes a prediction method based on improved genetic algorithm to optimize BP neural network, which can be used to predict MRR more accurately. It is more difficult to predict MRR because of the many evaluation metrics involved in the CMP process. In this paper, grey relation analysis and principal component analysis methods are introduced to extract and reduce the dimension of 19 evaluation indicators. After reconstruction, six principal components are obtained for input to the BP neural network. The improved adaptive genetic algorithm is used to optimize the weight threshold of BP neural network, and the prediction results are compared with the same type of algorithms. The experimental results show that the BP neural network improved by feature selection and adaptive genetic algorithm has the smallest error with the real results and the highest prediction accuracy for MRR.INDEX TERMS BP neural network, chemical mechanical planarization, feature selection, improved genetic algorithm, material removal rate.

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Section: Figure 1 Cmp Working Principlementioning

confidence: 99%

“…The MRR is affected by many parameters, such as pressure, slurry concentration, polishing pad speed. Some variables need to be idealized when predicting MRR using physical and model methods [21]. These methods has great limitations, the reason is variables are calculated as fixed constants.…”

Section: Figure 1 Cmp Working Principlementioning

confidence: 99%