Name
Title | ||
|---|---|---|
Takagi-Sugeno neural fuzzy modeling approach to fluid dispensing for electronic packaging |
Abstract | ||
|---|---|---|
In the semiconductor manufacturing industry fluid dispensing is a popular process which is commonly used in die-bonding as well as in microchip encapsulation for electronic packaging. Modeling the fluid 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 fluid dispensing mainly focus on the development of analytical models. However, an analytical model for fluid dispensing, which can provide accurate results, is very difficult to develop because of the complex behavior of fluid dispensing and high degree of uncertainty associated with the process in a real world environment. In this project, Takagi-Sugeno neural fuzzy systems, is introduced to model the fluid dispensing process for microchip encapsulation. Two process models were generated for the two quality characteristics; encapsulation weight and encapsulation thickness, respectively. Validation tests were performed. The test results were compared with approaches based on statistical regression, neural network and fuzzy regression. From a comparison of the results, it can be concluded that among these the TS neural fuzzy system is the best approach for modeling fluid dispensing. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.eswa.2007.02.035 | Expert Syst. Appl. |
Keywords | Field | DocType |
takagi–sugeno neural fuzzy systems,process model,analytical model,takagi-sugeno neural fuzzy modeling,process behavior,encapsulation weight,encapsulation thickness,electronic packaging,takagi-sugeno neural fuzzy system,process modeling,popular process,ts neural fuzzy system,semiconductor manufacturing industry fluid,fluid dispensing,microchip encapsulation,neural network,semiconductor manufacturing,operant conditioning,fuzzy system | Data mining,Neuro-fuzzy,Computer science,Regression analysis,Semiconductor device fabrication,Process modeling,Fuzzy logic,Electronic packaging,Fuzzy control system,Artificial neural network | Journal |
Volume | Issue | ISSN |
34 | 3 | Expert Systems With Applications |
Citations | PageRank | References |
8 | 0.69 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| C. K. Kwong | 1 | 533 | 40.00 |
| K. Y. Chan | 2 | 73 | 5.64 |
| Heung Wong | 3 | 80 | 22.74 |