An Improved Optimization Model to Predict the Deposition Rate and Smoothness of Ni Pulse-Reverse Electroplating Based on ANN and Experimental Results
The metallic layers are an essential part of MEMS (micro electromechanical system) devices, and their deposition process must be accurately controlled; this may lead to difficulties as there are many input parameters for such a process. This research focuses on the input parameters’ effects on the N...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/13/1/37 |
| _version_ | 1797438754897526784 |
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| author | Reza Ahmadian Koochaksaraie Farshad Barazandeh Mohammad Akbari |
| author_facet | Reza Ahmadian Koochaksaraie Farshad Barazandeh Mohammad Akbari |
| author_sort | Reza Ahmadian Koochaksaraie |
| collection | DOAJ |
| description | The metallic layers are an essential part of MEMS (micro electromechanical system) devices, and their deposition process must be accurately controlled; this may lead to difficulties as there are many input parameters for such a process. This research focuses on the input parameters’ effects on the Ni pulse-reverse electroplating. A neural network was constructed to characterize the pulse-reverse nickel electroforming process parameters. The sample training has accurately established the mapping relationship between input and output parameters. The nickel layer thickness and surface roughness prediction in the pulse-reverse electroplating process was realized and verified by experimental tests with a test error of 3.3%. Then, the effect of direct and reverse current density, deposition time, structure width, and stirring speed as input parameters on the thickness and surface roughness are investigated. Finally, a novel 4D diagram has been developed to derive the optimal values of direct and reverse current density relative to thickness, surface roughness, and deposition time. This diagram can help researchers and industries find suitable parameters to achieve the desired deposited Ni layer’s properties. |
| first_indexed | 2024-03-09T11:42:50Z |
| format | Article |
| id | doaj.art-c3df7313b6ee4f1b8237f6ae98e1e476 |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-09T11:42:50Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-c3df7313b6ee4f1b8237f6ae98e1e4762023-11-30T23:29:51ZengMDPI AGMetals2075-47012022-12-011313710.3390/met13010037An Improved Optimization Model to Predict the Deposition Rate and Smoothness of Ni Pulse-Reverse Electroplating Based on ANN and Experimental ResultsReza Ahmadian Koochaksaraie0Farshad Barazandeh1Mohammad Akbari2Micro-Technology-Lab, Mechanical Engineering Department, Amirkabir University of Technology, Tehran 15914, IranMechanical Engineering Department, Amirkabir University of Technology, Tehran 15914, IranMicro-Technology-Lab, Mechanical Engineering Department, Amirkabir University of Technology, Tehran 15914, IranThe metallic layers are an essential part of MEMS (micro electromechanical system) devices, and their deposition process must be accurately controlled; this may lead to difficulties as there are many input parameters for such a process. This research focuses on the input parameters’ effects on the Ni pulse-reverse electroplating. A neural network was constructed to characterize the pulse-reverse nickel electroforming process parameters. The sample training has accurately established the mapping relationship between input and output parameters. The nickel layer thickness and surface roughness prediction in the pulse-reverse electroplating process was realized and verified by experimental tests with a test error of 3.3%. Then, the effect of direct and reverse current density, deposition time, structure width, and stirring speed as input parameters on the thickness and surface roughness are investigated. Finally, a novel 4D diagram has been developed to derive the optimal values of direct and reverse current density relative to thickness, surface roughness, and deposition time. This diagram can help researchers and industries find suitable parameters to achieve the desired deposited Ni layer’s properties.https://www.mdpi.com/2075-4701/13/1/37metal MEMSpulse-reverse electroplatingneural networksurface roughnessoptimization |
| spellingShingle | Reza Ahmadian Koochaksaraie Farshad Barazandeh Mohammad Akbari An Improved Optimization Model to Predict the Deposition Rate and Smoothness of Ni Pulse-Reverse Electroplating Based on ANN and Experimental Results Metals metal MEMS pulse-reverse electroplating neural network surface roughness optimization |
| title | An Improved Optimization Model to Predict the Deposition Rate and Smoothness of Ni Pulse-Reverse Electroplating Based on ANN and Experimental Results |
| title_full | An Improved Optimization Model to Predict the Deposition Rate and Smoothness of Ni Pulse-Reverse Electroplating Based on ANN and Experimental Results |
| title_fullStr | An Improved Optimization Model to Predict the Deposition Rate and Smoothness of Ni Pulse-Reverse Electroplating Based on ANN and Experimental Results |
| title_full_unstemmed | An Improved Optimization Model to Predict the Deposition Rate and Smoothness of Ni Pulse-Reverse Electroplating Based on ANN and Experimental Results |
| title_short | An Improved Optimization Model to Predict the Deposition Rate and Smoothness of Ni Pulse-Reverse Electroplating Based on ANN and Experimental Results |
| title_sort | improved optimization model to predict the deposition rate and smoothness of ni pulse reverse electroplating based on ann and experimental results |
| topic | metal MEMS pulse-reverse electroplating neural network surface roughness optimization |
| url | https://www.mdpi.com/2075-4701/13/1/37 |
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