Modeling of epoxy dispensing process using a hybrid fuzzy regression approach
In the semiconductor manufacturing industry, epoxy dispensing is a popular process commonly used in die bonding as well as in microchip encapsulation for electronic packaging. Modeling the epoxy dispensing process is important because it enables us to understand the process behavior, as well as dete...
| Main Authors: | , |
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| Format: | Journal Article |
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Springer London
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/8392 |
| _version_ | 1848745645101285376 |
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| author | Chan, Kit Yan Kwong, C. |
| author_facet | Chan, Kit Yan Kwong, C. |
| author_sort | Chan, Kit Yan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In the semiconductor manufacturing industry, epoxy dispensing is a popular process commonly used in die bonding as well as in microchip encapsulation for electronic packaging. Modeling the epoxy dispensing process is important because it enables us to understand the process behavior, as well as determine the optimum operating conditions of the process for a high yield, low cost, and robust operation. Previous studies of epoxy dispensing have mainly focused on the development of analytical models. However, an analytical model for epoxy dispensing is difficult to develop because of its complex behavior and high degree of uncertainty associated with the process in a real-world environment. Previous studies of modeling the epoxy dispensing process have not addressed the development of explicit models involving high-order and interaction terms, as well as fuzziness between process parameters. In this paper, a hybrid fuzzy regression (HFR) method integrating fuzzy regression with genetic programming is proposed to make up the deficiency. Two process models are generated for the two quality characteristics of the process, encapsulation weight and encapsulation thickness based on the HFR, respectively. Validation tests are performed. The performance of the models developed based on the HFR outperforms the performance of those based on statistical regression and fuzzy regression. |
| first_indexed | 2025-11-14T06:20:39Z |
| format | Journal Article |
| id | curtin-20.500.11937-8392 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:20:39Z |
| publishDate | 2012 |
| publisher | Springer London |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-83922017-09-13T15:54:28Z Modeling of epoxy dispensing process using a hybrid fuzzy regression approach Chan, Kit Yan Kwong, C. Electronic packaging Epoxy dispensing Genetic programming Fuzzy regression Semiconductor manufacturing Evolutionary computation Process modeling Microchip - encapsulation In the semiconductor manufacturing industry, epoxy dispensing is a popular process commonly used in die bonding as well as in microchip encapsulation for electronic packaging. Modeling the epoxy dispensing process is important because it enables us to understand the process behavior, as well as determine the optimum operating conditions of the process for a high yield, low cost, and robust operation. Previous studies of epoxy dispensing have mainly focused on the development of analytical models. However, an analytical model for epoxy dispensing is difficult to develop because of its complex behavior and high degree of uncertainty associated with the process in a real-world environment. Previous studies of modeling the epoxy dispensing process have not addressed the development of explicit models involving high-order and interaction terms, as well as fuzziness between process parameters. In this paper, a hybrid fuzzy regression (HFR) method integrating fuzzy regression with genetic programming is proposed to make up the deficiency. Two process models are generated for the two quality characteristics of the process, encapsulation weight and encapsulation thickness based on the HFR, respectively. Validation tests are performed. The performance of the models developed based on the HFR outperforms the performance of those based on statistical regression and fuzzy regression. 2012 Journal Article http://hdl.handle.net/20.500.11937/8392 10.1007/s00170-012-4202-4 Springer London fulltext |
| spellingShingle | Electronic packaging Epoxy dispensing Genetic programming Fuzzy regression Semiconductor manufacturing Evolutionary computation Process modeling Microchip - encapsulation Chan, Kit Yan Kwong, C. Modeling of epoxy dispensing process using a hybrid fuzzy regression approach |
| title | Modeling of epoxy dispensing process using a hybrid fuzzy regression approach |
| title_full | Modeling of epoxy dispensing process using a hybrid fuzzy regression approach |
| title_fullStr | Modeling of epoxy dispensing process using a hybrid fuzzy regression approach |
| title_full_unstemmed | Modeling of epoxy dispensing process using a hybrid fuzzy regression approach |
| title_short | Modeling of epoxy dispensing process using a hybrid fuzzy regression approach |
| title_sort | modeling of epoxy dispensing process using a hybrid fuzzy regression approach |
| topic | Electronic packaging Epoxy dispensing Genetic programming Fuzzy regression Semiconductor manufacturing Evolutionary computation Process modeling Microchip - encapsulation |
| url | http://hdl.handle.net/20.500.11937/8392 |